The Beginning of Infinity Handbook

Basically a whole book clearly and comprehensively explaining and summarizing The Beginning of Infinity and its main ideas chapter by chapter.

Oct 06, 2025

🐣 0 — Introduction

A Word for Your Journey

I aim to concisely explain key points of the book, each chapter should take you around 30 minutes.

The best way to retain and incorporate ideas is to constantly challenge your understanding. Hence, chapters are structured by their main questions, after reading, you can practice them all as flashcards here.

I recorded summaries of all chapters as one podcast, you can listen to it above. You also can listen to my interview with Elias Schlie where we explore the main ideas of both The Beginning of Infinity, and The Fabric of Reality in 3 hours. Lastly, I made a public playlist with Brett Hall’s lectures in book’s order: you can listen to it here.

As a different medium you can read this handbook in Notion with toggled questions and chapters. (I’m working on a pdf version.)

The Beginning of Infinity was a very rewarding and challenging read for me. I hope my work makes it easier to understand David’s ideas without diminishing their value. When in hardship — persevere, the fruits of understanding taste sweet.

My handbook is no substitute for reading the book, nothing is. It is a companion along your journey.

By endurance we conquer, Mark

markkagach.com

🎯 5-Minute Summary

All knowledge creation is a three-step process: meeting a problem, guessing a solution, and then, criticizing it. This is true for art, philosophy and math.

Since knowledge grows through criticism, we can never claim 100% certainty, as we don’t justify or create positive evidence for theories (even in mathematics).

Hence, knowledge creation is a journey from a misconception to an ever smaller misconception. From problems to ever better problems.

Thus, problems are inevitable.

Science wants to understand the world, which inescapably means explaining it. Prediction is a mean for criticizing theories, not an end on its own.

Theories are just guesses. We criticize them experimentally or philosophically. David presents a breakthrough in the second one — explanations must be hard to vary.

From this we infer that something is real if it is in our best explanations of the world.

Good explanations are not limited to physical, reductionist phenomena, we constantly invoke abstract, emergent things. Thus, abstract entities are real.

Physical world provides a narrow window through which we observe the world of abstractions. Together they form the reality we are in.

Knowledge is a physical force, it can transform the landscape of Earth, and much more. Since Enlightenment for the first time in history we had a sustained progress, we created knowledge in every facet of life.

The only true constraints are laws of physics.

Everything they allow is achievable, the question is: How?

Knowledge is the answer.

Thus, problems are soluble.

Are things that create knowledge significant?

Their significance depends on knowledge power. With the right knowledge one can bend the universe to their will. Thus, things that create knowledge are universally significant.

Thus, humans are universally significant.

The other name for a problem-free state is death.

Thus, problems are desirable.

Optimism is a philosophy that all evils are due to the lack of knowledge. That problems are inevitable and soluble, so progress is unbound and infinite. That problems are desirable. That humans, as knowledge creators, are universally significant.

So what would it take for progress to occur? For man to ascent? For infinity to begin?

Only a bold guess that things could be better, and a relentless perseverance to make it so, fueled by blood, sweat and tears; all while staring straight into the deadliest beast of all — parochial social misconceptions, and fighting, fighting back; for the only sustainable way to live is to progress.

My Favorite Quotes

What would have happened if one of those hunters had had a different idea: to ride the beast before killing it? Generations later, the knock-on effects of that bold conjecture might have been tribes of warriors on horses and mammoths pouring back through Alaska and re-conquering the Old World. Their descendants would now be attributing this to the geographical distribution of megafauna. But the real cause would have been that one idea in the mind of that one hunter. — page 427

In this book I argue that all progress, both theoretical and practical, has resulted from a single human activity: the quest for what I call good explanations. — page vii

an unproblematic state is a state without creative thought. Its other name is death. — page 63

The world may or may not be as we wish it to be, and to reject good explanations on that account is to imprison oneself in parochial error — page 118

The laws of physics provide us with only a narrow window through which we can look out on the world of abstractions. — page 185

Every room is at the beginning of infinity. That is one of the attributes of the unbounded growth of knowledge too: we are only just scratching the surface, and shall never be doing anything else. — page 175

Like every other destruction of optimism, whether in a whole civilization or in a single individual, these must have been unspeakable catastrophes for those who had dared to expect progress. But we should feel more than sympathy for those people. We should take it personally. For if any of those earlier experiments in optimism had succeeded, our species would be exploring the stars by now, and you and I would be immortal. — page 221

All fiction that does not violate the laws of physics is fact. — page 300

all evils are due to lack of knowledge — page 319

Surely this is not coincidence: it is a regularity in nature. So it must have an explanation. — page 355

We have, so far, been transformed from the victims (and enforcers) of an eternal status quo into the mainly passive recipients of the benefits of relatively rapid innovation in a bumpy transition period. We now have to accept, and rejoice in bringing about, our next transformation: to active agents of progress in the emerging rational society – and universe. — page 396

So there is no resource-management strategy that can prevent disasters, just as there is no political system that provides only good leaders and good policies, nor a scientific method that provides only true theories. But there are ideas that reliably cause disasters, and one of them is, notoriously, the idea that the future can be scientifically planned. The only rational policy, in all three cases, is to judge institutions, plans and ways of life according to how good they are at correcting mistakes: removing bad policies and leaders, superseding bad explanations, and recovering from disasters. — page 436

if we choose instead to embark on an open-ended journey of creation and exploration whose every step is unsustainable until it is redeemed by the next – if this becomes the prevailing ethic and aspiration of our society – then the ascent of man, the beginning of infinity, will have become, if not secure, then at least sustainable. — page 441

the desirable future is one where we progress from misconception to ever better (less mistaken) misconception. — page 446

Find more quotes under the footnote.1

Found Mistakes?

This handbook will always be a work-in-progress. Yet, from now on I expect most of the future improvements to come from you — readers.

If you: find any mistakes, or have a better phrasing, or have a better example, or can think of any other improvement: comment below and I will update the handbook!

The beauty of internet as a medium is that you can easily correct and improve things. I want this handbook to stand the test of time, and become a timeless resources for all who are learning ideas of David.

Comment your proposals, I’ll review them all!

My Book Review

For someone who spend 400 hours on this book I won’t say something unexpected. Obviously this is a masterpiece, obviously this is the best book I’ve ever read on par with The Fabric of Reality.

It changed me as a person.

It demystified the world.

It revealed interesting questions.

I’ve changed my life, my expectations and my desires to pursue some of them.

David brought so much value to me that I had to give something back. This is my attempt.

⚛️ 1 — The Reach of Explanations

Supernovas, gamma-ray bursts, quasars and black holes are all so huge, so inhumane, so violent and significant that we are only left to tremble in awe and fear. It is only natural to perceive us as inconsequential ‘chemical scum’. But is that so?

No human has ever been further than the moon, and yet, in our small, humane, safe and insignificant buildings we contemplate the fate of the universe. How do we know? How could we — so small and unimportant, have access to them — so enormous and seminal?

This chapter is about how we understand the world. Specifically, how science works. It’s guiding question is: How do we learn about the world?

Summary

Science is our best tool to understand the world and for many centuries we had a mistaken view of how it works. We believed we derive scientific theories from our senses, by purely observing the world. We believed we could induce theories from ‘pure’ observation. We believed we could create a positive evidence for our theories. We believed that science is about predicting reality, not understanding it. These misconceptions are still prevalent, but now, we know better.

Since Enlightenment we had both rapid and stable progress that was initially caused by a philosophical revolution. We rejected intellectual authorities, and created a tradition of criticism. Eventually we learned that all knowledge is fallible, it consists of our guesses, which are then criticized to leave the last man standing. We criticize through experimental test, or philosophical arguments.

David introduces a hard to vary criterion as a way to criticize theories that are explanatory and impossible to falsify experimentally. He also proposes a criterion for whether something is real or not: something is real if it is in our best explanations of the world.

Practice chapter as flashcards here.

Misconceptions: Empiricism, Induction, Justificationism and Instrumentalism

1.1.0 What are scientific theories?

They are our ideas about what the world is and how it works.

Scientific theories are explanations: assertions about what is out there and how it behaves. — page 3

1.1.1 What were our initial ideas of how we acquire scientific theories? What is empiricism?

For centuries we have believed that we derive theories from our senses. Empiricism, was a philosophy of science that was prevalent during the Enlightenment:

the philosopher John Locke wrote in 1689 that the mind is like ‘white paper’ on to which sensory experience writes, and that that is where all our knowledge of the physical world comes from. Another empiricist metaphor was that one could read knowledge from the ‘Book of Nature’ by making observations. Either way, the discoverer of knowledge is its passive recipient, not its creator. — page 4

1.1.2 What is its criticism?

First, senses are not the source of theories, they are used to choose between them by performing experiments. Second: How can we ever derive something unexperienced from what we have experienced?

there is a logical gap: no amount of deduction applied to statements describing a set of experiences can reach a conclusion about anything other than those experiences. — page 5

Induction was an attempt to breach the problem of empiricism.

1.2.0 What is inductivism?

To breach the logical gap, repetition was seen as a savior:

if one repeatedly has similar experiences under similar circumstances, then one is supposed to ‘extrapolate’ or ‘generalize’ that pattern and predict that it will continue. For instance, why do we expect the sun to rise tomorrow morning? Because in the past (so the argument goes) we have seen it do so whenever we have looked at the morning sky. From this we supposedly ‘derive’ the theory that under similar circumstances we shall always have that experience, or that we probably shall. On each occasion when that prediction comes true, and provided that it never fails, the probability that it will always come true is supposed to increase. Thus one supposedly obtains ever more reliable knowledge of the future from the past, and of the general from the particular. That alleged process was called ‘inductive inference’ or ‘induction’, and the doctrine that scientific theories are obtained in that way is called inductivism. To bridge the logical gap, some inductivists imagine that there is a principle of nature – the ‘principle of induction’ – that makes inductive inferences likely to be true. ‘The future will resemble the past’ is one popular version of this, and one could add ‘the distant resembles the near,’ ‘the unseen resembles the seen’ and so on. — page 5

1.2.1 What is its criticism?

David focuses on two points.

First, inductivism focuses on the prediction of human experiences, not external reality. But most of our knowledge doesn’t fit that framework! It’s not about our experiences, and it cannot be predicted! Have anyone experienced a Big Bang, or a light year? Seen an atom or a perfect circle? Visited Mars to argue what laws of physics it has? No. Science tries to model reality through good explanations, predictions are merely deduced from those explanations. Inductivism fails to address how from a pale blue dot in the sky we arrive at the conclusion that these are massive, distant nuclear furnaces.

Second, inductivism claims that ‘the future resembles the past’, or ‘the seen resembles the unseen’ and so on. But the future is unlike the past, and same is true for seen and unseen:

For millennia people dreamed about flying, but they experienced only falling. Then they discovered good explanatory theories about flying, and then they flew – in that order. Before 1945, no human being had ever observed a nuclear-fission (atomic-bomb) explosion; there may never have been one in the history of the universe. Yet the first such explosion, and the conditions under which it would occur, had been accurately predicted – but not from the assumption that the future would be like the past. Even sunrise – that favourite example of inductivists – is not always observed every twenty-four hours: when viewed from orbit it may happen every ninety minutes, or not at all. And that was known from theory long before anyone had ever orbited the Earth. — page 6

The goal of inductivism is to show in which exactly ways the future resembles the past. To differentiate the infinite variables and patterns one can consider during any observation. Inductivism never tells on which variables (patterns) should we focus during observation, and this is a big problem. Good theory of knowledge must have this gap explained.

Sometimes I will reference relevant questions from The Fabric of Reality Handbook, with answers in footnotes:

FoR: 3.2.2 What are the stages of inductivism? Describe the knowledge creation process on the shadows example from the past chapter.2
FoR: 3.2.3. What is the criticism of induction? Describe an example of the Bertrand Russell chicken.3

Even though induction and empiricism are wrong, they had positive impact.

1.3.0 What was the value that they have brought?

Empiricism liberated philosophers from the grip of orthodox authority as a source of knowledge and allowed them to become scientists. Nonetheless, as most revolutions, it tried to the replace the old authority with a new one, namely senses.

Empiricism never did achieve its aim of liberating science from authority. It denied the legitimacy of traditional authorities, and that was salutary. But unfortunately it did this by setting up two other false authorities: sensory experience and whatever fictitious process of ‘derivation’, such as induction, one imagines is used to extract theories from experience. — page 8

1.3.1 What is justificationism?

A belief that scientific method must involve some positive argument, or appeal to authority that proves theory once and for all.

The misconception that knowledge needs authority to be genuine or reliable dates back to antiquity, and it still prevails. To this day, most courses in the philosophy of knowledge teach that knowledge is some form of justified, true belief, where ‘justified’ means designated as true (or at least ‘probable’) by reference to some authoritative source or touchstone of knowledge. Thus ‘how do we know . . . ?’ is transformed into ‘by what authority do we claim . . . ?’ The latter question is a chimera that may well have wasted more philosophers’ time and effort than any other idea. It converts the quest for truth into a quest for certainty (a feeling) or for endorsement (a social status). This misconception is called justificationism. — page 9

1.3.2 What is its criticism and opposing theory?

Fallibilism is the philosophy that rejects entirely appeal to any authoritative claim, or positive argument. It claims that all our knowledge is fallible, and must be hold tentatively. There is no ultimate source of truth, only our guesses (conjectures), which are chosen through criticism (refutations).

the recognition that there are no authoritative sources of knowledge, nor any reliable means of justifying ideas as being true or probable – is called fallibilism. To believers in the justified-true-belief theory of knowledge, this recognition is the occasion for despair or cynicism, because to them it means that knowledge is unattainable. But to those of us for whom creating knowledge means understanding better what is really there, and how it really behaves and why, fallibilism is part of the very means by which this is achieved. Fallibilists expect even their best and most fundamental explanations to contain misconceptions in addition to truth, and so they are predisposed to try to change them for the better. In contrast, the logic of justificationism is to seek (and typically, to believe that one has found) ways of securing ideas against change. Moreover, the logic of fallibilism is that one not only seeks to correct the misconceptions of the past, but hopes in the future to find and change mistaken ideas that no one today questions or finds problematic. — page 9

1.4.0 What Popper meant by saying that all observation is theory-laden?

We never observe things as a blank slate. We always have our prior beliefs and knowledge about anything we observe. We can’t get rid of it. Perceived reality changes depending on our goals, interests, and yes, beliefs and knowledge. You can try yourself by watching this video.

1.2 Quote from Popper to drive the point home. Also you can try this video and this link. And this paper.4

We never know any data before interpreting it through theories. All observations are, as Popper put it, theory-laden, and hence fallible, as all our theories are.* Consider the nerve signals reaching our brains from our sense organs. Far from providing direct or untainted access to reality, even they themselves are never experienced for what they really are – namely crackles of electrical activity. Nor, for the most part, do we experience them as being where they really are – inside our brains. Instead, we place them in the reality beyond. We do not just see blue: we see a blue sky up there, far away. We do not just feel pain: we experience a headache, or a stomach ache. The brain attaches those interpretations – ‘head’, ‘stomach’ and ‘up there’ – to events that are in fact within the brain itself. Our sense organs themselves, and all the interpretations that we consciously and unconsciously attach to their outputs, are notoriously fallible – as witness the celestial-sphere theory, as well as every optical illusion and conjuring trick. So we perceive nothing as what it really is. It is all theoretical interpretation: conjecture. — page 10

When we perceive the dog ‘other there’, all our experience is within our brain. ‘Other there’ is an interpretation we put on our observations (which are all actually electrical spikes in the brain) based on our theories. Your past beliefs and knowledge affect what conclusion you draw from the same evidence, if it changes, the conclusion will change too.

1.4.1 How is this idea connected to fallibility?

Our interpretations and beliefs can be wrong. All our observations (sensory input) are based on our interpretations and beliefs. Hence, all our observations can be wrong (fallible).

All our knowledge is based on observations (sensory input) and internal thinking. Our thinking can be wrong (fallible). Our observations can be wrong (fallible). Hence, our all our knowledge can be wrong (fallible).

Therefore regardless of what we study, be it physics, literature or math, all our knowledge about it can be wrong. We never reach 100% certainty.

You also arrive at the same conclusion by rejecting justificationism. If there is no way for us to create positive evidence for any theory we have, then how could be ever claim it with full 100% certainty? We never could. Our highest certainty can be that this is our best theory (which is just an educated guess) so far. All our knowledge and beliefs must be held tentatively — adjustable, open to change, criticism and revision. Nothing is a sacred cow.

1.5.0 What is instrumentalism?

The view that the basic purpose of science is to predict an experiment, not to explain the reality. Explanations for instrumentalists are no more than psychological props — empty words.

The important thing is to be able to make predictions about images on the astronomers’ photographic plates, frequencies of spectral lines, and so on, and it simply doesn’t matter whether we ascribe these predictions to the physical effects of gravitational fields on the motion of planets and photons [as in pre-Einsteinian physics] or to a curvature of space and time. — Gravitation and Cosmology, page 147

Instrumentalism can be appealing because people have many hidden assumptions, they don’t realize they use explanations because they seem so obvious. These hidden assumptions are rules of thumb, and they also have explanations.

From The Fabric of Reality Handbook, with answers in footnotes:

FoR: 1.4.1 What is the criticism of instrumentalism?5
FoR: 1.4.2 If wrong, why instrumentalism is so popular in academia?6

So far we have considered the misconceptions of how science works: relying too much on our senses, generalizing from observations and justifying theories. We have also seen that all knowledge is fallible, regardless of the subject studied we can be wrong. So what is the right way to do science?

Our Leading Theory: Popperian Epistemology

1.6.0 What is the Popperian epistemology?

A philosophical theory that science is a problem-solving process. The problem arises when our current theories seem inadequate. There is a conflict: something has happened that cannot happen under our current knowledge. Some people might not have a conflict, it depends on their existing knowledge. For instance a kid might find a magic trick not amusing at all, because he didn’t acquire theories that magician puts in conflict, like most adults do. A professional magician might also have no conflict, because his existing knowledge explains how the trick works, so nothing ‘amusing’ (as in unexplainable) happened for him.

To breach the gap from conflict we guess (conjecture) solutions to those problems. We guess how magic trick works, by creating explanations about it.

As those explanations are just guesses we need to choose between them. Hence, they are criticized. They can’t be supported because we can’t create positive evidence for any of our guesses (remember justificationism?). This is why testability is a crucial part of science, and why Popper called it a demarcation (divider) of science from non-science. Guesses that are impossible to test, are hard to criticize, and hence improve:

“Is there any anything that a man can do that couldn’t be explained by psychoanalysis? . . . there are many things that couldn’t be explained by general relativity or Newtonian physics.” [1.1] Popper was able to criticize psychoanalysis through philosophical argument, but science mainly relies on experimental testing, so theories that are experimentally irrefutable are unpreferred.

As we criticize our leading guesses (a.k.a. theories), we would reject some over others, and ideally, be left with one man standing — this is our best scientific theory of the world, so far. For all our knowledge is fallible, and we will never be sure with 100% certainty.

The example of a conjuring trick illustrates how observations provide problems for science – dependent, as always, on prior explanatory theories. For a conjuring trick is a trick only if it makes us think that something happened that cannot happen. Both halves of that proposition depend on our bringing quite a rich set of explanatory theories to the experience. That is why a trick that mystifies an adult may be uninteresting to a young child who has not yet learned to have the expectations on which the trick relies. Even those members of the audience who are incurious about how the trick works can detect that it is a trick only because of the explanatory theories that they brought with them into the auditorium. Solving a problem means creating an explanation that does not have the conflict. — page 17

From The Fabric of Reality Handbook, with answers in footnotes:

FoR: 3.3.1 What are the stages of Popperian epistemology?7
FoR: 3.4.0 What is a crucial experimental test?8

But testability cannot be the only criterion, because testable theories are two a penny. David writes:

Every would-be prophet who claims that the sun will go out next Tuesday has a testable theory. — page 14

1.7.0 Elaborate why testability cannot be the only criterion of science.

Testability cannot be the only criterion because the purpose of science is not only in predicting things. It is in explaining them, and prediction is a useful tool for testing your guesses and their explanations.

Consider an audience watching a conjuring trick. The problem facing them has much the same logic as a scientific problem. Although in nature there is no conjurer trying to deceive us intentionally, we can be mystified in both cases for essentially the same reason: appearances are not self-explanatory. If the explanation of a conjuring trick were evident in its appearance, there would be no trick. If the explanations of physical phenomena were evident in their appearance, empiricism would be true and there would be no need for science as we know it.
The problem is not to predict the trick’s appearance. I may, for instance, predict that if a conjurer seems to place various balls under various cups, those cups will later appear to be empty; and I may predict that if the conjurer appears to saw someone in half, that person will later appear on stage unharmed. Those are testable predictions. I may experience many conjuring shows and see my predictions vindicated every time. But that does not even address, let alone solve, the problem of how the trick works. Solving it requires an explanation: a statement of the reality that accounts for the appearance. — page 14

David Deutsch’s Contribution

appearances are not self-explanatory

Our theories must have explanations because prediction is not enough to understand reality: “appearances are not self-explanatory”. We also prefer testable theories because they are easier to refute — avoid fooling ourselves.

1.8.0 Are testable explanatory theories enough to explain how science works?

Its not!

But even testable, explanatory theories cannot be the crucial ingredient that made the difference between no-progress and progress. For they, too, have always been common. Consider, for example, the ancient Greek myth for explaining the annual onset of winter. Long ago, Hades, god of the underworld, kidnapped and raped Persephone, goddess of spring. Then Persephone’s mother, Demeter, goddess of the earth and agriculture, negotiated a contract for her daughter’s release, which specified that Persephone would marry Hades and eat a magic seed that would compel her to visit him once a year thereafter. Whenever Persephone was away fulfilling this obligation, Demeter became sad and would command the world to become cold and bleak so that nothing could grow. — page 19

Hades-Demeter theory is explanatory and testable. In fact, if Greeks knew that winter is not everywhere on Earth at once they would have refuted it! But we would still fool ourselves: Greek myth-tellers would just alter the story to fit new observations (for they don’t want to lose their job!).

1.8.1 How can we refute the new theories that Greek myth-tellers create to fit-in new observations?

David introduces a hard to vary criterion.

That is a good explanation – hard to vary, because all its details play a functional role. — page 24

Greek myth-tellers theories are bad because they can be arbitrarily changed to fit-in existing data — they are easily variable. David writes:

if the ancient Greeks had discovered that the seasons in the northern and southern hemispheres are out of phase, they would have had a choice of countless slight variants of the myth that would be consistent with that observation. One would be that when Demeter is sad she banishes warmth from her vicinity, and it has to go elsewhere – into the southern hemisphere. Similarly, slight variants of the Persephone explanation could account just as well for seasons that were marked by green rainbows, or seasons that happened once a week, or sporadically, or not at all. … Without a good explanatory theory, they can simply reinterpret the omens, pick a new date, and make essentially the same prediction. In such cases, testing one’s theory and abandoning it when it is refuted constitutes no progress towards understanding the world. If an explanation could easily explain anything in the given field, then it actually explains nothing. — page 21

1.8.2 What are some of the characteristics of this criterion?

A good explanation fits well within our existing knowledge:

The best explanations are the ones that are most constrained by existing knowledge – including other good explanations as well as other knowledge of the phenomena to be explained. — page 26

A hard to vary explanations makes it hard to fool ourselves, for we can’t alter it because of some external pressures — theory would instantly break.

What if you’d rather not know? You may not like these predictions. Your friends and colleagues may ridicule them. You may try to modify the explanation so that it will not make them, without spoiling its agreement with observations and with other ideas for which you have no good alternatives. You will fail. That is what a good explanation will do for you: it makes it harder for you to fool yourself.

1.8.3 Elaborate on the reach of explanations.

An explanation has a reach that is beyond what is currently known to its creator.

Whether you like it or not, it makes predictions about places both known to you and unknown to you, predictions that you have thought of and ones that you have not thought of. … The theory reaches out, as it were, from its finite origins inside one brain that has been affected only by scraps of patchy evidence from a small part of one hemisphere of one planet – to infinity. This reach of explanations is another meaning of ‘the beginning of infinity’. It is the ability of some of them to solve problems beyond those that they were created to solve.

Not all explanations have infinite reach, good explanations explicitly set their boundaries.

the reach of an explanation is neither an additional assumption nor a detachable one. It is determined by the content of the explanation itself. The better an explanation is, the more rigidly its reach is determined – because the harder it is to vary an explanation, the harder it is in particular to construct a variant with a different reach, whether larger or smaller, that is still an explanation. We expect the law of gravity to be the same on Mars as on Earth because only one viable explanation of gravity is known – Einstein’s general theory of relativity – and that is a universal theory; but we do not expect the map of Mars to resemble the map of Earth, because our theories about how Earth looks, despite being excellent explanations, have no reach to the appearance of any other astronomical object. Always, it is explanatory theories that tell us which (usually few) aspects of one situation can be ‘extrapolated’ to others.

1.9.0 How do we know whether something is real or not?

criterion for reality – namely that we should conclude that a particular thing is real if and only if it figures in our best explanation of something — page 23

🔭 2 — Closer to Reality

Most of scientific discoveries are made “in our small, humane, safe and insignificant buildings”. How can we study and know of something so distant like stars from the proximity of our homes? This chapter is about scientific instruments.

Summary

We never observe reality directly, the only thing we experience are electrical currents in our brain. Yet, we don’t observe them either. Our senses with our knowledge model reality we observe.

Popper’s thesis that all observation is theory-laden holds for scientific instruments as well. Operating them correctly relies on explanatory theories of how they work and so on. As with any knowledge, such theories are fallible.

It may seem strange that scientific instruments bring us closer to reality when in purely physical terms they only ever separate us further from it. But we observe nothing directly anyway. All observation is theory-laden. Likewise, whenever we make an error, it is an error in the explanation of something. That is why appearances can be deceptive, and it is also why we, and our instruments, can correct for that deceptiveness. The growth of knowledge consists of correcting misconceptions in our theories. — page 41

Practice chapter as flashcards here.

2.1.0 When David was an undergraduate he was looking at a cluster of galaxies through a microscope. (See image on the right.) He has mistaken one of those dots with the galaxy, while in fact it was a defect in the photo. Is it a big mistake? It is, at the end of the day, just blobs on a glass, and they were so alike that he couldn’t distinguish them. What is the essence of this mistake?

But wait. Was I ever looking at a galaxy? All the other blobs were in fact microscopic smudges of silver too. If I misclassified the cause of one of them, because it looked too like the others, why was that such a big error?
Because an error in experimental science is a mistake about the cause of something. Like an accurate observation, it is a matter of theory. Very little in nature is detectable by unaided human senses. Most of what happens is too fast or too slow, too big or too small, or too remote, or hidden behind opaque barriers, or operates on principles too different from anything that influence our evolution. But in some cases we can arrange for such phenomena to become perceptible, via scientific instruments. — page 37

2.2.0 Why we use scientific instruments? Don’t they separate us further from perceived phenomena? Why look in the microscope and not the sky when studying stars?

We experience such instruments as bringing us closer to the reality – just as I felt while looking at that galactic cluster. But in purely physical terms they only ever separate us further from it. I could have looked up at the night sky in the direction of that cluster, and there would have been nothing between it and my eye but a few grams of air – but I would have seen nothing at all. I could have interposed a telescope, and then I might have seen it. In the event, I was interposing a telescope, a camera, a photographic development laboratory, another camera (to make copies of the plates), a truck to bring the plates to my university, and a microscope. I could see the cluster far better with all that equipment in the way. — page 37

2.2.1 When looking at the stars with our unaided eye, do we ever perceive them directly?

No! We only experience electric currents in our brains. Yet we never observe those either!

2.2.2 What we observe then?

Our past knowledge and senses create a representation of reality for us. We never observe something purely. This is what Popper meant when he said that all observation is theory-laden: it is never decoupled from our beliefs (theories of the world). (For more, revisit card 1.3.0.)

Using scientific instruments is similar. Our scientific instruments get us closer to reality only when coupled with good theories behind it (like how the telescope that made pictures works). Hence, no observation is separate from its underlying theories, and no observation can be 100% trusted for all theories are fallible.

[scientific] instruments are rare and fragile configuration of matter. Press one wrong button on the telescope’s control panel, or code one wrong instruction into its computer, and the whole immensely complex artefact may well revert to revealing nothing other than itself. The same would be true if, instead of making that scientific instrument, you were to assemble those raw materials into almost any other configuration stare at them, and you would see nothing other than them.
Explanatory theories tell us how to build and operate instruments in exactly the right way to work this miracle. Like conjuring tricks in reverse, such instruments fool our senses into seeing what is really there. Our minds, through the methodological criterion that I mentioned in Chapter 1, conclude that a particular thing is real if and only if it figure in our best explanation of something. Physically, all that has happened is that human beings, on Earth, have dug up raw materials such as iron ore and sand, and have rearranged them – still on Earth – into complex objects such as radio telescopes, computers and display screens, and now, instead of looking at the sky, they look at those objects. They are focusing their eyes on human artefacts that are close enough to touch. But their minds are focused on alien entities and processes, light years away. — page 40

✨ 3 — The Spark

For most of our history theories of the world were centered around humans, they were anthropocentric. Winter would be attributed to humans with supernatural abilities and so on. Enlightenment revealed ‘insignificance’ of humans in the universal scheme of things, Principle of Mediocrity and Spaceship Earth ideas are its manifestations. But is our situation that ‘mediocre’? And is our environment without technologies that ‘hospitable’?

This chapter is about what makes humans unique and significant in the cosmic scheme of things. It consists of three parts:

Misconceptions: Principle of Mediocrity and Spaceship Earth Idea
Human Reach and Problems
Significance of People

Summary

Principle of Mediocrity claims that Earth is a typical place in the universe, revealing how our past views of the world were parochial. Yet, to claim that Earth is a ‘typical place’ is to make just as big anthropocentric mistake. Nothing about Earth is ‘typical’ on the universe scale. Spaceship Earth seems to form a similar worldview while claiming the opposite: Earth is a spaceship, and if we are too frivolous with its resources we are destined to extinct. Even though Earth is not a ‘typical’ place in its view, it also claims that humans are parochial, arrogant and anthropocentric and must be more grateful for the gifts they have received from the environment. Somehow, Spaceship Earth accomplishes even higher level of parochialism than Principle of Mediocrity. Humans have not received any gifts, they were the one that created them. Humans made Earth hospitable and ‘friendly’, it never had such intentions.

Humans create explanatory knowledge that helps us to understand and transform nature. Nature is universal, so our reach is too. Yet, there are factors that limit knowledge creation: matter, energy and evidence. As it turns out such features are present in an actual typical place in the universe, one that is dark, cold and lifeless. Hence, human reach is tremendous, covering most of the universe.

Given the right knowledge any transformation that is allowed by the laws of physics is possible. Thus, problems are soluble. Our knowledge would always be fallible for it relies on refutations, not justifications. Thus, problems are inevitable. Problems are soluble and inevitable, so our knowledge would always be at the beginning of potential infinity. We would never do anything but scratch the surface.

Knowledge is one of the most powerful forces in the universe, with a ‘sliver’ of it one can decide fate of the star or galaxy. Things that create knowledge are powerful, and must be significant.

To understand the universe one would have to understand humans, our morals and decisions. As eventually we would decide the structure of massive cosmic objects through our knowledge. Hence, humans are cosmically significant.

Practice chapter as flashcards here.

Misconceptions: Principle of Mediocrity and Spaceship Earth Idea

3.1.0 What is the Principle of Mediocrity?

‘Principle of Mediocrity’: there is nothing significant about humans (in the cosmic scheme of things). As the physicist Stephen Hawking put it, humans are ‘just a chemical scum on the surface of a typical planet that’s in orbit around a typical star on the outskirts of a typical galaxy’. The proviso ‘in the cosmic scheme of things’ is necessary because the chemical scum evidently does have a special significant according to values that it applies to itself, such as moral values. But the Principle says that all such values are themselves anthropocentric: they explain only the behaviour of the scum, which is itself insignificant. — page 43

3.1.1 What is the Principle of Mediocrity criticism?

It is true that we are on a (somewhat) typical planet of a typical star in a typical galaxy. But we are far from typical of the matter in the universe. For one thing, about 80 per cent of that matter is thought to be invisible ‘dark matter’, which can neither emit nor absorb light. We currently detect it only through its indirect gravitational effects on galaxies. Only the remaining 20 per cent is matter of the type that we parochially call ‘ordinary matter’. It is characterized by glowing continuously.
…
Concentrations of matter as dense as ourselves and our planet and star, though numerous, are not exactly typical either. They are isolated, uncommon phenomena. The universe is mostly vacuum (plus radiation and dark matter). Ordinary matter is familiar to us only because we are made of it, and because of our untypical location near large concentrations of it. Moreover, we are an uncommon form of ordinary matter. The commonest form is plasma (atoms dissociated into their electrically charged components), which typically emits bright, visible light because it is in stars, which are rather hot. We scums are mainly infra-red emitters because we contain liquids and complex chemicals which can exist only at a much lower range of temperatures.
…
What is a typical place in the universe like? Let me assume that you are reading this on Earth. In your mind’s eye, travel straight upwards a few hundred kilometres. … travel a few trillion kilometres further in the same direction. … it is not yet typical: you are still inside the Milky Way galaxy, and most places in the universe are not in any galaxy. Continue until you are clear outside the galaxy – say, a hundred thousand light years from Earth. At this distance you could not glimpse the Earth even if you used the most powerful telescope that humans have yet built. But the Milky Way still fill much of your sky. To get to a typical place in the universe, you have to imagine yourself at least a thousand times as far out as that, deep in intergalactic space.
…
Cold, dark and empty. That unimaginably desolate environment is typical of the universe – and is another measure of how untypical the Earth and its chemical scum are, in a straightforward physical sense. **
…
The Principle of Mediocrity is paradoxical too. Since it singles out anthropocentrism for special opprobrium among all forms of parochial misconception, it is itself anthropocentric. Also, it claims that all value judgements are anthropocentric, yet it itself is often expressed in value-laden terminology, such as ‘arrogance’, ‘just scum’ and the very word ‘mediocrity’. With respect to whose values are those disparagements to be understood? Why is arrogance even relevant as a criticism? Also, even if holding an arrogant opinion is morally wrong, morality is supposed to refer only to the internal organization of chemical scum. So how can it tell us anything about how the world beyond the scum is organized, as the Principle of Mediocrity purports to do? — page 45 to 47, 51

3.2.0 What is the Spaceship Earth idea?

Another influential idea about the human condition is sometimes given the dramatic name Spaceship Earth. Imagine a ‘generation ship’ – a spaceship on a journey so long that many generations of passengers live out their lives in transit. This has been proposed as a means of colonizing other star systems. In the Spaceship Earth idea, that generation ship is a metaphor for the biosphere – the system of all living things on Earth and the regions they inhabit. Its passengers represent all humans on Earth. Outside the spaceship, the universe is implacably hostile, but the interior is a vastly complex life-support system, capable of providing everything that the passengers need to thrive. Like the spaceship, the biosphere recycles all waste and, using its capacious nuclear power plant (the sun), it is completely self-sufficient. **
Just as the spaceship’s life-support system is designed to sustain its passengers, so the biosphere has the ‘appearance of design’: it seems highly adapted to sustaining us (claims the metaphor) because we were adapted to it by evolution. But its capacity is finite if we overload it, either by our sheer numbers or by adopting lifestyles too different from those that we evolved to live (the ones that it is ‘designed’ to support), it will break down. And, like the passengers on that spaceship, we get no second chances: if our lifestyle becomes too careless or profligate and we ruin our life-support system, we have nowhere else to go. — page 44

3.2.1 What is the Spaceship Earth idea criticism?

While Earth may be more hospitable than a ‘typical’ place in the universe, it is incorrect to say it is designed to meet our needs. We have made Earth ‘friendly’ through our knowledge. We designed the healthcare system, built the roads, and constructed the buildings. The ‘typical’ state of the universe and Earth is pure indifference to us, whether we’re alive or dead. We are the ones who have made a person’s death a shocking event. Earth never intended it.

if, tomorrow, physical conditions on the Earth’s surface were to change even slightly by astrophysical standards, then no humans could live here unprotected, just as they could not survive on a spaceship whose life-support system had broken down. Yet I am writing this in Oxford, England, where winter nights are likewise often cold enough to kill any human unprotected by clothing and other technology. So, while intergalactic space would kill me in a matter of seconds, Oxfordshire in its primeval state might do it in a matter of hours – which can be considered ‘life support’ only in the most contrived sense. There is a life-support system in Oxfordshire today, but it was not provided by the biosphere. It has been built by humans. It consists of clothes, houses, farms, hospitals, an electrical grid, a sewage system and so on. Nearly the whole of the Earth’s biosphere in its primeval state was likewise incapable of keeping an unprotected human alive for long. It would be much more accurate to call it a death trap for humans rather than a life-support system. Even the Great Rift Valley in eastern Africa, where our species evolved, was barely more hospitable than primeval Oxfordshire. Unlike the life-support system in that imagined spaceship, the Great Rift Valley lacked a safe water supply, and medical equipment, and comfortable living quarters, and was infested with predators, parasites and disease organisms. It frequently injured, poisoned, drenched, starved and sickened its ‘passengers’, and most of them died as a result. …
Hence the metaphor of a spaceship or a life-support system, is quite perverse: when humans design a life-support system, they design it to provide the maximum possible comfort, safety and longevity for its users within the available resources; the biosphere has no such priorities.
Nor is the biosphere a great preserver of species. In addition to being notoriously cruel to individuals, evolution involves continual extinctions of entire species. The average rate of extinction since the beginning of life on Earth has been about ten species per year (the number is known only very approximately), becoming much higher during the relatively brief periods that palaeontologists call ‘mass extinction events’. The rate at which species have come into existence has on balance only slightly exceeded the extinction rate, and the net effect is that the overwhelming majority of species that have ever existed on Earth (perhaps 99.9 per cent of them) are now extinct. — page 48, 49

3.3.0 What is the explanation that Richard Dawkins invokes to support Principle of Mediocrity?

Human attributes, like those of all other organisms, evolved under natural selection in an ancestral environment. That is why our senses are adapted to detecting things like the colours and smell of fruit, or the sound of a predator: being able to detect such things gave our ancestors a better chance of surviving to have offspring. But, for the same reason, Dawkins points out, evolution did not waste our resources on detecting phenomena that were never relevant to our survival. We cannot, for instance, distinguish between the colours of most stars with the naked eye. Our night vision is poor and monochromatic because not enough of our ancestors died of that limitation to create evolutionary pressure for anything better. So Dawkins argues – and here he is invoking the Principle of Mediocrity – that there is no reason to expect our brains to be any different from our eyes in this regard: they evolved to cope with the narrow class of phenomena that commonly occur in the biosphere, on approximately human scales of size, time, energy and so on. Most phenomena in the universe happen far above or below those scales. Some would kill us instantly; others could never affect anything in the lives of early humans. So, just as our senses cannot detect neutrinos or quasars or most other significant phenomena in the cosmic scheme of things, there is no reason to expect our brains to understand them. To the extent that they already do understand them, we have been lucky – but a run of luck cannot be expected to continue for long. Hence Dawkins agrees with an earlier evolutionary biologist, John Haldane, who expected that ‘the universe is not only queerer than we suppose, but queerer than we can suppose.’ — page 52

3.4.0 What is the world-view that both of these ideas imply?

The Spaceship Earth metaphor and the Principle of Mediocrity have both gained wide acceptance among scientifically minded people – to the extent of becoming truisms. This is despite the fact that, on the face of it, they argue in somewhat opposite directions: the Principle of Mediocrity stresses how typical the Earth and its chemical scum are (in the sense of being unremarkable), while Spaceship Earth stresses how untypical they are (in the sense of being uniquely suited to each other). But when the two ideas are interpreted in broad, philosophical ways, as they usually are, they can easily converge. Both see themselves as correcting much the same parochial misconceptions, namely that our experience of life on Earth is representative of the universe, and that the Earth is vast, fixed and permanent. They both stress instead that it is tiny and ephemeral. Both oppose arrogance: the Principle of Mediocrity opposes the pre-Enlightenment arrogance of believing ourselves significan in the world; the Spaceship Earth metaphor opposes the Enlightenment arrogance of aspiring to control the world. Both have a moral element: we should not consider ourselves significant, they assert; we should not expect the world to submit indefinitel to our depredations. …
At root, the Principle of Mediocrity and the Spaceship Earth metaphor overlap in a claim about reach: they both claim that the reach of the distinctively human way of being – that is to say, the way of problem-solving, knowledge-creating and adapting the world around us – is bounded. And they argue that its bounds cannot be very far beyond what it has already reached. Trying to go beyond that range must lead to failure and catastrophe respectively. — page 45, 54

3.4.1 What is the rebuttal that David provides for this world-view and Dawkins’s argument?

Davids writes that both ideas rely on the absence of explanation of why we might have have no limit. He rebutts by providing an explanation:

Humans cognitive capabilities are different from birds flying altitudes because we have achieved universality; we can create explanatory knowledge:

The ability to create and use explanatory knowledge gives people a power to transform nature which is ultimately not limited by parochial factors, as all other adaptations are, but only by universal laws. This is the cosmic significant of explanatory knowledge – and hence of people, whom I shall henceforward define as entities that can create explanatory knowledge*. — page 56*

Thus, people can reach any physical transformation that is not forbidden by the laws of physics because our created knowledge is about the nature itself, which is universal.

3.4.2 What is the idea of ‘universal constructors’ that David introduces?

Humans are ‘universal constructors’ because we can create explanatory knowledge that has universal reach — anything that is within the laws of physics:

Every physical transformation that we can imagine is either forbidden by the laws of physics, or is achievable given the right knowledge.

Using knowledge to cause automated physical transformations is, in itself, not unique to humans. It is the basic method by which all organisms keep themselves alive: every cell is a chemical factory. The difference between humans and other species is in what kind of knowledge they can use (explanatory instead of rule-of-thumb) and in how they create it (conjecture and criticism of ideas, rather than the variation and selection of genes). It is precisely those two differences that explain why every other organism can function only in a certain range of environments that are hospitable to it, while humans transform inhospitable environments like the biosphere into support systems for themselves. And, while every other organism is a factory for converting resources of a fixe type into more such organisms, human bodies (including their brains) are factories for transforming anything into anything that the laws of nature allow. They are ‘universal constructors’. — page 58

Human Reach and Problems

As we have seen, human reach is equal to the explanatory knowledge. So humans can exist anywhere where explanatory knowledge can be created.

3.5.0 What are the necessary features of the environment for knowledge creation to be possible?

Environment must have access to matter, energy and evidence.

Access to matter is one. For example, the trick of extracting oxygen from moon rocks depends on having compounds of oxygen available. With more advanced technology, one could manufacture oxygen by transmutation; but, no matter how advanced one’s technology is, one still needs raw materials of some sort. And, although mass can be recycled, creating an open-ended stream of knowledge depends on having an ongoing supply of it, both to make up for inevitable inefficiencies and to make the additional memory capacity to store new knowledge as it is created.
Also, many of the necessary transformations require energy: something must power conjectures and scientific experiments and all those manufacturing processes; and, again, the laws of physics forbid the creation of energy from nothing. So access to an energy supply is also a necessity. To some extent, energy and mass can be transformed into each other. For instance, transmuting hydrogen into any other element releases energy through nuclear fusion. Energy can also be converted into mass by various subatomic processes (but I cannot imagine naturally occurring circumstances in which those would be the best way of obtaining matter).
In addition to matter and energy, there is one other essential requirement, namely evidence: the information needed to test scientific theories. The Earth’s surface is rich in evidence. — page 61

3.5.1 Are those features widespread across the universe?

Yes they are! A ‘typical’ place that we have portrayed rebutting Principle of Mediocrity possess all of them:

Intergalactic space is indeed very empty by human standards. But each of those solar-system-sized cubes still contains over a billion tonnes of matter – mostly in the form of ionized hydrogen. A billion tonnes is more than enough mass to build, say, a space station and a colony of scientists creating an open-ended stream of knowledge – if anyone were present who knew how to do that.
No human today knows how. For instance, one would first have to transmute some of the hydrogen into other elements. Collecting it from such a diffuse source would be far beyond us at present. And, although some types of transmutation are already routine in the nuclear industry, we do not know how to transmute hydrogen into other elements on an industrial scale. Even a simple nuclear-fusion reactor is currently beyond our technology. But physicists are confident that it is not forbidden by any laws of physics, in which case, as always, it can only be a matter of knowing how. — page 66

3.6.0 What are problems?

Problems are inevitable. Problems are soluble.

3.6.1 Why problems are inevitable?

Nor will we ever run out of problems. The deeper an explanation is, the more new problems it creates. That must be so, if only because there can be no such thing as an ultimate explanation: just as ‘the gods did it’ is always a bad explanation, so any other purported foundation of all explanations must be bad too. It must be easily variable because it cannot answer the question: why that foundation and not another? Nothing can be explained only in terms of itself. That holds for philosophy just as it does for science, and in particular it holds for moral philosophy: no utopia is possible, but only because our values and our objectives can continue to improve indefinitely. — page 64

3.3 A different explanation for why problems are inevitable.
It has to do with fallibility of our knowledge. We grow knowledge by refutations, not supportive arguments, hence, we’ll never approach “100% truth and certainty once and for all”. We’ll always be wrong, and we’ll always strive to be less wrong. You arrive at the conclusions by taking seriously the idea that all observation is theory-laden and hence fallible.

3.6.2 Why problems are soluble?

It is inevitable that we face problems, but no particular problem is inevitable. We survive, and thrive, by solving each problem as it comes up. And, since the human ability to transform nature is limited only by the laws of physics, none of the endless stream of problems will ever constitute an impassable barrier. So a complementary and equally important truth about people and the physical world is that problems are soluble. By ‘soluble’ I mean that the right knowledge would solve them. It is not, of course, that we can possess knowledge just by wishing for it; but it is in principle accessible to us. — page 64

3.7.0 What is the meaning of ‘the beginning of infinity’ idea?

As problems are inevitable, yet soluble, we’ll always be at the beginning of infinite progress that lies in front of us. We’ll never do anything else but scratch a surface.

That progress is both possible and desirable is perhaps the quintessential idea of the Enlightenment. It motivates all traditions of criticism, as well as the principle of seeking good explanations. But it can be interpreted in two almost opposite ways, both of which, confusingly, are known as ‘perfectibility’. One is that humans, or human societies, are capable of attaining a state of supposed perfection – such as the Buddhist or Hindu ‘nirvana’, or various political utopias. The other is that every attainable state can be indefinitel improved. Fallibilism rules out that first position in favour of the second. Neither the human condition in particular nor our explanatory knowledge in general will ever be perfect, nor even approximately perfect. We shall always be at the beginning of infinity. …
So a typical location in the universe is amenable to the open-ended creation of knowledge. And therefore so are almost all other kinds of environment, since they have more matter, more energy and easier access to evidence than intergalactic space. The thought experiment considered almost the worst possible case. Perhaps the laws of physics do not allow knowledge-creation inside, say, the jet of a quasar. Or perhaps they do. But either way, in the universe at large, knowledge-friendliness is the rule, not the exception. That is to say, the rule is person-friendliness to people who have the relevant knowledge. Death is the rule for those who do not. These are the same rules that prevailed in the Great Rift Valley from whence we came, and have prevailed ever since. — page 65, 69

Significance of People

We have started this chapter by regarding people as ‘chemical scum’, small and insignificant. So far, we have just argued that Earth is not a typical place in the universe, but we still have a long way to go. Earth ‘untypicalness’ implies little about humans.

3.8.0 Are humans significant in the cosmic scheme of things?

If one would want to understand the universe one would have to invoke humans: what decisions we’ve made, what problems we’ve solved, what morals we have. This is because knowledge is one of the most powerful forces in the universe, you just a ‘sliver’ of it to decide the fate of the stars. Hence humans, or any other intelligent being is cosmically significant.

In the longer run, humans may colonize other solar systems and, by increasing their knowledge, control ever more powerful physical processes. If people ever choose to live near a star that is capable of exploding, they may well wish to prevent such an explosion – probably by removing some of the material from the star. Such a project would use many orders of magnitude more energy than humans currently control, and more advanced technology as well. But it is a fundamentally simple task, not requiring any steps that are even close to limits imposed by the laws of physics. So, with the right knowledge, it could be achieved. Indeed, for all we know, engineers elsewhere in the universe are already achieving it routinely. And consequently it is not true that the attributes of supernovae in general are independent of the presence or absence of people, or of what those people know and intend.
More generally, if we want to predict what a star will do, we first have to guess whether there are any people near it, and, if so, what knowledge they may have and what they may want to achieve. Outside our parochial perspective, astrophysics is incomplete without a theory of people, just as it is incomplete without a theory of gravity or nuclear reactions. — page 70

David then goes on to argue that to understand human behavior you would need to understand everything of cosmic significance. For instance, there is a lab that studies the presence of extraterrestrial life and they have a bottle of champagne that would be opened only once aliens are found. Hence, to understand the behavior of this bottle of champagne one must invoke such cosmically significant things as extraterrestrial life. In fact, there are multiple similar labs that study something cosmically significant and have similar bottles of champagne. Thus, to understand humans one must understand everything that is cosmically significant.

Similar champagne bottles are stored in other laboratories. The popping of each such cork signals a discovery about something significant in the cosmic scheme of things. Thus the study of the behaviour of champagne corks and other proxies for what people do is logically equivalent to the study of everything significant It follows that humans, people and knowledge are not only objectively significant they are by far the most significant phenomena in nature – the only ones whose behaviour cannot be understood without understanding everything of fundamental importance. — page 73

From The Fabric of Reality Handbook, with answers in footnotes:

FoR: 8.9.0 Why life is significant?9

3.9.0 What is the difference between explanatory and non-explanatory knowledge?

David claims that humans create explanatory knowledge which is different from non-explanatory knowledge that is created through evolution. Explanatory knowledge can engage with unexperienced evidence or non-existent phenomena (like aliens). Non-explanatory cannot cross that gap, and thus, is inferior. According to David, dinosaurs would never be able to deflect an asteroid, because there was no evolutionary pressure from it. Yet humans can imagine a potential disaster and prepare in advance!

Note also that the SETI instrument is exquisitely adapted to detecting something that has never yet been detected. Biological evolution could never produce such an adaptation. Only scientific knowledge can. This illustrates why non-explanatory knowledge cannot be universal. Like all science, the SETI project can conjecture the existence of something, calculate what some of its observable attributes would be, and then construct an instrument to detect it. Non-explanatory systems cannot cross the conceptual gap that an explanatory conjecture crosses, to engage with unexperienced evidence or non-existent phenomena. Nor is that true only of fundamental science: if such-and-such a load were put on the proposed bridge it would collapse, says the engineer, and such statements can be true and immensely valuable even if the bridge is never even built, let alone subjected to such a load. — page 73

🌱 4 — Creation

As we have discussed the significance of humans and life we shall look at how those came to be. The central questions of this chapter are:

How organisms were initially created? How knowledge in organisms arises?
What is the difference between human and biological knowledge?
What is the fine tuning problem and its potential solutions?

Summary

There have been several misconceptions about how life came to be. Creationism assign all the responsibility to god, but then: Who created god? Spontaneous generation theory assumes that life just assembles from some laws of physics. This might be a complete explanation for some inorganic phenomena like rainbows and rocks, but not for mice. This is because mice has an appearance of design — it is adapted to some purpose (i.e. it has some instantiated knowledge). Lamarckism assumes that knowledge was created by improvements that are gained during animals life and are passed on to their children. This doesn’t explain how the first knowledge (thing) is created for it to be improved afterwards.

Neo-Darwinism explains that the basis of life is molecular. Genes use organisms to replicate themselves. Evolution optimizes not the good of the species or organisms, but spreading the most ‘viral’ genes throughout its population (regardless of its effect on organisms). A replicator doesn’t have to be a gene, it can be a joke. Knowledge is an abstract replicator — it is substrate independent.

Fine tuning is a problem of why our laws of physics are so well fit for life to arise — even with minor changes of physical constants the universe would collapse. There has been many solutions proposed but none seem to give a complete explanation. Fine tuning is a problem that is yet to be solved by science.

Practice chapter questions as flashcards here.

Misconceptions about Biological Knowledge

4.1.0 What is Creationism?

Creationism is the idea that some supernatural being or beings designed and created all biological adaptations. In other words, ‘the gods did it.’ — page 79

4.1.1 What is creationism criticism?

As I explained in Chapter 1, theories of that form are bad explanations. Unless supplemented by hard-to-vary specifics they do not even address the problem – just as ‘the laws of physics did it’ will never win you a Nobel prize, and ‘the conjurer did it’ does not solve the mystery of the conjuring trick. …
the problem of explaining the biosphere is that of explaining how the knowledge embodied in its adaptations could possibly have been created. In particular, a putative designer of any organism must also have created the knowledge of how that organism works. Creationism thus faces an inherent dilemma: is the designer a purely supernatural being – one who was ‘just there’, complete with all that knowledge – or not? A being who was ‘just there’ would serve no explanatory purpose (in regard to the biosphere), since then one could more economically say that the biosphere itself ‘just happened’, complete with that same knowledge, embodied in organisms. On the other hand, to whatever extent a creationist theory provides explanations about how supernatural beings designed and created the biosphere, they are no longer supernatural beings but merely unseen ones. They might, for instance, be an extraterrestrial civilization. But then the theory is not really creationism – unless it proposes that the extraterrestrial designers themselves had supernatural designers. — page 79

David then adds that the creator must have had the intention for the designs (organisms). But all species are filled with suboptimal choices. For instance, many animals can create vitamin C on their own and humans have a specific gene to do that, but it is erroneous: it doesn’t create vitamin C!

4.2.0 What is the spontaneous generation theory?

Spontaneous generation is the formation of organisms not as offspring of other organisms, but entirely from non-living precursors – for example, the generation of mice from a pile of rags in a dark corner. The theory that small animals are being spontaneously generated like that all the time (in addition to reproducing in the normal way) was part of unquestioned conventional wisdom for millennia, and was taken seriously until well into the nineteenth century. — page 81

4.1 On inorganic things becoming organic.
We don’t know how something that is inorganic becomes organic. The Miller–Urey experiment is the first experiment to produce amino acids (organic compounds) from inorganic ones by mimicking the early atmosphere of the Earth. Yet, amino acids are no more than bricks of living organisms, we are yet to understand how exactly a pile of bricks (amino acids) becomes Sydney Opera House (any complex living being).

4.2.1 What is the experiment that has disproved it?

We have performed an experiment by boiling a broth without air contamination and with and then wait to see whether would life arise or not. Turned out, without air contamination it didn’t.

Louis Pasteur’s 1859 experiment showed that a boiled nutrient broth did not give rise spontaneously to new life, but that if direct access to air was permitted, the broth decomposed, implying that small organisms (in modern terms, microbial spores) had fallen in and started to grow in the broth. — Wikipedia, Spontaneous generation

Neo-Darwinism

4.5.0 What is Neo-Darwinism?

The central idea of neo-Darwinism is that evolution favours the genes that spread best through the population. There is much more to this idea than meets the eye, as I shall explain. — page 89

4.5.1 What is the common misconception about it?

A common misconception about Darwinian evolution is that it maximizes ‘the good of the species’. That provides a plausible, but false, explanation of apparently altruistic behaviour in nature, such as parents risking their lives to protect their young, or the strongest animals going to the perimeter of a herd under attack – thereby decreasing their own chances of having a long and pleasant life or further offspring. Thus, it is said, evolution optimizes the good of the species, not the individual. But, in reality, evolution optimizes neither. — page 89

4.5.2 Explain why it is a misconception.

To see why, consider this thought experiment. Imagine an island on which the total number of birds of a particular species would be maximized if they nested at, say, the beginning of April. The explanation for why a particular date is optimal will refer to various trade-offs involving factors such as temperature, the prevalence of predators, the availability of food and nesting materials, and so on. Suppose that initially the whole population has genes that cause them to nest at that optimum time. That would mean that those genes were well adapted to maximizing the number of birds in the population – which one might call ‘maximizing the good of the species’.
Now suppose that this equilibrium is disturbed by the advent of a mutant gene in a single bird which causes it to nest slightly earlier – say, at the end of March. Assume that when a bird has built a nest, the species’ other behavioural genes are such that it automatically gets whatever cooperation it needs from a mate. That pair of birds would then be guaranteed the best nesting site on the island – an advantage which, in terms of the survival of their offspring, might well outweigh all the slight disadvantages of nesting earlier. In that case, in the following generation, there will be more March-nesting birds, and, again, all of them will find excellent nesting sites. That means that a smaller proportion than usual of the April-nesting variety will find good sites: the best sites will have been taken by the time they start looking. In subsequent generations, the balance of the population will keep shifting towards the March-nesting variants. If the relative advantage of having the best nesting sites is large enough, the Aprilnesting variant could even become extinct. If it arises again as a mutation, its holder will have no offspring, because all sites will have been taken by the time it tries to nest.
Thus the original situation that we imagined – with genes that were optimally adapted to maximizing the population (‘benefiting the species’) – is unstable. There will be evolutionary pressure to make the genes become less well adapted to that function.
This change has harmed the species, in the sense of reducing its total population (because the birds are no longer nesting at the optimum time). It may thereby also have harmed it by increasing the risk of extinction, making it less likely to spread to other habitats, and so on. So an optimally adapted species may in this way evolve into one that is less ‘well off’ by any measure.
If a further mutant gene then appears, causing nesting still earlier in March, the same process may be repeated, with the earlier-nesting genes taking over and the total population falling again. Evolution will thus drive the nesting time ever earlier, and the population lower. A new equilibrium would be reached only when the advantage to an individual bird’s offspring of getting the very best nesting site was finally outweighed by the disadvantages of slightly earlier nesting. That equilibrium might be very far from what was optimal for the species. — page 89

4.5.3 What then has evolution achieved in this example? What is its goal if not to benefit the organism?

It has maximized the number of genes that spread the most throughout the population. Sometimes this can hurt organisms, even get them extinct. But it is also in genes interest to keep organisms healthy and strong because then they are less likely to spread.

What exactly has the evolution of those birds achieved during that period? It has optimized not the functional adaptation of a variant gene to its environment – the attribute that would have impressed Paley – but the relative ability of the surviving variant to spread through the population. An April-nesting gene is no longer able to propagate itself to the next generation, even though it is functionally the best variant. The early-nesting gene that replaced it may still be tolerably functional, but it is fittest for nothing except preventing variants of itself from procreating. From the point of view of both the species and all its members, the change brought about by this period of its evolution has been a disaster. But evolution does not ‘care’ about that. It favours only the genes that spread best through the population.
Evolution can even favour genes that are not just suboptimal, but wholly harmful to the species and all its individuals. A famous example is the peacock’s large, colourful tail, which is believed to diminish the bird’s viability by making it harder to evade predators, and to have no useful function at all.
If the best-spreading genes impose sufficiently large disadvantages on the species, the species becomes extinct. Nothing in biological evolution prevents that. It has presumably happened many times in the history of life on Earth, to species less lucky than the peacock. Dawkins named his tour-de-force account of neo-Darwinism The Selfish Gene because he wanted to stress that evolution does not especially promote the ‘welfare’ of species or individual organisms. But, as he also explained, it does not promote the ‘welfare’ of genes either: it adapts them not for survival in larger numbers, nor indeed for survival at all, but only for spreading through the population at the expense of rival genes, particularly slight variants of themselves.
Is it sheer luck, then, that most genes do usually confer some, albeit less than optimal, functional benefit on their species, and on their individual holders? No. Organisms are the slaves, or tools, that genes use to achieve their ‘purpose’ of spreading themselves through the population. (That is the ‘purpose’ that Paley and even Darwin never guessed.) Genes gain advantages over each other in part by keeping their slaves alive and healthy, just as human slave owners did. Slave owners were not working for the benefit of their workforces, nor for the benefit of individual slaves: it was solely to achieve their own objectives that they fed and housed their slaves, and indeed forced them to reproduce. Genes do much the same thing. — page 91

4.6.0 What would refute Neo-Darwinism?

Evidence which, in the light of the best available explanation, implies that knowledge came into existence in a different way. For instance, if an organism was observed to undergo only (or mainly) favourable mutations, as predicted by Lamarckism or spontaneous generation, then Darwinism’s ‘random variation’ postulate would be refuted. If organisms were observed to be born with new, complex adaptations – for anything – of which there were no precursors in their parents, then the gradual-change prediction would be refuted and so would Darwinism’s mechanism of knowledge-creation. If an organism was born with a complex adaptation that has survival value today, yet was not favoured by selection pressure in its ancestry (say, an ability to detect and use internet weather forecasts to decide when to hibernate), then Darwinism would again be refuted. — page 96

Differences between Biological and Human Knowledge

4.7.0 What is a replicator?

[Neo-Darwinism] is based on the idea of a replicator (anything that contributes causally to its own copying).* For instance, a gene conferring the ability to digest a certain type of food causes the organism to remain healthy in some situations where it would otherwise weaken or die. Hence it increases the organism’s chances of having offspring in the future, and those offspring would inherit, and spread, copies of the gene.
Ideas can be replicators too. For example, a good joke is a replicator: when lodged in a person’s mind, it has a tendency to cause that person to tell it to other people, thus copying it into their minds. Dawkins coined the term memes (rhymes with ‘dreams’) for ideas that are replicators. Most ideas are not replicators: they do not cause us to convey them to other people. Nearly all long-lasting ideas, however, such as languages, scientific theories and religious beliefs, and the ineffable states of mind that constitute cultures such as being British, or the skill of performing classical music, are memes (or ‘memeplexes’ – collections of interacting memes). — page 93

FoR: 8.2.0 What is a replicator?10

4.8.0 Elaborate on how a replicator idea is related to knowledge.

Non-explanatory human knowledge can also evolve in an analogous way: rules of thumb are not passed on perfectly to the next generation of users, and the ones that survive in the long run are not necessarily the ones that optimize the ostensible function. For instance, a rule that is expressed in an elegant rhyme may be remembered, and repeated, better than one that is more accurate but expressed in ungainly prose. Also, no human knowledge is entirely non-explanatory. There is always at least a background of assumptions about reality against which the meaning of a rule of thumb is understood, and that background can make some false rules of thumb seem plausible.
Explanatory theories evolve through a more complicated mechanism. Accidental errors in transmission and memory still play a role, but a much smaller one. That is because good explanations are hard to vary even without being tested, and hence random errors in the transmission of a good explanation are easier for the receiver to detect and correct. The most important source of variation in explanatory theories is creativity. For instance, when people are trying to understand an idea that they hear from others, they typically understand it to mean what makes most sense to them, or what they are most expecting to hear, or what they fear to hear, and so on. Those meanings are conjectured by the listener or reader, and may differ from what the speaker or writer intended. In addition, people often try to improve explanations even when they have received them accurately: they make creative amendments, spurred by their own criticism. If they then pass the explanation on to others, they usually try to pass on what they consider to be the improved version. — page 94

FoR: 8.2.1 Is anything that can be copied a replicator?11

4.9.0 What does it mean for something to be an abstract replicator?

An information that causes its own replication and can be represented in different physical forms. Knowledge is an abstract replicator. It is substrate independent: it exists independently of the physical form it is in, like being on a piece of paper or inside a computer as a program.

Unlike genes, many memes take different physical forms every time they are replicated. People rarely express ideas in exactly the same words in which they heard them. They also translate from one language to another, and between spoken and written language, and so on. Yet we rightly call what is transmitted the same idea – the same meme – throughout. Thus, in the case of most memes, the real replicator is abstract: it is the knowledge itself. This is in principle true of genes as well: biotechnology routinely transcribes genes into the memories of computers, where they are stored in a different physical form. Those records could be translated back into DNA strands and implanted in different animals. …
So, both human knowledge and biological adaptations are abstract replicators: forms of information which, once they are embodied in a suitable physical system, tend to remain so while most variants of them do not. — page 94

Fine Tuning Problem

This sub-chapter explores why the laws of physics seems to be fine tuned. David doesn’t provide a conclusive explanation because this is still an open problem for science to solve. I think that understanding this part of the book is best through a mind map, not flashcards.

You can find the mindmap here.

I have added quotes from the book as comments.

The conclusion of fine tuning section is that this is a problem in science that is yet to be solved.

➕ 5 — The Reality of Abstractions

We have studied how human and biological knowledge is created and misconceptions about it. But what is knowledge? David claims it is an abstraction, something that has no singular physical form.

The central questions of the chapter are:

What is emergence? What impact it has on our understanding of the world?
What are abstractions? Do they exist? How do we know about them?
How abstractions play in our understanding of morality and aesthetics?

Summary

Our world consists of two types of things: abstract and physical ones. Denying either would lead to error. Complete explanations usually requires both, just as with Hofstadter’s domino computer and primality. And as we have previously stated: something exists as long as it is in our best explanations of reality; abstractions are in our best explanations of reality, hence they exist.

We understand and operate the world through emergent abstract phenomena. Emergent because of tractability reasons: high-level simplicity is easier to navigate than low-level complexity. Abstract because we never experience the world directly. All our observations are theory laden and are just electrical currents in our brain, so there must be something else that we perceive (for we never ‘observe’ directly electrical currents). We perceive emergent abstractions.

Moral and aesthetic theories are abstractions. We can learn about them through conjecture and refutations. Our understanding of theories relies on explanations of factual events and logic, that could be wrong. Hence, moral and aesthetic theories could be wrong.

You practice chapter questions as flashcards here.

Emergence

5.1.0 What is emergence?

High-level simplicity that arises from low-level complexity. For example trillions of atoms interacting with each other yield a boiling tea. You can explain and predict water boiling, but you can’t explain and explain movement of every atom.

Levels of emergence Sets of phenomena that can be explained well in terms of each other without analysing them into their constituent entities such as atoms. — page 123

5.2.0 How high-level explanations are related to low-level ones?

Whenever a high-level explanation does follow logically from low-level ones, that also means that the high-level one implies something about the low-level ones. Thus, additional high-level theories, provided that they were all consistent, would place more and more constraints on what the low-level theories could be. So it could be that all the high-level explanations that exist, taken together, imply all the low-level ones, as well as vice versa. Or it could be that some low-level, some intermediate-level and some high-level explanations, taken together, imply all explanations. I guess that that is so. — page 110

If this relationship holds for explanations, it holds for physical phenomena too.

5.3.0 How is emergence related to computational complexity?

everyday events are stupendously complex when expressed in terms of fundamental physics. If you fill a kettle with water and switch it on, all the supercomputers on Earth working for the age of the universe could not solve the equations that predict what all those water molecules will do – even if we could somehow determine their initial state and that of all the outside influence on them, which is itself an intractable task.
Fortunately, some of that complexity resolves itself into a higher-level simplicity. For example, we can predict with some accuracy how long the water will take to boil. To do so, we need know only a few physical quantities that are quite easy to measure, such as its mass, the power of the heating element, and so on. For greater accuracy we may also need information about subtler properties, such as the number and type of nucleation sites for bubbles. But those are still relatively ‘high-level’ phenomena, composed of intractably large numbers of interacting atomic-level phenomena. Thus there is a class of high-level phenomena – including the liquidity of water and the relationship between containers, heating elements, boiling and bubbles – that can be well explained in terms of each other alone, with no direct reference to anything at the atomic level or below. In other words, the behaviour of that whole class of high-level phenomena is quasi-autonomous – almost self-contained. This resolution into explicability at a higher, quasi-autonomous level is known as emergence. — page 107

5.3.1 How emergence impacts our ability to understand the world?

High-level simplicity allows us to understand the world, because we could never operate at a low-level complexity due to computational intractability (see FoR 9.2.0 card). It allows for successive improvements of theories — we don’t have to get something 100% right for it to be any useful (which we can’t do anyways).

In any case, emergent phenomena are essential to the explicability of the world. Long before humans had much explanatory knowledge, they were able to control nature by using rules of thumb. Rules of thumb have explanations, and those explanations were about high-level regularities among emergent phenomena such as fir and rocks. Long before that, it was only genes that were encoding rules of thumb, and the knowledge in them, too, was about emergent phenomena. Thus emergence is another beginning of infinity all knowledge-creation depends on, and physically consists of, emergent phenomena. Emergence is also responsible for the fact that discoveries can be made in successive steps, thus providing scope for the scientific method. The partial success of each theory in a sequence of improving theories is tantamount to the existence of a ‘layer’ of phenomena that each theory explains successfully – though, as it then turns out, partly mistakenly. — page 111

FoR: 9.2.0 Many people criticize the universality of computation (and virtual-reality rendering) for its impracticality. It is a highly abstract concept, and its ‘in principle’ effects are miniscule in real-life (because no object has infinite time or memory). Hence, it is not a profound property of reality. What counterargument David provides and what are its implications?12

5.4.0 What is instrumentalism, holism and its criticism?

There is often a moral overtone to reductionism (science should be essentially reductive). This is related both to instrumentalism and to the Principle of Mediocrity, which I criticized in Chapters 1 and 3. Instrumentalism is rather like reductionism except that, instead of rejecting only high-level explanations, it tries to reject all explanations. The Principle of Mediocrity is a milder form of reductionism: it rejects only high-level explanations that involve people. While I am on the subject of bad philosophical doctrines with moral overtones, let me add holism, a sort of mirror image of reductionism. It is the idea that the only valid explanations (or at least the only significant ones) are of parts in terms of wholes. Holists also often share with reductionists the mistaken belief that science can only (or should only) be reductive, and therefore they oppose much of science. All those doctrines are irrational for the same reason: they advocate accepting or rejecting theories on grounds other than whether they are good explanations. — page 110

FoR: 1.6.0 What is reductionism?13

FoR: 1.6.1 What is its criticism?14

Biological and Explanatory Knowledge

5.5.0 What is the difference between biological and explanatory knowledge?

Such large discontinuities in the meanings of successive scientific theories have no biological analogue: in an evolving species, the dominant strain in each generation differs only slightly from that in the previous generation. [He is referring to the difference in explaining the world with Einsteinian vs Newtonian physics.] Nevertheless, scientific discovery is a gradual process too; it is just that, in science, all the gradualness, and nearly all the criticism and rejection of bad explanations, takes place inside the scientists’ minds. As Popper put it, ‘We can let our theories die in our place.’
There is another, even more important, advantage in that ability to criticize theories without staking one’s life on them. In an evolving species, the adaptations of the organisms in each generation must have enough functionality to keep the organism alive, and to pass all the tests that they encounter in propagating themselves to the next generation. In contrast, the intermediate explanations leading a scientist from one good explanation to the next need not be viable at all. The same is true of creative thought in general. This is the fundamental reason that explanatory ideas are able to escape from parochialism, while biological evolution, and rules of thumb, cannot. — page 113

Sometimes I will reference past questions from The Beginning of Infinity Handbook, just search the question’s number (3.9.0 for this one) to find the answer. They will start with BoI instead of FoR.

BoI: 3.9.0 What is the difference between explanatory and non-explanatory knowledge?

Existence of Abstractions

5.6.0 How abstractions impact explaining things?

Abstractions are essential, without them many explanations would be incomplete. When you have lost in chess to a computer, one must invoke many abstractions to provide a full explanation, like a computer program, rules of chess, win, loss and so on.

abstractions are essential to a fuller explanation. You know that if your computer beats you at chess, it is really the program that has beaten you, not the silicon atoms or the computer as such. The abstract program is instantiated physically as a high-level behaviour of vast numbers of atoms, but the explanation of why it has beaten you cannot be expressed without also referring to the program in its own right. That program has also been instantiated, unchanged, in a long chain of different physical substrates, including neurons in the brains of the programmers and radio waves when you downloaded the program via wireless networking, and finally as states of long- and short-term memory banks in your computer. The specific of that chain of instantiations may be relevant to explaining how the program reached you, but it is irrelevant to why it beat you: there, the content of the knowledge (in it, and in you) is the whole story. That story is an explanation that refers ineluctably to abstractions; and therefore those abstractions exist, and really do affect physical objects in the way required by the explanation. — page 114

In fact, all explanations are abstractions.

5.6.1 Explain the computer-domino example that illustrates this point.

The computer scientist Douglas Hofstadter has a nice argument that this sort of explanation is essential in understanding certain phenomena. In his book I am a Strange Loop (2007) he imagines a special-purpose computer built of millions of dominoes. They are set up – as dominoes often are for fun – standing on end, close together, so that if one of them is knocked over it strikes its neighbour and so a whole stretch of dominoes falls, one after another. But Hofstadter’s dominoes are spring-loaded in such a way that, whenever one is knocked over, it pops back up after a fixed time. Hence, when a domino falls, a wave or ‘signal’ of falling dominoes propagates along the stretch in the direction in which it fell until it reaches either a dead end or a currently fallen domino. By arranging these dominoes in a network with looping, bifurcating and rejoining stretches, one can make these signals combine and interact in a sufficiently rich repertoire of ways to make the whole construction into a computer: a signal travelling down a stretch can be interpreted as a binary ‘1’, and the lack of a signal as a binary ‘0’, and the interactions between such signals can implement a repertoire of operations – such as ‘and’, ‘or’ and ‘not’ – out of which arbitrary computations can be composed.
One domino is designated as the ‘on switch’: when it is knocked over, the domino computer begins to execute the program that is instantiated in its loops and stretches. The program in Hofstadter’s thought experiment computes whether a given number is a prime or not. One inputs that number by placing a stretch of exactly that many dominos at a specific position, before tripping the ‘on switch’. Elsewhere in the network, a particular domino will deliver the output of the computation: it will fall only if a divisor is found, indicating that the input was not a prime.
Hofstadter sets the input to the number 641, which is a prime, and trips the ‘on switch’. Flurries of motion begin to sweep back and forth across the network. All 641 of the input dominos soon fall as the computation ‘reads’ its input – and snap back up and participate in further intricate patterns. It is a lengthy process, because this is a rather inefficient way to perform computations – but it does the job.
Now Hofstadter imagines that an observer who does not know the purpose of the domino network watches the dominoes performing and notices that one particular domino remains resolutely standing, never affected by any of the waves of downs and ups sweeping by.
The observer points at [that domino] and asks with curiosity, ‘How come that domino there is never falling?’
We know that it is the output domino, but the observer does not. Hofstadter continues:
Let me contrast two different types of answer that someone might give. The first type of answer – myopic to the point of silliness – would be, ‘Because its predecessor never falls, you dummy!’
Or, if it has two or more neighbours, ‘Because none of its neighbours ever fall.’
To be sure, this is correct as far as it goes, but it doesn’t go very far. It just passes the buck to a different domino.
In fact one could keep passing the buck from domino to domino, to provide ever more detailed answers that were ‘silly, but correct as far as they go’. Eventually, after one had passed the buck billions of times (many more times than there are dominoes, because the program ‘loops’), one would arrive at that first domino – the ‘on switch’. At that point, the reductive (to high-level physics) explanation would be, in summary, ‘That domino did not fall because none of the patterns of motion initiated by knocking over the “on switch” ever include it.’ But we knew that already. We can reach that conclusion – as we just have – without going through that laborious process. And it is undeniably true. But it is not the explanation we were looking for because it is addressing a different question – predictive rather than explanatory – namely, if the firs domino falls, will the output domino ever fall? And it is asking at the wrong level of emergence. What we asked was: why does it not fall? To answer that, Hofstadter then adopts a different mode of explanation, at the right level of emergence:
The second type of answer would be, ‘Because 641 is prime.’ Now this answer, while just as correct (indeed, in some sense it is far more on the mark), has the curious property of not talking about anything physical at all. Not only has the focus moved upwards to collective properties . . . these properties somehow transcend the physical and have to do with pure abstractions, such as primality.
Hofstadter concludes, ‘The point of this example is that 641’s primality is the best explanation, perhaps even the only explanation, for why certain dominoes did fall and certain others did not fall.
Just to correct that slightly: the physics-based explanation is true as well, and the physics of the dominoes is also essential to explaining why prime numbers are relevant to that particular arrangement of them. But Hofstadter’s argument does show that primality must be part of any full explanation of why the dominos did or did not fall. Hence it is a refutation of reductionism in regard to abstractions. For the theory of prime numbers is not part of physics. It refers not to physical objects, but to abstract entities – such as numbers, of which there is an infinite set. — page 115

Hofstadter presents his argumentation for reductionism in the mind-body problem:

His book is primarily about one particular emergent phenomenon, the mind – or, as he puts it, the ‘I’. He asks whether the mind can consistently be thought of as affecting the body – causing it to do one thing rather than another, given the all-embracing nature of the laws of physics. This is known as the mind–body problem. For instance, we often explain our actions in terms of choosing one action rather than another, but our bodies, including our brains, are completely controlled by the laws of physics, leaving no physical variable free for an ‘I’ to affect in order to make such a choice. Following the philosopher Daniel Dennett, Hofstadter eventually concludes that the ‘I’ is an illusion. Minds, he concludes, can’t ‘push material stuff around’, because ‘physical law alone would suffice to determine [its] behaviour’. Hence his reductionism. — page 117

5.7.0 What is David’s counterargument to why emergent abstract phenomena are as important as low-level, physical one (i.e. counter arguments for the reductive view on the mind-body problem)?

No explanation of reality would be complete without referring to abstractions and emergent things. We as people always operate and understand the world on that level. If one tries to embrace reductionism, they would have incomplete explanations which would deny existence of abstractions, such as numbers or their primality. This means that reality consists of two types of objects: physical and abstract. As it is a mistake to embrace ‘abstractionism’ and deny the existence of anything physical, embracing reductionism is also wrong

But, first of all, physical laws can’t push anything either. They only explain and predict. And they are not our only explanations. The theory that the domino stands ‘because 641 is a prime (and because the domino network instantiates a primality-testing algorithm)’ is an exceedingly good explanation. What is wrong with it? It does not contradict the laws of physics. It explains more than any explanation purely in terms of those laws. And no known variant of it can do the same job.
Second, that reductionist argument would equally deny that an atom can ‘push’ (in the sense of ‘cause to move’) another atom, since the initial state of the universe, together with the laws of motion, has already determined the state at every other time.
Third, the very idea of a cause is emergent and abstract. It is mentioned nowhere in the laws of motion of elementary particles, and, as the philosopher David Hume pointed out, we cannot perceive causation, only a succession of events. Also, the laws of motion are ‘conservative’ – that is to say, they do not lose information. That means that, just as they determine the final state of any motion given the initial state, they also determine the initial state given the final state, and the state at any time from the state at any other time. So, at that level of explanation, cause and effect are interchangeable – and are not what we mean when we say that a program causes a computer to win at chess, or that a domino remained standing because 641 is a prime.
There is no inconsistency in having multiple explanations of the same phenomenon, at different levels of emergence. Regarding microphysical explanations as more fundamental than emergent ones is arbitrary and fallacious. There is no escape from Hofstadter’s 641 argument, and no reason to want one. The world may or may not be as we wish it to be, and to reject good explanations on that account is to imprison oneself in parochial error. — page 117

{This is something that I have missed on my first two readings of the book. It is amazing how David spends 4 paragraphs to refute one of the most well-accepted ideas in intellectual circles only to move on to the next topic!}

It is helpful to revisit chapter 10 of The Fabric of Reality Handbook if existence of abstractions is still not obvious. TODO

FoR: 10.1.0 Do abstract entities, like numbers, exist? If so, in what way?15

FoR: 10.2.0 We never experience mathematical entities. No one ever saw a perfect circle, yet it is a clear concept in our mind. How do we obtain knowledge about it if no one ever experienced it?16

5.8.0 What is an example of we use abstractions to interact with physical phenomena and the other way around?

When we use theories about emergent physical quantities to explain the behaviour of water in a kettle, we are using an abstraction – an ‘idealized’ model of the kettle that ignores most of its details – as an approximation to a real physical system. But when we use a computer to investigate prime numbers, we are doing the reverse: we are using the physical computer as an approximation to an abstract one which perfectly models prime numbers. Unlike any real computer, the latter never goes wrong, requires no maintenance, and has unlimited memory and unlimited time to run its program. — page 119

If you don’t believe in existence or importance of abstract emergent phenomena then you have to explain to me how you navigate the world with its complexity of google’s of particles, quantum interference and so on. Emergent abstract phenomena is all we’ll ever experience, especially when taking into account what all observations of the world are just electrical currents in our brain.

Morality and Aesthetics

5.9.0 How this world-view plays in our understanding of morality and aesthetics?

Morality and aesthetics are abstractions just as numbers. We learn about them through the same way: conjecture and refutations. Our refutations of moral theories relies on explanations, which are usually (at least partially) based on real-life factual events (or on logic etc.) that can be right and wrong. Hence, our moral theories can be right and wrong.

In the case of moral philosophy, the empiricist and justificationist misconceptions are often expressed in the maxim that ‘you can’t derive an ought from an is’ (a paraphrase of a remark by the Enlightenment philosopher David Hume). It means that moral theories cannot be deduced from factual knowledge. This has become conventional wisdom, and has resulted in a kind of dogmatic despair about morality: ‘you can’t derive an ought from an is, therefore morality cannot be justified by reason’. That leaves only two options: either to embrace unreason or to try living without ever making a moral judgement. Both are liable to lead to morally wrong choices, just as embracing unreason or never attempting to explain the physical world leads to factually false theories (and not just ignorance).
Certainly you can’t derive an ought from an is, but you can’t derive a factual theory from an is either. That is not what science does. The growth of knowledge does not consist of finding ways to justify one’s beliefs. It consists of finding good explanations. And, although factual evidence and moral maxims are logically independent, factual and moral explanations are not. Thus factual knowledge can be useful in criticizing moral explanations. For example, in the nineteenth century, if an American slave had written a bestselling book, that event would not logically have ruled out the proposition ‘Negroes are intended by Providence to be slaves.’ No experience could, because that is a philosophical theory. But it might have ruined the explanation through which many people understood that proposition. And if, as a result, such people had found themselves unable to explain to their own satisfaction why it would be Providential if that author were to be forced back into slavery, then they might have questioned the account that they had formerly accepted of what a black person really is, and what a person in general is – and then a good person, a good society, and so on.
Conversely, advocates of highly immoral doctrines almost invariably believe associated factual falsehoods as well. For instance, ever since the attack on the United States on 11 September 2001, millions of people worldwide have believed it was carried out by the US government, or the Israeli secret service. Those are purely factual misconceptions, yet they bear the imprint of moral wrongness just as clearly as a fossil – made of purely inorganic material – bears the imprint of ancient life. And the link, in both cases, is explanation. To concoct a moral explanation for why Westerners deserve to be killed indiscriminately, one needs to explain factually that the West is not what it pretends to be – and that requires uncritical acceptance of conspiracy theories, denials of history, and so on.
Quite generally, in order to understand the moral landscape in terms of a given set of values, one needs to understand some facts as being a certain way too. And the converse is also true: for example, as the philosopher Jacob Bronowski pointed out, success at making factual, scientific discoveries entails a commitment to all sorts of values that are necessary for making progress. The individual scientist has to value truth, and good explanations, and be open to ideas and to change. The scientific community, and to some extent the civilization as a whole, has to value tolerance, integrity and openness of debate.
We should not be surprised at these connections. The truth has structural unity as well as logical consistency, and I guess that no true explanation is entirely disconnected from any other. Since the universe is explicable, it must be that morally right values are connected in this way with true factual theories, and morally wrong values with false theories. — page 120

Empiricism was wrong to assume that our senses provide theories. We use our senses to find problems by brining already-existing ideas into conflict.

🧬 6 — The Jump to Universality

We have studied the world of abstractions: from knowledge and math to morality. These are just theories like any else and they have reach. This chapter is about the reach of theories, and how some systems achieve a sudden jump to universal reach.

Chapter is divided into four parts:

What are universal systems? How they reach universality?
Are there hierarchies of universalities?
What is a computational universality?
What is an evolutionary universality?

Summary

Our systems, like language or numerals are theories of the world. They encode some regularity of nature. The deeper the regularity, the more the reach. Sometimes, systems combine multiple regularities and reach universality, this is the jump to universality.

Language got universality once it started using rules and digital systems like alphabet. Interestingly, systems that achieve universalities can have hierarchies depending on their efficiency of use. For instance tally marks system is universal, but it is less efficient than Hindu-Arabic numerals.

To achieve universality of systems we must encode in it some regularity of nature (i.e. knowledge), make it digital and don’t put arbitrary upper bounds. We must understand that abstractions exist independently of physical matter, but they can be instantiated in it.

Universality of computation is a powerful example that encoded profound regularity in nature — computation. Computation is about calculating from input an output by following specifically defined rules. There are computable and uncomputable numbers. Uncomputable numbers will never exist physically, but they exist in the abstract world. Universal computer is a machine that can simulate any physical process because all physical processes are computations. Your brain would be one of such, hence AI is possible.

Evolution seems to achieve some sort of universality with DNA. (Interestingly, it is also a digital system!) Yet, we can’t explain which regularity it has encoded and why it stopped there. This is a problem yet to be solved by science.

You can practice chapter questions as flashcards here.

Universalities: Language and Math

6.1.0 What is the jump to universality?

Be it writing system, numerals or computation, these are all our theories of the world. Theory can have reach in its domain. Writing system might be able to represent every possible word, it might not. It might do it elegantly and efficiently and not. All these are relevant factors of a system that define its reach and its efficiency. This is why we use some theories (systems) over others.

Theories capture some underlying regularity in nature, if it is profound, theory can have a universal reach. After some gradual improvements system usually jumps to universality sharply, it can also improve in efficiency once it achieves universalities — there are hierarchies of universalities.

The jump to universality The tendency of gradually improving systems to undergo a sudden large increase in functionality, becoming universal in some domain. — page 146

6.1 On definition of universality.
Universality doesn’t mean an infinite reach, for then there could not be an improvement in it. It means a broad or deep enough reach that we empirically start to consider ‘universal’.

6.2.0 What was the initial writing system?

Most languages are success stories of jumps to universality, but it wasn’t like this for a while. First writing systems used unique pictogram for each word. If one wanted to add a new word new pictogram had to be drawn and then spread to every speaker so they know its meaning. This is quite cumbersome.

6.2.1 How can we represent every possible word in language?

Having a unique dedicated picture doesn’t work out. Turns out creating rules gives far more reach. A rule might apply to every known and unknown word for a person.

no [pictogram] system ever came close to having a pictogram for every word in its spoken language. Why not?
Originally, there was no intention to do so. Writing was for specialized applications such as inventories and tax records. Later, new applications would require larger vocabularies, but by then scribes would increasingly have found it easier to add new rules to their writing system rather than new pictograms. For example, in some systems, if a word sounded like two or more other words in sequence, it could be represented by the pictograms for those words. If English were written in pictograms, that would allow us to write the word ‘treason’ as ‘🌲☀️”. — page 125

This system has bigger reach than pure pictogram system:

it brought words into the writing system that no one had explicitly added — page 126

6.2.2 Was it universal?

No!

However, the rule could not be applied in all cases: it could not represent any new single-syllable words, nor many other words. It seems clumsy and inadequate compared to modern writing systems. Yet there was already something significant about it which no purely pictographic system could achieve: it brought words into the writing system that no one had explicitly added. That means that it had reach. And reach always has an explanation. Just as in science a simple formula may summarize a mass of facts, so a simple, easily remembered rule can bring many additional words into a writing system, but only if it reflect an underlying regularity. The regularity in this case is that all the words in any given language are built out of only a few dozen ‘elementary sounds’, with each language using a different set chosen from the enormous range of sounds that the human voice can produce. — page 125

Every rule or theory tries to capture an underlying regularity of the world. The captured regularity determines the reach of the theory. Some regularities are so profound that they have a universal reach.

6.2.3 What alphabet has achieved?

An alphabet (which is a rule) captures a regularity that every possible (not just known*)* word can be broken down to a few elementary sounds that humans make. Turns out, this has a universal reach:

Universality achieved through rules has a different character from that of a completed list (such as the hypothetical complete set of pictograms). One difference is that the rules can be much simpler than the list. The individual symbols can be simpler too, because there are fewer of them. But there is more to it than that. Since a rule works by exploiting regularities in the language, it implicitly encodes those regularities, and so contains more knowledge than the list. An alphabet, for instance, contains knowledge of what words sound like. That allows it to be used by a foreigner to learn to speak the language, while pictograms could at most be used to learn to write it. Rules can also accommodate inflection such as prefixes and suffixe without adding complexity to the writing system, thus allowing written texts to encode more of the grammar of sentences. Also, a writing system based on an alphabet can cover not only every word but every possible word in its language, so that words that have yet to be coined already have a place in it. Then, instead of each new word temporarily breaking the system, the system can itself be used to coin new words, in an easy and decentralized way. — page 126

Adding on a few rules (encoding a few additional regularities of nature) can give universality to the system.

6.3.0 How numeral systems reveal hierarchies of universalities?

What is interesting with numerals is that humans quickly achieved universality by simple systems like tally marks. Yet, numerals reveal that there are hierarchies of universalities.

6.2 Applying hierarchies of universalities idea to languages.
What are the better (higher) universal systems for language than what we currently use?
What are the impracticalities of English that could be improved upon to produce a better universal system for language (similar to arithmetic and comparison in arabic vs roman numeral systems)?
Improving universal systems is about making system’s computations easier/ faster/ more tractable. We can achieve significant efficiency gains by using one system other another, and that is why there are a hierarchies of universal systems.
Is English the most efficient language for manipulations with information for humans? Obviously it’s not, but creating language from the scratch and making it world-wide adopted is as hard as it sounds, so it seems English won’t go anywhere. I found this playlist talking about constructed languages interesting.

Tally marks and roman numerals turned out to be impractical:

It is only with hindsight that we can regard tally marks as a system of numerals, known as the ‘unary’ system. As such, it is an impractical system. For instance, even the simplest operations on numbers represented by tally marks, such as comparing them, doing arithmetic, and even just copying them, involves repeating the entire tallying process. If you had forty goats, and sold twenty, and had tally-mark records of both those numbers, you would still have to perform twenty individual deletion operations to bring your record up to date. Similarly, checking whether two fairly close numerals were the same would involve tallying them against each other. So people began to improve the system. The earliest improvement may have been simply to group the tally marks – for instance, writing ~~⎥⎥⎥⎥~~ ~~⎥⎥⎥⎥~~ instead of ⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥. This made arithmetic and comparison easier, since one could tally whole groups and see at a glance that ~~⎥⎥⎥⎥~~ ~~⎥⎥⎥⎥~~ is different from ~~⎥⎥⎥⎥~~ ~~⎥⎥⎥⎥~~ ⎥. Later, such groups were themselves represented by shorthand symbols: the ancient Roman system used symbols like [some roman numerals I can’t copy] to represent one, five ten, fifty one hundred, five hundred, and one thousand. — page 128

6.3.1 What is the problem with tally marks type systems?

The problem with such systems is in its arithmetics. Since there is a highest possible number, summing up several of them would give us just a string which is no different from tally marks. For instance let’s say “X” is the highest numeral and it means 10. If we would want to represent 70 we would just have to write: “XXXXXXX”. Creating more symbols for higher numbers like 50, 100 and so on doesn’t solve the problem, only postpones it.

Just as one could upgrade the vocabulary of an ancient writing system by adding pictograms, so one could add symbols to a system of numerals to increase its range. And this was done. But the resulting system would still always have a highest-valued symbol, and hence would not be universal for doing arithmetic without tallying. — page 130

6.3.2 How do we solve the this problem so it doesn’t involve ‘tallying’?

The answer came from Hindu-Arabic numerals: 0123456789. The insight was that the value of a numeral (e.g. 2) depends on its position in the number (e.g. 204):

The only way to emancipate arithmetic from tallying is with rules of universal reach. As with alphabets, a small set of basic rules and symbols is sufficient The universal system in general use today has ten symbols, the digits 0 to 9, and its universality is due to a rule that the value of a digit depends on its position in the number. For instance, the digit 2 means two when written by itself, but means two hundred in the numeral 204. Such ‘positional’ systems require ‘placeholders’, such as the digit 0 in 204, whose only function is to place the 2 into the position where it means two hundred. — page 131

Many systems came close to being universal as Hindu-Arabic system was, but they didn’t. Archimedes in his research of astronomy had to deal with big numbers, so he invented his own numeral system. Yet, he put an upper limit to a highest possible number (10^800,000,000), hence he had the same arithmetic-tally problem as Roman numerals did.

6.4.0 It was a conscious choice of his: Why did he do that?

Ancient Greeks did not realize the independent existence of abstract entities! For them, they always had to be referred to some physical objects, hence, they never achieved universality!

Archimedes must have been aware that his method of extending a number system – which he used twice in succession – could be continued indefinitely But perhaps he doubted that the resulting numerals would refer to anything about which one could validly reason. Indeed, one motivation for that whole project was to contradict the idea – which was a truism at the time – that the grains of sand on a beach could literally not be numbered. So he used his system to calculate the number of grains of sand that would be needed to fill the entire celestial sphere. This suggests that he, and ancient Greek culture in general, may not have had the concept of an abstract number at all, so that, for them, numerals could refer only to objects – if only objects of the imagination. In that case universality would have been a difficult property to grasp, let alone to aspire to. Or maybe he merely felt that he had to avoid aspiring to infinite reach in order to make a convincing case. At any rate, although from our perspective Archimedes’ system repeatedly ‘tried’ to jump to universality, he apparently did not want it to. — page 133

6.2 Exploring independence of abstractions from physical things.
Abstractions exist independently of physical entities. We can achieve universality of abstract systems (like numerals) only when acknowledging it.
It then seems reasonable to assume that not only numerals, but all abstract systems exist independently of physical ones. The abstract phenomena don’t have to be physically instantiated to exist in ‘their own way’. For example, we know that prime numbers is an infinite set, and we have a physical instantiation of some specific highest known prime number at the moment. It is not the last one, and we know there is a higher one — it exists, we just don’t have a physical instantiation of it! (Similar example can be made with computable and uncomputable numbers!)
Taking this idea seriously we arrive at interesting conclusions. Morality, knowledge and aesthetics are abstract systems — they don’t have one singular physical instantiation. So, morality exists regardless of physical entities. This would imply that regardless the laws of physics, morality should stay the same! So, some laws of physics can be more or less moral!
This is what David wrote in fifth chapter:

This argument that abstractions really exist does not tell us what they exist as – for instance, which of them are purely emergent aspects of others, and which exist independently of the others. Would the laws of morality still be the same if the laws of physics were different? If they were such that knowledge could best be obtained by blind obedience to authority, then scientists would have to avoid what we think of as the values of scientific inquiry in order to make progress. My guess is that morality is more autonomous than that, and so it makes sense to say that such laws of physics would be immoral, and (as I remarked in Chapter 4) to imagine laws of physics that would be more moral than the real ones.
The reach of ideas into the world of abstractions is a property of the knowledge that they contain, not of the brain in which they may happen to be instantiated. A theory can have infinite reach even if the person who originated it is unaware that it does. However, a person is an abstraction too. And there is a kind of infinite reach that is unique to people: the reach of the ability to understand explanations. And this ability is itself an instance of the wider phenomenon of universality – to which I turn next. — page 123

Attributes of Universalities and Computation

6.5.0 What is the typical transition in using the system that happens once it becomes universal?

Before universality one has to make specialized objects for each case. Once system is universal you customize (specializes/ programs) some universal singular object. Universality is about creating an object with universal repertoire (like an alphabet or computer), and then customizing it for your purposes.

Here we see a transition that is typical of the jump to universality: before the jump, one has to make specialized objects for each document to be printed; after the jump, one customizes (or specializes, or programs) a universal object – in this case a printing press with movable type. Similarly, in 1801 Joseph Marie Jacquard invented a general-purpose silk-weaving machine now known as the Jacquard loom. Instead of having to control manually each row of stitches in each individual bolt of patterned silk, one could program an arbitrary pattern on punched cards which would instruct the machine to weave that pattern any number of times. — page 134

6.5.1 What is the other necessary attribute of a universal system?

Universal systems are usually digital because they have an error correction mechanism! This is why alphabet, numerals and human brain (with neuron firing or not) are all digital!

Another thing that they have in common is that they are all digital: they operate on information in the form of discrete values of physical variables, such as electronic switches being on or off, or cogs being at one of ten positions. The alternative, ‘analogue’, computers, such as slide rules, which represent information as continuous physical variables, were once ubiquitous but are hardly ever used today. That is because a modern digital computer can be programmed to imitate any of them, and to outperform them in almost any application. The jump to universality in digital computers has left analogue computation behind. That was inevitable, because there is no such thing as a universal analogue computer.
That is because of the need for error correction: during lengthy computations, the accumulation of errors due to things like imperfectly constructed components, thermal fluctuations and random outside influence makes analogue computers wander off the intended computational path. This may sound like a minor or parochial consideration. But it is quite the opposite. Without error-correction all information processing, and hence all knowledge-creation, is necessarily bounded. Error-correction is the beginning of infinity. — page 140

6.6.0 Explain why analogue computation couldn’t work.

It treats noise as an information, which accumulates over time leading off the computation. Running operation multiple times and taking its median wouldn’t work because that operation itself with comparison can be performed to a fixed level of accuracy.

For example, tallying is universal only if it is digital. Imagine that some ancient goatherds had tried to tally the total length of their floc instead of the number. As each goat left the enclosure, they could reel out some string of the same length as the goat. Later, when the goats returned, they could reel that length back in. When the whole length had been reeled back in, that would mean that all the goats had returned. But in practice the outcome would always be at least a little long or short, because of the accumulation of measurement errors. For any given accuracy of measurement, there would be a maximum number of goats that could be reliably tallied by this ‘analogue tallying’ system. The same would be true of all arithmetic performed with those ‘tallies’. Whenever the strings representing several flock were added together, or a string was cut in two to record the splitting of a flock and whenever a string was ‘copied’ by making another of the same length, there would be errors. One could mitigate their effect by performing each operation many times, and then keeping only the outcome of median length. But the operations of comparing or duplicating lengths can themselves be performed only with finite accuracy, and so could not reduce the rate of error accumulation per step below that level of accuracy. That would impose a maximum number of consecutive operations that could be performed before the result became useless for a given purpose – which is why analogue computation can never be universal.
What is needed is a system that takes for granted that errors will occur, but corrects them once they do – a case of ‘problems are inevitable, but they are soluble’ at the lowest level of information-processing emergence. But, in analogue computation, error correction runs into the basic logical problem that there is no way of distinguishing an erroneous value from a correct one at sight, because it is in the very nature of analogue computation that every value could be correct. Any length of string might be the right length. — page 140

6.7.0 What is computation universality (i.e. Turing completeness)?

Turing completeness is a universality for computers — it can calculate any number that is computable (i.e. physically possible). This means, that such computer can arbitrarily well simulate any physical process:

Neither they nor anyone else for over a century afterwards imagined today’s most common uses of computation, such as the internet, word processing, database searching, and games. But another important application that they did foresee was making scientific predictions. The Analytical Engine would be a universal simulator – able to predict the behaviour, to any desired accuracy, of any physical object, given the relevant laws of physics. This is the universality that I mentioned in Chapter 3, through which physical objects that are unlike each other and dominated by different laws of physics (such as brains and quasars) can exhibit the same mathematical relationships. — page 136

6.7.1 Would such computer be able to simulate human intelligence?

Yes it would! Our brains follow physical processes, hence, given the right algorithm it could simulate it:

Babbage and Lovelace also thought about one application of universal computers that has not been achieved to this day, namely so-called artificial intelligence (AI). Since human brains are physical objects obeying the laws of physics, and since the Analytical Engine is a universal simulator, it could be programmed to think, in every sense that humans can (albeit very slowly and requiring an impractically vast number of punched cards). Nevertheless, Babbage and Lovelace denied that it could. Lovelace argued that ‘The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.’
The mathematician and computer pioneer Alan Turing later called this mistake ‘Lady Lovelace’s objection’. It was not computational universality that Lovelace failed to appreciate, but the universality of the laws of physics. — page 137

Philosophers like John Searle objects to the idea that brain could be simulated on a computer:

the philosopher John Searle has placed the AI project in the following historical perspective: for centuries, some people have tried to explain the mind in mechanical terms, using similes and metaphors based on the most complex machines of the day. First the brain was supposed to be like an immensely complicated set of gears and levers. Then it was hydraulic pipes, then steam engines, then telephone exchanges – and, now that computers are our most impressive technology, brains are said to be computers. But this is still no more than a metaphor, says Searle, and there is no more reason to expect the brain to be a computer than a steam engine. — page 138

6.8.0 What is the counterargument that David provides?

If we can achieve computational universality with hydraulic pipes then human brain can be simulated on them! People who object fail to understand that computational universality is inherently about the laws of physics, about what is computable and non-computable in nature. (Computation can be defined as arriving from input to some output by following a number of specific rules. Non-computable means that there are no steps we can perform to get some output!)

But there is. A steam engine is not a universal simulator. But a computer is, so expecting it to be able to do whatever neurons can is not a metaphor: it is a known and proven property of the laws of physics as best we know them. (And, as it happens, hydraulic pipes could also be made into a universal classical computer, and so could gears and levers, as Babbage showed.) — page 138

David then add on computational universality:

improvements led to a jump to universality in about 1970, when several companies independently produced a microprocessor, a universal classical computer on a single silicon chip. From then on, designers of any information-processing device could start with a microprocessor and then customize it – program it – to perform the specific tasks needed for that device. Today, your washing machine is almost certainly controlled by a computer that could be programmed to do astrophysics or word processing instead, if it were given suitable input–output devices and enough memory to hold the necessary data.
It is a remarkable fact that, in that sense (that is to say, ignoring issues of speed, memory capacity and input–output devices), the human ‘computers’ of old, the steam-powered Analytical Engine with its literal bells and whistles, the room-sized vacuum-tube computers of the Second World War, and present-day supercomputers all have an dentical repertoire of computations. — page 139

Evolutionary Universality

6.9.0 How can we apply universal system idea to the evolution?

It is not exact, but it is fair to say that at the start evolution has formed a stable language for living organisms. At first it was RNA, which then switched to DNA, both are digital and have phenomenal reach which is far beyond the environment it has originally evolved in!

[David describes initial evolution process which I’ll skip for brevity.] Gradually, the ability of these catalysts to promote their own production became robust and specific enough for it to be worth calling them replicators. Evolution produced replicators that caused themselves to be replicated ever faster and more reliably.
Different replicators began to join forces in groups, each of whose members specialized in causing one part of a complex web of chemical reactions whose net effect was to construct more copies of the entire group. Such a group was a rudimentary organism. At that point, life was at a stage roughly analogous to that of non-universal printing, or Roman numerals: it was no longer a case of each replicator for itself, but there was still no universal system being customized or programmed to produce specific substances.
The most successful replicators may have been RNA molecules. They have catalytic properties of their own, depending on the precise sequence of their constituent molecules (or bases, which are similar to those of DNA). As a result, the replication process became ever less like straightforward catalysis and ever more like programming – in a language, or genetic code, that used bases as its alphabet.
Genes are replicators that can be interpreted as instructions in a genetic code. Genomes are groups of genes that are dependent on each other for replication. The process of copying a genome is called a living organism. Thus the genetic code is also a language for specifying organisms. At some point, the system switched to replicators made of DNA, which is more stable than RNA and therefore more suitable for storing large amounts of information. — page 143

6.9.1 What has happened next?

As language of living organisms (DNA) has achieved ‘universality’ it stopped evolving, but organisms continued to do so.

The familiarity of what happened next can obscure how remarkable and mysterious it is. Initially, the genetic code and the mechanism that interpreted it were both evolving along with everything else in the organisms. But there came a moment when the code stopped evolving yet the organisms continued to do so. At that moment the system was coding for nothing more complex than primitive, single-celled creatures. Yet virtually all subsequent organisms on Earth, to this day, have not only been based on DNA replicators but have used exactly the same alphabet of bases, grouped into three-base ‘words’, with only small variations in the meanings of those ‘words’.
That means that, considered as a language for specifying organisms, the genetic code has displayed phenomenal reach. It evolved only to specify organisms with no nervous systems, no ability to move or exert forces, no internal organs and no sense organs, whose lifestyle consisted of little more than synthesizing their own structural constituents and then dividing in two. And yet the same language today specified the hardware and software for countless multicellular behaviours that had no close analogue in those organisms, such as running and flying and breathing and mating and recognizing predators and prey. It also specified engineering structures such as wings and teeth, and nanotechnology such as immune systems, and even a brain that is capable of explaining quasars, designing other organisms from scratch, and wondering why it exists.
During the entire evolution of the genetic code, it was displaying far less reach. It may be that each successive variant of it was used to specify only a few species that were very similar to each other. At any rate, it must have been a frequent occurrence that a species embodying new knowledge was specified in a new variant of the genetic code. But then the evolution stopped, at a point when it had already attained enormous reach. Why? It looks like a jump to some sort of universality, does it not? — page 144

6.9.2 Has it achieved universality? What is the explanation of its reach?

We are not sure if it has achieved universality, there must be an explanation reach and we are yet to have one.

What happened next followed the same sad pattern that I have described in other stories of universality: for well over a billion years after the system had reached universality and stopped evolving, it was still only being used to make bacteria. …
Reach always has an explanation. But this time, to the best of my knowledge, the explanation is not yet known. If the reason for the jump in reach was that it was a jump to universality, what was the universality? The genetic code is presumably not universal for specifying life forms, since it relies on specific types of chemicals, such as proteins. Could it be a universal constructor? Perhaps. It does manage to build with inorganic materials sometimes, such as the calcium phosphate in bones, or the magnetite in the navigation system inside a pigeon’s brain. Biotechnologists are already using it to manufacture hydrogen and to extract uranium from seawater. It can also program organisms to perform constructions outside their bodies: birds build nests; beavers build dams. Perhaps it would it be possible to specify, in the genetic code, an organism whose life cycle includes building a nuclear-powered spaceship. Or perhaps not. I guess it has some lesser, and not yet understood, universality. — page 145

👾 7 — Artificial Creativity

We touched upon knowledge creation, evolution and computation. Specifically, we have talked about how computers can simulate human brain. Why then, we haven’t done it yet? This problem turns out to be harder than expected. It also doesn’t help that most AI researchers, just as most physicists, have been led astray by bad philosophy.

This chapter’s main questions are:

What is the Turing test and its mistake?
How could we know whether something is AI or not?
What is behaviorism? How it misled AI researchers for years?
What is an artificial evolution?

Summary

We know from universality of computation that it is possible to simulate human brain: we can create an intelligent machine that is just like humans. Turing test was the first attempt to test whether a machine is intelligent or not. If machine could fool you into believing it is a human, than it was intelligent. This is a bad test because it is empirical.

To know whether machine is intelligent or not we would have to rely on good explanations. AI would have to create its own knowledge. We will rely on explanations to judge the source of created knowledge: is it programmers or the machine itself.

Qualia, consciousness and other attributes of intelligence are barriers to creating an AI. We first have to solve them only then to encode it in a machine. If we can’t program it, then we haven’t understood it.

Behaviorism is akin to instrumentalism and has misled researchers for years. Mimicking intelligence is not the same as being intelligent. Programmers giving the knowledge is not the same as a program creating it itself. Even in utilitarian terms all the value lies in the second one.

Artificial evolution suffers from the same problem: its knowledge is created by the programmers, not the program itself. David proposes an experiment to tell whether artificial evolution has actually worked or not.

{David uses “AI” which we nowadays refer to as AGI — a machine that is conscious and intelligent, just like humans.}

You can practice chapter questions as flashcards here.

Turing Test and its Mistake

7.1.0 What is a universal computer (i.e. Turing completeness)? How is it related to AI?

Turing was one of the first ones to conceive of such thing as computation. Computation is deriving outputs from inputs by following some specific rules. He then explained how one can create a set of computable and uncomputable numbers from this idea. (Some numbers can be found by following some steps, others not.)

Computation is closely related to physics, uncomputable numbers are simply physically impossible — there is no steps one can take to get them. From such definition of computation one can think of our universe as a computation — it calculates from the Big Bang (input) and the laws of physics (set of rules) its end (output).

Turing then went on to prove that one can make a computer that could, given infinite time and memory, calculate any computable number. Now, numbers are just abstractions, they can be made to represent anything one likes. So let’s apply them to the universe: there are computable states of universe which we can arrive at by follow some steps. Turing has proved that there is a machine that can arrive at every possible state given the right steps to follow.

Hence, this computer can simulate any physical state. It is a universal computer that can calculate all computable numbers, and hence, arrive at all physically possible states.

Now, when taking this idea seriously, it is obvious that such computer could simulate human brain because it is just a physical process. It is physically possible, so it could arrive at it. The problem is that we don’t know which steps to give to the computer because we don’t understand human brain well enough.

Eventually we will. This is a problem that science is yet to solve. It is clearly physically possible, we just don’t know how. (Yet!).

People tried to create programs that simulate humans and called them AI (artificial intelligence).

7.2.0 What is a Turing test?

Turing designed wanted to test whether machines are intelligent or not (like humans are). You empirically talk to a program, and if you can’t tell whether it’s a human or not, program passes the test.

In 1950, in a paper entitled ‘Computing Machinery and Intelligence’, he famously addressed the question: can a machine think? Not only did he defend the proposition that it can, on the grounds of universality, he also proposed a test for whether a program had achieved it. Now known as the Turing test, it is simply that a suitable (human) judge be unable to tell whether the program is human or not. In that paper and subsequently, Turing sketched protocols for carrying out his test. For instance, he suggested that both the program and a genuine human should separately interact with the judge via some purely textual medium such as a teleprinter, so that only the thinking abilities of the candidates would be tested, not their appearance. — page 148

7.2.1 What are Eliza-type programs?

In 1964 the computer scientist Joseph Weizenbaum wrote a program called Eliza, designed to imitate a psychotherapist. He deemed psychotherapists to be an especially easy type of human to imitate because the program could then give opaque answers about itself, and only ask questions based on the user’s own questions and statements. It was a remarkably simple program. Nowadays such programs are popular projects for students of programming, because they are fun and easy to write. A typical one has two basic strategies. First it scans the input for certain keywords and grammatical forms. If this is successful, it replies based on a template, filling in the blanks using words in the input. For instance, given the input I hate my job, the program might recognize the grammar of the sentence, involving a possessive pronoun ‘my’, and might also recognize ‘hate’ as a keyword from a built-in list such as ‘love/hate/like/dislike/want’, in which case it could choose a suitable template and reply: What do you hate If it cannot parse the input to that extent, most about your job? it asks a question of its own, choosing randomly from a stock pattern which may or may not depend on the input sentence. For instance, if asked How does a television work?, it might reply, What is so interesting about “How does a television work?”? Or it might just ask, Why does that interest you? Another strategy, used by recent internet-based versions of Eliza, is to build up a database of previous conversations, enabling the program simply to repeat phrases that other users have typed in, again choosing them according to keywords found in the current user’s input.
Weizenbaum was shocked that many people using Eliza were fooled by it. So it had passed the Turing test – at least, in its most naive version. …
Programs written today – a further twenty-six years later – are still no better at the task of seeming to think than Eliza was. They are now known as ‘chatbots’, and their main application is still amusement, both directly and in computer games. They have also been used to provide friendly seeming interfaces to lists of ‘frequently asked questions’ about subjects like how to operate computers. But I think that users fin them no more helpful than a searchable list of the questions and answers. — page 148 and 150

7.3.0 What is the mistake of Turing test?

Turing invented his test in the hope of bypassing all those philosophical problems. In other words, he hoped that the functionality could be achieved before it was explained. Unfortunately it is very rare for practical solutions to fundamental problems to be discovered without any explanation of why they work.
Nevertheless, rather like empiricism, which it resembles, the idea of the Turing test has played a valuable role. It has provided a focus for explaining the significant of universality and for criticizing the ancient, anthropocentric assumptions that would rule out the possibility of AI. Turing himself systematically refuted all the classic objections in that seminal paper (and some absurd ones for good measure). But his test is rooted in the empiricist mistake of seeking a purely behavioural criterion: it requires the judge to come to a conclusion without any explanation of how the candidate AI is supposed to work. — page 154

7.3.1 How could we ever know whether something is an AI or not?

We can judge only be being presented with a good explanation. The knowledge inside the machine should be created by it. If we can explain why it isn’t, then it is not an AI.

Chess program is not intelligent because humans have put all the knowledge seeds in it (by writing programs and so on). The same way, program that mimics humans but had all its knowledge originated from programmers (like a chatbot) is not intelligent.

Artificial intelligence must create its own knowledge, just like the humans do, and we could only judge that through good explanations.

judging whether something is a genuine AI will always depend on explanations of how it works.
That is because the task of the judge in a Turing test has similar logic to that faced by Paley when walking across his heath and finding a stone, a watch or a living organism: it is to explain how the observable features of the object came about. In the case of the Turing test, we deliberately ignore the issue of how the knowledge to design the object was created. The test is only about who designed the AI’s utterances: who adapted its utterances to be meaningful – who created the knowledge in them? If it was the designer, then the program is not an AI. If it was the program itself, then it is an AI. …
When testing an AI, we are hoping to find a hard-to-vary explanation to the effect that its utterances cannot come from any human but only from the AI. …
Without a good explanation of how an entity’s utterances were created, observing them tells us nothing about that. In the Turing test, at the simplest level, we need to be convinced that the utterances are not being directly composed by a human masquerading as the AI, as in the Hofstadter hoax. But the possibility of a hoax is the least of it. For instance, I guessed above that Elbot had recited a stock joke in response to mistakenly recognizing the keyword ‘spouse’. But the joke would have quite a different significant if we knew that it was not a stock joke – because no such joke had ever been encoded into the program.
How could we know that? Only from a good explanation. For instance, we might know it because we ourselves wrote the program. Another way would be for the author of the program to explain to us how it works – how it creates knowledge, including jokes. If the explanation was good, we should know that the program was an AI. In fact, if we had only such an explanation but had not yet seen any output from the program – and even if it had not been written yet – we should still conclude that it was a genuine AI program. So there would be no need for a Turing test. That is why I said that if lack of computer power were the only thing preventing the achievement of AI, there would be no need to wait. — page 155

Qualia and Consciousness

7.4.0 What is qualia and its problem?

We can’t encode subjective properties of sensations.

qualia (singular quale, which rhymes with ‘baa-lay’) – meaning the subjective aspect of sensations. So for instance the sensation of seeing the colour blue is a quale. Consider the following thought experiment. You are a biochemist with the misfortune to have been born with a genetic defect that disables the blue receptors in your retinas. Consequently you have a form of colour blindness in which you are able to see only red and green, and mixtures of the two such as yellow, but anything purely blue also looks to you like one of those mixtures. Then you discover a cure that will cause your blue receptors to start working. Before administering the cure to yourself, you can confidently make certain predictions about what will happen if it works. One of them is that, when you hold up a blue card as a test, you will see a colour that you have never seen before. You can predict that you will call it ‘blue’, because you already know what the colour of the card is called (and can already check which colour it is with a spectrophotometer). You can also predict that when you first see a clear daytime sky after being cured you will experience a similar quale to that of seeing the blue card. But there is one thing that neither you nor anyone else could predict about the outcome of this experiment, and that is: what blue will look like. Qualia are currently neither describable nor predictable – a unique property that should make them deeply problematic to anyone with a scientific world view (though, in the event, it seems to be mainly philosophers who worry about it). …
the original problem of qualia again: we seem to have them; it seems impossible to describe what they seem to be. — page 153 and 154

7.4.1 Can self-awareness be one of the defining characteristics for distinguishing intelligence?

some abilities of humans that are commonly included in that constellation associated with general-purpose intelligence do not belong in it. One of them is self-awareness – as evidenced by such tests as recognizing oneself in a mirror. Some people are unaccountably impressed when various animals are shown to have that ability. But there is nothing mysterious about it: a simple pattern-recognition program would confer it on a computer. The same is true of tool use, the use of language for signalling (though not for conversation in the Turing-test sense), and various emotional responses (though not the associated qualia). At the present state of the field a useful rule of thumb is: if it can already be programmed, it has nothing to do with intelligence in Turing’s sense. — page 154

7.5.0 What is David’s criterion for judging someone’s understanding (especially of consciousness)?

If you claim to have understood consciousness you must be able to program it. Computer wise we have all the tools needed: required speed, memory and so on. The only missing part is the program itself, which can only be created once we understand consciousness.

I have settled on a simple test for judging [anyone’s] claims, including Dennett’s, to have explained the nature of consciousness (or any other computational task): if you can’t program it, you haven’t understood it. — page 154

Behaviorism and Artificial Evolution

7.6.0 What is behaviorism and how it applies to AI?

Behaviorism is instrumentalism that is applied to psychology — we disregard whatever happens in the mind and just observe external stimuli and responses of people.

Same idea could be applied to AIs: we disregard the inner workings of the program and just judge its external behavior.

Becoming better at pretending to think is not the same as coming closer to being able to think.
There is a philosophy whose basic tenet is that those are the same. It is called behaviourism – which is instrumentalism applied to psychology. In other words, it is the doctrine that psychology can only, or should only, be the science of behaviour, not of minds; that it can only measure and predict relationships between people’s external circumstances (‘stimuli’) and their observed behaviours (‘responses’). The latter is, unfortunately, exactly how the Turing test asks the judge to regard a candidate AI. Hence it encouraged the attitude that if a program could fake AI well enough, one would have achieved it. But ultimately a non-AI program cannot fake AI. The path to AI cannot be through ever better tricks for making chatbots more convincing.
A behaviourist would no doubt ask: what exactly is the difference between giving a chatbot a very rich repertoire of tricks, templates and databases and giving it AI abilities? What is an AI program, other than a collection of such tricks? — page 157

7.6.1 What is the criticism of behaviorism? How is it similar to Lamarckism?

There is a difference in the program that does certain tricks because humans gave it the knowledge to do so, and machine that itself created such knowledge. The second one is a true AI because it genuinely creates knowledge.

Similar to Lamarckism, the knowledge that is already in the animal is not the same as the one that gets genuinely created (evolved).

When discussing Lamarckism in Chapter 4, I pointed out the fundamental difference between a muscle becoming stronger in an individual’s lifetime and muscles evolving to become stronger. For the former, the knowledge to achieve all the available muscle strengths must already be present in the individual’s genes before the sequence of changes begins. (And so must the knowledge of how to recognize the circumstances under which to make the changes.) This is exactly the analogue of a ‘trick’ that a programmer has built into a chatbot: the chatbot responds ‘as though’ it had created some of the knowledge while composing its response, but in fact all the knowledge was created earlier and elsewhere. The analogue of evolutionary change in a species is creative thought in a person. The analogue of the idea that AI could be achieved by an accumulation of chatbot tricks is Lamarckism, the theory that new adaptations could be explained by changes that are in reality just a manifestation of existing knowledge. — page 158

7.6.2 What if one is just interested solving some problem, does the source of knowledge matter?

One might argue that use-cases are the same: at the end of the day you care about solving some problem, ‘the how’ is secondary. But that is to miss the point. Once we create true AI (one that creates its own knowledge), then it would quickly exceed ‘use-cases’ you created it for, it would be a mile more useful than any narrow problem-solving program.

Our criticism of behaviorism (which is just instrumentalism) for AI is similar to oracle’s counterargument from The Fabric of Reality: both misunderstand the value allocation. If someone is just interesting in the utilitarian value of the program (AI or not), they should still aim to create AI. For AI use-cases would be universal, they won’t be parochial problem-solving.

FoR: 1.4.0 What is instrumentalism?17

7.7.0 What is artificial evolution? What is the student-robot example David gives?

A method of trying to simulate evolution artificially.

For example, suppose that you are a graduate student in robotics, hoping to build a robot that walks on legs better than previous robots do. The first phase of the solution must involve inspiration – that is to say, creative thought, attempting to improve upon previous researchers’ attempts to solve the same problem. You will start from that, and from existing ideas about other problems that you conjecture may be related, and from the designs of walking animals in nature. All of that constitutes existing knowledge, which you will vary and combine in new ways, and then subject to criticism and further variation. Eventually you will have created a design for the hardware of your new robot: its legs with their levers, joints, tendons and motors; its body, which will hold the power supply; its sense organs, through which it will receive the feedback that will allow it to control those limbs effectively; and the computer that will exercise that control. You will have adapted everything in that design as best you can to the purpose of walking, except the program in the computer.
The function of that program will be to recognize situations such as the robot beginning to topple over, or obstacles in its path, and to calculate the appropriate action and to take it. This is the hardest part of your research project. How does one recognize when it is best to avoid an obstacle to the left or to the right, or jump over it or kick it aside or ignore it, or lengthen one’s stride to avoid stepping on it – or judge it impassable and turn back? And, in all those cases, how does one specifically do those things in terms of sending countless signals to the motors and the gears, as modified by feedback from the senses? You will break the problem down into sub-problems. Veering by a given angle is similar to veering by a different angle. That allows you to write a subroutine for veering that takes care of that whole continuum of possible cases. Once you have written it, all other parts of the program need only call it whenever they decide that veering is required, and so they do not have to contain any knowledge about the messy details of what it takes to veer. When you have identified and solved as many of these sub-problems as you can, you will have created a code, or language, that is highly adapted to making statements about how your robot should walk. Each call of one of its subroutines is a statement or command in that language.
So far, most of what you have done comes under the heading of ‘inspiration’: it required creative thought. But now perspiration looms. Once you have automated everything that you know how to automate, you have no choice but to resort to some sort of trial and error to achieve any additional functionality. …
you can delegate the perspiration to a computer, but using a so-called evolutionary algorithm. Using the same computer simulation, you run many trials, each with a slight random variation of that first program. The evolutionary algorithm subjects each simulated robot automatically to a battery of tests that you have provided – how far it can walk without falling over, how well it copes with obstacles and rough terrain, and so on. At the end of each run, the program that performed best is retained, and the rest are discarded. Then many variants of that program are created, and the process is repeated. After thousands of iterations of this ‘evolutionary’ process, you may find that your robot walks quite well, according to the criteria you have set. You can now write your thesis. Not only can you claim to have achieved a robot that walks with a required degree of skill, you can claim to have implemented evolution on a computer. — page 159

7.1 Note on how much we understand evolution.
As David said: if you can’t program it, you don’t understand it. We can’t program or simulate evolutionary process. Specifically, we don’t understand how geochemistry becomes biochemistry. We can run experiment like Miller-Urey and get amino acids (which are basis for proteins, and proteins are basis of life), but this is not enough.
Amino acids is like a random pile of bricks, which somehow becomes a Sydney Opera House (living organisms). We still have no clue of how this transition happens. This is great, because this a problem for science to solve!
If we could properly simulate evolutionary process, then we could create AI by it.

7.7.1 What is the criticism of artificial evolution?

It suffers from the similar problem: the source of knowledge within the program has been encoded by humans previously. Even if program shows some behaviors that are ‘unique’ (have not been hard-coded), they are just acting within the limited reach that programmers gave to it. For artificial evolution to work, it must happen without knowledge of programmers.

This sort of thing has been done successfully many times. It is a useful technique. It certainly constitutes ‘evolution’ in the sense of alternating variation and selection. But is it evolution in the more important sense of the creation of knowledge by variation and selection? This will be achieved one day, but I doubt that it has been yet, for the same reason that I doubt that chatbots are intelligent, even slightly. The reason is that there is a much more obvious explanation of their abilities, namely the creativity of the programmer.
The task of ruling out the possibility that the knowledge was created by the programmer in the case of ‘artificial evolution’ has the same logic as checking that a program is an AI – but harder, because the amount of knowledge that the ‘evolution’ purportedly creates is vastly less. Even if you yourself are the programmer, you are in no position to judge whether you created that relatively small amount of knowledge or not. For one thing, some of the knowledge that you packed into that language during those many months of design will have reach, because it encoded some general truths about the laws of geometry, mechanics and so on. For another, when designing the language you had constantly in mind what sorts of abilities it would eventually be used to express.
The Turing-test idea makes us think that, if it is given enough standard reply templates, an Eliza program will automatically be creating knowledge; artificial evolution makes us think that if we have variation and selection, then evolution (of adaptations) will automatically happen. But neither is necessarily so. In both cases, another possibility is that no knowledge at all will be created during the running of the program, only during its development by the programmer. — page 160

David’s position seems very critical and defensive, is there something that would make him say that he is in the presence of AI?

7.8.0 What is the counterexample to Turing’s test that David provides for the student-robot example?

I would like to see an experiment of a slightly different kind: eliminate the graduate student from the project. Then, instead of using a robot designed to evolve better ways of walking, use a robot that is already in use in some real-life application and happens to be capable of walking. And then, instead of creating a special language of subroutines in which to express conjectures about how to walk, just replace its existing program, in its existing microprocessor, by random numbers. For mutations, use errors of the type that happen anyway in such processors (though in the simulation you are allowed to make them happen as often as you like). The purpose of all that is to eliminate the possibility that human knowledge is being fed into the design of the system, and that its reach is being mistaken for the product of evolution. Then, run simulations of that mutating system in the usual way. As many as you like. If the robot ever walks better than it did originally, then I am mistaken. If it continues to improve after that, then I am very much mistaken.
One of the main features of the above experiment, which is lacking in the usual way of doing artificial evolution, is that, for it to work, the language (of subroutines) would have to evolve along with the adaptations that it was expressing. This is what was happening in the biosphere before that jump to universality that finally settled on the DNA genetic code. As I said, it may be that all those previous genetic codes were only capable of coding for a small number of organisms that were all rather similar. And that the overwhelmingly rich biosphere that we see around us, created by randomly varying genes while leaving the language unchanged, is something that became possible only after that jump. We do not even know what kind of universality was created there. So why should we expect our artificial evolution to work without it? — page 161

As you can see, David’s judgement of whether something is AI or not relies on an explanation of how the machine works. He designed an experiment that would make sure that any created knowledge comes from the machine, not humans.

♾️ 8 — A Window on Infinity

We have talked about ideas that are all closely related to infinities: reach of explanations, abstractions, computation and so on. Yet, we haven’t studied the infinities themselves. This chapter is about to fix it.

It focuses on several topics:

What are the properties of infinities? How do they work? What is Cantor’s argument?
What is the paradox of Zeno? What was its mistake?
How physics impacts what can be computed? How it impacts what can be proved in mathematics?
What all those physical limitations imply? What is the main limit of the knowledge-creation?

Summary

Infinities are unintuitive. Every its number is extremely close to beginning. Some infinities are larger than others. If we can create an algorithm to list every number of it, it is countable. Otherwise, it is an uncountable which is bigger than the countable infinities. Probabilities seem to change depending on how we ‘shuffle’ the infinities, hence, they don’t work. This is a refutation of the anthropic argument for solving the fine tuning problem: depending on how you present the infinity of universes with different laws of physics astrophysicists would be more or less likely.

Paradox of Zeno is no paradox at all. It is a mistake of confusing an abstract mathematical infinity with the real physical one. These are two different things that are interlinked, but exist independently. Immanuel Kant made a similar mistake of applying abstract geometry to the physical world and trying to derive some conclusions from it.

Through the window of the physical world we can observe the abstract one. Laws of physics determine what is computed or not. Some mathematical functions seem simple to us only because our laws of physics are predisposed to it. Mathematical proof is just a computation: you derive output from input by following some specific rules. Computable and uncomputable things are defined by physics, and hence, what can be proved and not in mathematics is the question of physics. But it should not undermine the existence of uncomputable things — they still exist abstractly, just not physically. Mathematical ‘proof theory’ is a specific science, and its name is computer science.

Problems that are severely constrained by the laws of physics are usually too abstract and thus, uninteresting. Physics doesn’t put severe limitations on our exploration of the world. One can understand something without having a robust proof or computation to present. Knowledge creation is about creative thought and criticism, to claim its about proof is to make a reductive mistake.

You can practice chapter questions as flashcards here.

Misconceptions about Infinities

In the book David describes an infinity hotel to explain some ideas about infinities. Instead of describing it I will focus on the ideas themselves.

8.1.0 What is the idea of the ‘beginning of infinity’?

Infinities are universal, they are not parochial, they don’t have bounds. People like to reject them and create arbitrary limits, but this is irrational. Normalcy should not be the criterion for judging ideas, reason should.

The ‘beginning of infinity – the possibility of the unlimited growth of knowledge in the future – depends on a number of other infinities. One of them is the universality in the laws of nature which allows finite local symbols to apply to the whole of time and space – and to all phenomena and all possible phenomena. Another is the existence of physical objects that are universal explainers – people – which, it turns out, are necessarily universal constructors as well, and must contain universal classical computers. …
the beginning of infinity can be described either as a condition where ‘progress in the future will be unbounded’ or as the condition where ‘an infinite amount of progress will be made’. — page 164

David adds, that in a hotel with infinite number of rooms, every room is very close to its beginning. This leads to an interesting idea: We are extremely lucky compared to our ancestors and extremely unlucky compared to our descendants.

Every room is at the beginning of infinity. That is one of the attributes of the unbounded growth of knowledge too: we are only just scratching the surface, and shall never be doing anything else.
So there is no such thing as a typical room number at Infinity Hotel. Every room number is untypically close to the beginning. — page 175

8.2.0 What is the defining property of an infinite set?

In mathematics, infinity is studied via infinite sets (meaning sets with infinitely many members). The defining property of an infinite set is that some part of it has as many elements as the whole thing. For instance, think of the natural numbers:
In the upper line in the illustration, every natural number appears exactly once. The lower line contains only part of that set: the natural numbers starting at 2. The illustration tallies the two sets – mathematicians call it a ‘one-to-one correspondence’ – to prove that there are equally many numbers in each. — page 167

8.3.0 Are some infinities larger than others?

Yes! There are countable and uncountable infinities, second ones are bigger. Explanation of why is known as Cantor’s argument.

I will use the explanation and cards created for The Fabric of Reality. There, the question was:

Given that we can create a virtual reality machine that could simulate any physically possible environment and it has infinite memory and time to run them.

Could this machine in principle have every logically possible environment on its memory? Would its repertoire be the set of all logically possible environments?

The answer would be an astounding no, and it is due to the Cantor’s argument. All logically possible environments is an uncountable infinity (enumerable). Whatever we put on computers memory, even infinite memory, would still be written down, which would make it countable (numerable) infinity. Uncountable infinities are larger than countable ones.

FoR: 6.2.1 What is Cantor’s argument?18

8.4.0 What is an infinite regress?

The fallacious idea of delegating all one’s work to other staff in higher-numbered rooms is called an infinite regress. It is one of the things that one cannot validly do with infinity. There is an old joke about the heckler who interrupts an astrophysics lecture to insist that the Earth is flat and supported on the back of elephants standing on a giant turtle. ‘What supports the turtle?’ asks the lecturer. ‘Another turtle.’ ‘What supports that turtle?’ ‘You can’t fool me,’ replies the heckler triumphantly: ‘it’s turtles from there on down.’ That theory is a bad explanation not because it fails to explain everything (no theory does), but because what it leaves unexplained is effectively the same as what it purports to explain in the first place. (The theory that the designer of the biosphere was designed by another designer, and so on ad infinitum is another example of an infinite regress.) — page 173

David gives a definition at the end of the chapter:

Infinite regress A fallacy in which an argument or explanation depends on a sub-argument of the same form which purports to address essentially the same problem as the original argument. — page 193

8.5.0 How probabilites work in infinite sets?

They don’t! It would be wrong to apply any probabilistic thinking to infinities. Depending on how we ‘shuffle’ the numbers in an infinite set similar events might seem to become either very unlikely, or very likely. But this ‘seeming’ as likely or unlikely is meaningless — probabilistic thinking doesn’t work with infinities.

Consider an infinity hotel with infinite number of rooms. Imagine you distribute David’s old book to every millionth room, and a new one to all the rest.

What book of two would any guest expect to receive? What if all the old books have to move to odd-numbered rooms and new books to the even ones?

The number of books didn’t change (it stayed infinite), but now the likelihoods of receiving receiving one or the other seem to have changed.

Suppose that you are a guest at the hotel. A book – gift-wrapped in opaque paper – appears in your room’s delivery chute. You are hoping that it will be the newer book, because you have already read the old one. You are fairly confident that it will be, because, after all, what are the chances that your room is one of those that receive the old book? Exactly one in a million, it seems.
But, before you have a chance to open the package, there is an announcement. Everyone is to change rooms, to a number designated on a card that will come through the chute. The announcement also mentions that the new allocation will move all the recipients of one of the books to odd-numbered rooms, and the recipients of the other book to even-numbered ones, but it does not say which is which. So you cannot tell, from your new room number, which book you have received. Of course there is no problem with filling the rooms in this manner: both books had infinitely many recipients.
Your card arrives and you move to your new room. Are you now any less sure about which of the two books you have received? Presumably not. By your previous reasoning, there is now only a one in two chance that your book is The Beginning of Infinity because it is now in ‘half the rooms’. Since that is a contradiction, your method of assessing those probabilities must have been wrong. Indeed, all methods of assessing them are wrong, because – as this example shows – in Infinit Hotel there is no such thing as the probability that you have received the one book or the other.
Mathematically, this is nothing momentous. The example merely demonstrates again that the attributes probable or improbable, rare or common, typical or untypical have literally no meaning in regard to comparing infinite sets of natural numbers. — page 176

Revisiting Anthropic Argument for Fine Tuning

8.6.0 How our understanding of universes and probabilities impacts anthropic reasoning we have discussed in the 4th chapter (trying to solve fine tuning problem)?

Reminder on the anthropic reasoning and fine-tuning problem:
Any slight deviations from our laws of physics produce catastrophic results (like collapse of the universe etc.). Hence, it seems that our laws of physics were ‘designed’ to accommodate life, or that it is such an unlikely coincidence that it must be explained.
Anthropic reasoning claims that laws of physics are actually fine tuned and there are other ‘parallel universes’ with different laws of physics (don’t mistake with the multiverse). Subjective perspective explains the unlikeliness of human-friendly laws of physics: universes with astrophysicists are the only ones that wonder.

Probabilities don’t work in infinities. Anthropic argument claims that there are infinite number of universes with every possible laws of physics and in rare occasion they are life-friendly.

But just as with books in the hotel we can ‘shuffle’ this infinite set of universes and make life-friendly ones seem ‘likely’. There is no rare or likely occasions in infinities! Hence, anthropic reasoning is false — it doesn’t explain the fine-tuning problem.

8.6.1 What would we have to change in our description of reality to make anthropic argument work?

Infinity of universes cannot be a set: a collection of independent things. Because then we can shuffle them for some things to appear more or less likely. We have to treat these universes as a part of one bigger object: multiverse. In it, there is only one specific way to arrange universes (no shuffling allowed).

With such changes we can now meaningfully apply probabilities to our object and hence anthropic argument. Likelihoods would be seen as a measure, like a centimeter on a ruler.

But now suppose we also tell a story about the reality that connects all these universes and gives a preferred physical meaning to one way of labelling them. Here is one. A girl called Lyra, who was born in universe 1, discovers a device that can move her to other universes. It also keeps her alive inside a small sphere of life support, even in universes whose laws of physics do not otherwise support life. So long as she holds down a certain button on the device, she moves from universe to universe, in a fixe order, at intervals of exactly one minute. As soon as she lets go, she returns to her home universe. Let us label the universes 1, 2, 3 and so on, in the order in which the device visits them.
Sometimes Lyra also takes with her a measuring instrument that measures the constant D, and another that measures – rather like the SETI project, only much faster and more reliably – whether there are astrophysicists in the universe. She is hoping to test the predictions of the anthropic principle. …
[Lyra travels through all the universes in a fixed order within 2 minutes and measures values that would imply very likely presence of astrophysicists.] at the end of a two-minute journey through all the universes her SETI-like instrument will be displaying 0.5. So in that multiverse it is meaningful to say that half the universes have astrophysicists.
Using a universe-travelling device that visited the same universes in a different order, one would obtain a different value for that proportion. But, suppose that the laws of physics permit visiting them in only one order (rather as our own laws of physics normally allow us to be at different times only in one particular order). Since there is now only one way for measuring instruments to respond to averages, typical values and so on, a rational agent in those universes will always get consistent results when reasoning about probabilities – and about how rare or common, typical or untypical, sparse or dense, fine-tune or not anything is. And so now the anthropic principle can make testable, probabilistic predictions. What has made this possible is that the infinite set of universes with different values of D is no longer merely a set. It is a single physical entity, a multiverse with internal interactions (as harnessed by Lyra’s device) that relate different parts of it to each other and thereby provide a unique meaning, known as a measure, to proportions and averages over different universes.
None of the anthropic-reasoning theories that have been proposed to solve the fine-tuning problem provides any such measure. … There is, however, one theory in physics that already describes a multiverse for independent reasons. All its universes have the same constants of physics, and the interactions of these universes do not involve travel to, or measurement of, each other. But it does provide a measure for universes. That theory is quantum theory, which I shall discuss in Chapter 11. — page 179

Paradox of Zeno and Similar Mistakes

8.7.0 What is the paradox of Zeno of Elea?

The paradoxes of Zeno of Elea, such as that of Achilles and the tortoise, were early examples. Zeno managed to conclude that, in a race against a tortoise, Achilles will never overtake the tortoise if it has a head start – because, by the time Achilles reaches the point where the tortoise began, the tortoise will have moved on a little. By the time he reaches that new point, it will have moved a little further, and so on ad infinitum Thus the ‘catching-up’ procedure requires Achilles to perform an infinite number of catching-up steps in a finite time, which as a finite being he presumably cannot do. — page 182

8.7.1 What is the mistake that Zeno has made?

He has confused mathematical infinity with the physical one. But abstract and physical worlds are different, even though they are closely interlinked.

Do you see what Zeno did there? He just presumed that the mathematical notion that happens to be called ‘infinity faithfully captures the distinction between finite and infinite that is relevant to that physical situation. That is simply false. If he is complaining that the mathematical notion of infinite does not make sense, then we can refer him to Cantor, who showed that it does. If he is complaining that the physical event of Achilles overtaking the tortoise does not make sense, then he is claiming that the laws of physics are inconsistent – but they are not. But if he is complaining that there is something inconsistent about motion because one could not experience each point along a continuous path, then he is simply confusing two different things that both happen to be called ‘infinity’ There is nothing more to all his paradoxes than that mistake.
What Achilles can or cannot do is not deducible from mathematics. It depends only on what the relevant laws of physics say. If they say that he will overtake the tortoise in a given time, then overtake it he will. If that happens to involve an infinite number of steps of the form ‘move to a particular location’, then an infinite number of such steps will happen. If it involves his passing through an uncountable infinite of points, then that is what he does. But nothing physically infinite has happened. — page 182

8.7.2 What does this imply?

Depending on the laws of physics the same event can be infinite or not. Laws of physics (for physical world, not abstract one) determine finiteness.

Thus the laws of physics determine the distinction not only between rare and common, probable and improbable, fine-tune or not, but even between finite and infinite Just as the same set of universes can be packed with astrophysicists when measured under one set of laws of physics but have almost none when measured under another, so exactly the same sequence of events can be finite or infinite depending on what the laws of physics are.
Zeno’s mistake has been made with various other mathematical abstractions too. In general terms, the mistake is to confuse an abstract attribute with a physical one of the same name. — page 182

8.8.0 How Immanuel Kant have done a similar mistake to Zeno?

He has also confused abstract world with the physical one which led him to several wrong conclusions. There is an abstract concept of geometry and a physical one, these are not the same. (Even though one approximates the other quite well.)

Another example was in geometry. For centuries, no clear distinction was made between its status as a mathematical system and as a physical theory – and at first that did little harm, because the rest of science was very unsophisticated compared with geometry, and Euclid’s theory was an excellent approximation for all purposes at the time. But then the philosopher Immanuel Kant (1724–1804), who was well aware of the distinction between the absolutely necessary truths of mathematics and the contingent truths of science, nevertheless concluded that Euclid’s theory of geometry was self-evidently true of nature. Hence he believed that it was impossible rationally to doubt that the angles of a real triangle add up to 180 degrees. And in this way he elevated that formerly harmless misconception into a central fla in his philosophy, namely the doctrine that certain truths about the physical world could be ‘known a priori’ – that is to say, without doing science. And of course, to make matters worse, by ‘known’ he unfortunately meant ‘justified’.
Yet, even before Kant had declared it impossible to doubt that the geometry of real space is Euclidean, mathematicians had already doubted it. Soon afterwards the mathematician and physicist Carl Friedrich Gauss went so far as to measure the angles of a large triangle – but found no deviation from Euclid’s predictions. Eventually Einstein’s theory of curved space and time, which contradicted Euclid’s, was vindicated by experiments that were more accurate than Gauss’s. In the space near the Earth, the angles of a large triangle can add up to as much as 180.0000002 degrees, a variation from Euclid’s geometry which, for instance, satellite navigation systems nowadays have to take into account. In other situations – such as near black holes – the differences between Euclidean and Einsteinian geometry are so profound that they can no longer be described in terms of ‘deviations’ of one from the other. — page 183

Physics, Computation and Mathematics

8.9.0 How physical world plays a role of what can be computed or not?

The laws of physics provide us with only a narrow window through which we can look out on the world of abstractions. — page 185

What is computable or not in our physical world depends on the laws of physics. Uncomputable numbers are uncomputable because of the laws of physics we operate it, they are totally possible in the abstract world.

By applying Cantor’s argument to computation we can tell that uncomputable set of numbers is an uncountable infinity, and computable set is countable. Hence, the first one is significantly bigger than the second one:

Hence also – as the mathematician Kurt Gödel had discovered using a different approach to Hilbert’s challenge – almost all mathematical truths have no proofs. They are unprovable truths.
It also follows that almost all mathematical statements are undecidable: there is no proof that they are true, and no proof that they are false. Each of them is either true or false, but there is no way of using physical objects such as brains or computers to discover which is which. The laws of physics provide us with only a narrow window through which we can look out on the world of abstractions.
All undecidable statements are, directly or indirectly, about infinite sets. To the opponents of infinity in mathematics, this is due to the meaninglessness of such statements. But to me it is a powerful argument – like Hofstadter’s 641 argument – that abstractions exist objectively. For it means that the truth value of an undecidable statement is certainly not just a convenient way of describing the behaviour of some physical object like a computer or a collection of dominoes. — page 184

As most undecidable statement are about infinite sets we can infer that our laws of physics are particularly ‘bad’ at handling infinities. But this should not undermine their existence as abstract entities. Given different rules of physics we could easily prove them:

there are many unsolved mathematical conjectures, and some of those may well be undecidable. Take, for instance, the ‘prime-pairs conjecture’. A prime pair is a pair of prime numbers that differ by 2 – such as 5 and 7. The conjecture is that there is no largest prime pair: there are infinitely many of them. Suppose for the sake of argument that that is undecidable – using our physics. Under many other laws of physics it is decidable. The laws of Infinite Hotel are an example. Again, the details of how the management would settle the prime-pairs issue are not essential to my argument, but I present them here for the benefit of mathematically minded readers. The management would announce:

At the end of five minutes, the management would know the truth of the prime-pairs conjecture.
So, there is nothing mathematically special about the undecidable questions, the non-computable functions, the unprovable propositions. They are distinguished by physics only. Different physical laws would make different things infinite different things computable, different truths – both mathematical and scientific – knowable. It is only the laws of physics that determine which abstract entities and relationships are modelled by physical objects such as mathematicians’ brains, computers and sheets of paper. — page 185

8.10.0 Does the simplicity and complexity of something depend on laws of physics?

It certainly does because laws of physics determine computational tractability.

That brings me to another distinction that depends on the laws of physics: simple versus complex. Brains are physical objects. Thoughts are computations, of the types permitted under the laws of physics. Some explanations can be grasped easily and quickly – like ‘If Socrates was a man and Plato was a man then they were both men.’ This is easy because it can be stated in a short sentence and relies on the properties of an elementary operation (namely and). Other explanations are inherently hard to grasp, because their shortest form is still long and depends on many such operations. But whether the form of an explanation is long or short, and whether it requires few or many elementary operations, depends entirely on the laws of physics under which it is being stated and understood. — page 186

8.11.0 Do laws of physics determine whether some mathematical proposition is true or not?

Yes and no. They don’t determine abstractly whether it’s true or not: this is a matter of the abstract world. But, for us to prove something as true or not we have to use physical objects, which entirely depends on our laws of physics.

Computation is about representing some abstract phenomena (like numbers) in physical things (like computers). Proving something is about running a physical computation from which we infer things about abstract phenomena, of course, always tentatively. Computer science is the actual ‘proof theory’ of mathematics.

Whether a mathematical proposition is true or not is indeed independent of physics. But the proof of such a proposition is a matter of physics only. There is no such thing as abstractly proving something, just as there is no such thing as abstractly knowing something. Mathematical truth is absolutely necessary and transcendent, but all knowledge is generated by physical processes, and its scope and limitations are conditioned by the laws of nature. One can define a class of abstract entities and call them ‘proofs’ (or computations), just as one can define abstract entities and call them triangles and have them obey Euclidean geometry. But you cannot infer anything from that theory of ‘triangles’ about what angle you will turn through if you walk around a closed path consisting of three straight lines. Nor can those ‘proofs’ do the job of verifying mathematical statements. A mathematical ‘theory of proofs’ has no bearing on which truths can or cannot be proved in reality, or be known in reality; and similarly a theory of abstract ‘computation’ has no bearing on what can or cannot be computed in reality.
So, a computation or a proof is a physical process in which objects such as computers or brains physically model or instantiate abstract entities like numbers or equations, and mimic their properties. It is our window on the abstract. It works because we use such entities only in situations where we have good explanations saying that the relevant physical variables in those objects do indeed instantiate those abstract properties.
Consequently, the reliability of our knowledge of mathematics remains for ever subsidiary to that of our knowledge of physical reality. Every mathematical proof depends absolutely for its validity on our being right about the rules that govern the behaviour of some physical objects, like computers, or ink and paper, or brains. So, contrary to what Hilbert thought, and contrary to what most mathematicians since antiquity have believed and believe to this day, proof theory can never be made into a branch of mathematics. Proof theory is a science: specifically it is computer science. — page 188

8.11.1 How our world-view of mathematics should be influenced by such conclusion?

Necessary truth is merely the subject-matter of mathematics, not the reward we get for doing mathematics. The objective of mathematics is not, and cannot be, mathematical certainty. It is not even mathematical truth, certain or otherwise. It is, and must be, mathematical explanation. — The Fabric of Reality, page 253

The whole motivation for seeking a perfectly secure foundation for mathematics was mistaken. It was a form of justificationism Mathematics is characterized by its use of proofs in the same way that science is characterized by its use of experimental testing; in neither case is that the object of the exercise. The object of mathematics is to understand – to explain – abstract entities. Proof is primarily a means of ruling out false explanations; and sometimes it also provides mathematical truths that need to be explained. But, like all field in which progress is possible, mathematics seeks not random truths but good explanations. — page 188

Limits on Knowledge-Creation

8.12.0 “How do all those drastic limitations on what can be known and what can be achieved by mathematics and by computation, including the existence of undecidable questions in mathematics, square with the maxim that problems are soluble?”

David provides 5 arguments:

Problems that are so far from the physical world are usually uninteresting.
Lack of proof doesn’t imply that something is not understood.
We can use unproved things to create explanations and solve problems.
Most of knowledge, even in math, builds upon conjecture, not proofs.
Failing to prove something, or showing that the problem is undecidable is a success. Eliminating guesses is the essence of creating knowledge.

The core of David’s counterarguments is that solving problems is about conjecture and refutations — creative acts, that aren’t merely just about proofs. Limiting knowledge creation to proof generation is wrong and reductive.

[His first argument on interest of some ideas over others is purely empirical, but it might be worth exploring. I don’t take it seriously because a person claiming the reverse has just as good of a claim.]

Problems are conflict between ideas. Most mathematical questions that exist abstractly never appear as the subject of such a conflict they are never the subject of curiosity, never the focus of conflict in misconceptions about some attribute of the world of abstractions. In short, most of them are uninteresting.
Moreover, recall that finding proofs is not the purpose of mathematics: it is merely one of the methods of mathematics. The purpose is to understand, and the overall method, as in all fields is to make conjectures and to criticize them according to how good they are as explanations. One does not understand a mathematical proposition merely by proving it true. This is why there are such things as mathematics lectures rather than just lists of proofs. And, conversely, the lack of a proof does not necessarily prevent a proposition from being understood. On the contrary, the usual order of events is for the mathematician first to understand something about the abstraction in question and then to use that understanding to conjecture how true propositions about the abstraction might be proved, and then to prove them.
A mathematical theorem can be proved, yet remain for ever uninteresting. And an unproved mathematical conjecture can be fruitful in providing explanations even if it remains unproved for centuries, or even if it is unprovable. One example is the conjecture known in the jargon of computer science as ‘P ≠ NP’. It is, roughly speaking, that there exist classes of mathematical questions whose answers can be verified efficiently once one has them but cannot be computed efficiently in the first place by a universal (classical) computer. (‘Efficient computation has a technical definitio that roughly approximates what we mean by the phrase in practice.) Almost all researchers in computing theory are sure that the conjecture is true (which is further refutation of the idea that mathematical knowledge consists only of proofs). That is because, although no proof is known, there are fairly good explanations of why we should expect it to be true, and none to the contrary. (And so the same is thought to hold for quantum computers.)
Moreover, a vast amount of mathematical knowledge that is both useful and interesting has been built on the conjecture. It includes theorems of the form ‘if the conjecture is true then this interesting consequence follows.’ And there are fewer, but still interesting, theorems about what would follow if it were false. …
There are the direct limitations imposed by the universal laws of physics – we cannot exceed the speed of light, and so on. Then there are the limitations of epistemology: we cannot create knowledge other than by the fallible method of conjecture and criticism; errors are inevitable, and only error correcting processes can succeed or continue for long. None of this contradicts the maxim, because none of those limitations need ever cause an unresolvable conflict of explanations. — page 191

8.13.0 What is the most important limitation on the knowledge-creation?

We can’t predict it! Innovation that can be predicted is an already existing knowledge.

The most important of all limitations on knowledge-creation is that we cannot prophesy: we cannot predict the content of ideas yet to be created, or their effects. This limitation is not only consistent with the unlimited growth of knowledge, it is entailed by it, as I shall explain in the next chapter. — page 193

🚀 9 — Optimism

We have explored many topics, but still not the essence of David’s philosophy. Optimism chapter fixes this. Prepare to be excited.

Summary

Many public intellectuals like Martin Rees and Nick Bostrom make pessimistic predictions of humanity’s survival in the next century. They make a mistake of trying to predict what knowledge we’ll create in the future, but that’s why it’s a creation — we don’t know it yet. So any probabilities that they assign to events are hopelessly wrong and will be changed by events they can not foresee. Due to their pessimistic bias they make a pessimistic prediction. Anyone with optimistic bias can make the same mistake but arrive at diametrically opposite conclusion. Obviously both approaches are wrong: how then should we create policies for the future?

Policies are theories, and those are always guessed. We can never derive them, we guess them by making good explanations of the world. Trying to derive policies from something makes us think that there is better and worse source of knowledge, which makes political philosophy about ‘Who should rule?’ question. This promotes violence: either for putting the right ruler in charge, or suppressing its opposition so he can rule. This is wrong. There is no universally good source of knowledge, all our policies are just guesses which are full of errors. Hence, political philosophy should be about: ‘How do we find and fix errors non violently?’

David summarizes the optimism part well:

Optimism (in the sense that I have advocated) is the theory that all failures – all evils – are due to insufficient knowledge. This is the key to the rational philosophy of the unknowable. It would be contentless if there were fundamental limitations to the creation of knowledge, but there are not. It would be false if there were field – especially philosophical field such as morality – in which there were no such thing as objective progress. But truth does exist in all those fields and progress towards it is made by seeking good explanations. Problems are inevitable, because our knowledge will always be infinitely far from complete. Some problems are hard, but it is a mistake to confuse hard problems with problems unlikely to be solved. Problems are soluble, and each particular evil is a problem that can be solved. An optimistic civilization is open and not afraid to innovate, and is based on traditions of criticism. Its institutions keep improving, and the most important knowledge that they embody is knowledge of how to detect and eliminate errors. There may have been many short-lived enlightenments in history. Ours has been uniquely long-lived. — page 221

You can practice chapter questions as flashcards here.

Pessimism, precautionary principle and prophesying

9.1.0 What are Martin Rees’s views on humanity’s chances to survive twenty-first century?

Rees believes that new knowledge we will create could have catastrophic consequences. Not all of it, but it is enough to get one innovation ‘wrong’ for the humanity to go extinct. It’s like pulling a Russian roulette.

Martin Rees suspects that civilization was lucky to survive the twentieth century. For throughout the Cold War there was always a possibility that another world war would break out, this time fought with hydrogen bombs, and that civilization would be destroyed. That danger seems to have receded, but in Rees’s book Our Final Century, published in 2003, he came to the worrying conclusion that civilization now had only a 50 per cent chance of surviving the twenty-first century.
Again this was because of the danger that newly created knowledge would have catastrophic consequences. For example, Rees thought it likely that civilization-destroying weapons, particularly biological ones, would soon become so easy to make that terrorist organizations, or even malevolent individuals, could not be prevented from acquiring them. He also feared accidental catastrophes, such as the escape of genetically modified micro-organisms from a laboratory, resulting in a pandemic of an incurable disease. Intelligent robots, and nanotechnology (engineering on the atomic scale), ‘could in the long run be even more threatening’, he wrote. And ‘it is not inconceivable that physics could be dangerous too.’ For instance, it has been suggested that elementary-particle accelerators that briefly create conditions that are in some respects more extreme than any since the Big Bang might destabilize the very vacuum of space and destroy our entire universe.
Rees pointed out that, for his conclusion to hold, it is not necessary for any one of those catastrophes to be at all probable, because we need be unlucky only once, and we incur the risk afresh every time progress is made in a variety of fields He compared this with playing Russian roulette. — page 196

9.1.1 What are David’s counterarguments?

Martin Rees tries to predict knowledge creation, but this is inherently impossible. If he could this would not be prediction, this knowledge would already be created. And if we can’t predict knowledge creation, we can’t come up with any probabilities about it. Moreover, existential threats to humanity is not reductive to ‘pulling a trigger’, it is a complex process that would be about what decisions humans make.

But there is a crucial difference between the human condition and Russian roulette: the probability of winning at Russian roulette is unaffected by anything that the player may think or do. Within its rules, it is a game of pure chance. In contrast, the future of civilization depends entirely on what we think and do. If civilization falls, that will not be something that just happens to us: it will be the outcome of choices that people make. If civilization survives, that will be because people succeed in solving the problems of survival, and that too will not have happened by chance.
Both the future of civilization and the outcome of a game of Russian roulette are unpredictable, but in different senses and for entirely unrelated reasons. Russian roulette is merely random. Although we cannot predict the outcome, we do know what the possible outcomes are, and the probability of each, provided that the rules of the game are obeyed. The future of civilization is unknowable, because the knowledge that is going to affect it has yet to be created. Hence the possible outcomes are not yet known, let alone their probabilities.
The growth of knowledge cannot change that fact. On the contrary, it contributes strongly to it: the ability of scientific theories to predict the future depends on the reach of their explanations, but no explanation has enough reach to predict the content of its own successors – or their effects, or those of other ideas that have not yet been thought of. Just as no one in 1900 could have foreseen the consequences of innovations made during the twentieth century – including whole new field such as nuclear physics, computer science and biotechnology – so our own future will be shaped by knowledge that we do not yet have. We cannot even predict most of the problems that we shall encounter, or most of the opportunities to solve them, let alone the solutions and attempted solutions and how they will affect events. People in 1900 did not consider the internet or nuclear power unlikely: they did not conceive of them at all.
No good explanation can predict the outcome, or the probability of an outcome, of a phenomenon whose course is going to be significantly affected by the creation of new knowledge. This is a fundamental limitation on the reach of scientific prediction, and, when planning for the future, it is vital to come to terms with it. — page 197

9.2.0 What is the distinction David draws between prediction and prophecy?

Following Popper, I shall use the term prediction for conclusions about future events that follow from good explanations, and prophecy for anything that purports to know what is not yet knowable. Trying to know the unknowable leads inexorably to error and self-deception. — page 198

9.2.1 What is the Michelson example that shows this error?

For example, in 1894 the physicist Albert Michelson made the following prophecy about the future of physics:
The more important fundamental laws and facts of physical science have all been discovered, and these are now so firmly established that the possibility of their ever being supplanted in consequence of new discoveries is exceedingly remote . . . Our future discoveries must be looked for in the sixth place of decimals. — Albert Michelson, address at the opening of the Ryerson Physical Laboratory, University of Chicago, 1894
What exactly was Michelson doing when he judged that there was only an ‘exceedingly remote’ chance that the foundations of physics as he knew them would ever be superseded? He was prophesying the future. How? On the basis of the best knowledge available at the time. But that consisted of the physics of 1894! Powerful and accurate though it was in countless applications, it was not capable of predicting the content of its successors. It was poorly suited even to imagining the changes that relativity and quantum theory would bring – which is why the physicists who did imagine them won Nobel prizes. Michelson would not have put the expansion of the universe, or the existence of parallel universes, or the non-existence of the force of gravity, on any list of possible discoveries whose probability was ‘exceedingly remote’. He just didn’t conceive of them at all. — page 198

9.2.2 What is the other point that Michelson’s example underlines?

Observation is theory-laden. If only he had some theory (even wrong one) about what the constant speed of light relative to the observer meant, he could have persevered and find Einstein’s breakthrough!

Michelson himself had already contributed unwittingly to the new system – in this case with an experimental result. In 1887 he and his colleague Edward Morley had observed that the speed of light relative to an observer remains constant when the observer moves. This astoundingly counter-intuitive fact later became the centrepiece of Einstein’s special theory of relativity. But Michelson and Morley did not realize that that was what they had observed. Observations are theory-laden. Given an experimental oddity, we have no way of predicting whether it will eventually be explained merely by correcting a minor parochial assumption or by revolutionizing entire sciences. We can know that only after we have seen it in the light of a new explanation. In the meantime we have no option but to see the world through our best existing explanations – which include our existing misconceptions. And that biases our intuition. Among other things, it inhibits us from conceiving of significant changes. — page 199

9.3.0 What is the precautionary principle according to David?

Blind optimism is a stance towards the future. It consists of proceeding as if one knows that the bad outcomes will not happen. The opposite approach, blind pessimism, often called the precautionary principle, seeks to ward off disaster by avoiding everything not known to be safe. No one seriously advocates either of these two as a universal policy, but their assumptions and their arguments are common, and often creep into people’s planning.
Blind optimism is also known as ‘overconfidence or ‘recklessness’. An often cited example, perhaps unfairly, is the judgement of the builders of the ocean liner Titanic that it was ‘practically unsinkable’. The largest ship of its day, it sank on its maiden voyage in 1912. Designed to survive every foreseeable disaster, it collided with an iceberg in a manner that had not been foreseen. A blind pessimist argues that there is an inherent asymmetry between good and bad consequences: a successful maiden voyage cannot possibly do as much good as a disastrous one can do harm. As Rees points out, a single catastrophic consequence of an otherwise beneficia innovation could put an end to human progress for ever. So the blindly pessimistic approach to building ocean liners is to stick with existing designs and refrain from attempting any records. — page 201

9.3.1 What is its criticism?

It assumes that disaster cannot happen from the absence of some knowledge, but can with its presence because innovation is like a Russian roulette: it can be for good and bad*.* This is wrong. Actually, the reason of every disaster that has ever happened was the absence of knowledge — people didn’t know how to do right (like design an incentive system, have an irrigation technology and so on).

But blind pessimism is a blindly optimistic doctrine. It assumes that unforeseen disastrous consequences cannot follow from existing knowledge too (or, rather, from existing ignorance). Not all shipwrecks happen to record-breaking ships. Not all unforeseen physical disasters need be caused by physics experiments or new technology. But one thing we do know is that protecting ourselves from any disaster, foreseeable or not, or recovering from it once it has happened, requires knowledge; and knowledge has to be created. The harm that can flo from any innovation that does not destroy the growth of knowledge is always finite the good can be unlimited. There would be no existing ship designs to stick with, nor records to stay within, if no one had ever violated the precautionary principle. — page 201

But just as every other failed civilization in the past has done so because of absence of knowledge, why would we expect ourselves presently to be any different? We should not, and precautionary principle gives no answer to that question.

As we look back on the failed civilizations of the past, we can see that they were so poor, their technology was so feeble, and their explanations of the world so fragmentary and full of misconceptions that their caution about innovation and progress was as perverse as expecting a blindfold to be useful when navigating dangerous waters. Pessimists believe that the present state of our own civilization is an exception to that pattern. But what does the precautionary principle say about that claim? Can we be sure that our present knowledge, too, is not riddled with dangerous gaps and misconceptions? That our present wealth is not pathetically inadequate to deal with unforeseen problems? Since we cannot be sure, would not the precautionary principle require us to confine ourselves to the policy that would always have been salutary in the past – namely innovation and, in emergencies, even blind optimism about the benefit of new knowledge? — page 204

9.3.2 What would be an example of precautionary policy by Stephen Hawking?

An example of a blindly pessimistic policy is that of trying to make our planet as unobtrusive as possible in the galaxy, for fear of contact with extraterrestrial civilizations. Stephen Hawking recently advised this, in his television series Into the Universe. He argued, ‘If [extraterrestrials] ever visit us, I think the outcome would be much as when Christopher Columbus first landed in America, which didn’t turn out very well for the Native Americans.’ He warned that there might be nomadic, space-dwelling civilizations who would strip the Earth of its resources, or imperialist civilizations who would colonize it. — page 202

9.3.3 What is its criticism?

Taking this theory seriously leads to nonsense. What would aliens that have traveled millions of light-years be mining? What in the world could Earth offer them of interest that they can’t have with their ‘god-like’ level of knowledge? Do we still ants resources and make them our slaves?

One is the Spaceship Earth idea on a larger scale: the assumption that progress in a hypothetical rapacious civilization is limited by raw materials rather than by knowledge. What exactly would it come to steal? Gold? Oil? Perhaps our planet’s water? Surely not, since any civilization capable of transporting itself here, or raw materials back across galactic distances, must already have cheap transmutation and hence does not care about the chemical composition of its raw materials. So essentially the only resource of use to it in our solar system would be the sheer mass of matter in the sun. But matter is available in every star. Perhaps it is collecting entire stars wholesale in order to make a giant black hole as part of some titanic engineering project. But in that case it would cost it virtually nothing to omit inhabited solar systems (which are presumably a small minority, otherwise it is pointless for us to hide in any case); so would it casually wipe out billions of people? Would we seem like insects to it? — page 203

9.3.4 Alright, but then they wouldn’t notice us like we don’t notice ants when building a highway! What is the rebuttal to that?

Sustainable knowledge creation requires certain moral stands as Jacob Bronowski has pointed out, such as: openness to criticism, tolerance, value of the truth, integrity and so on. Alien morality should not be that different if they have achieved such scientific progress. Eventually, we will ourselves fully embrace as a society value of life, be it human one or not.

Moreover, there is only one way of making progress: conjecture and criticism. And the only moral values that permit sustained progress are the objective values that the Enlightenment has begun to discover. No doubt the extraterrestrials’ morality is different from ours; but that will not be because it resembles that of the conquistadors. Nor would we be in serious danger of culture shock from contact with an advanced civilization: it will know how to educate its own children (or AIs), so it will know how to educate us – and, in particular, to teach us how to use its computers. — page 203

Popper and Political Philosophy

9.4.0 We have seen that both blind pessimism and optimism are unfavorable. We also can’t predict the future because of knowledge creation unpredictability. The question arises: How then can we formulate policies for the unknown? What should we derive them from?

Our policies are just theories, guesses that are not derived from anything. We will always have errors, and we could only hope to be better at eliminating them.

So – how? How can we formulate policies for the unknown? If we cannot derive them from our best existing knowledge, or from dogmatic rules of thumb like blind optimism or pessimism, where can we derive them from? Like scientific theories, policies cannot be derived from anything. They are conjectures. And we should choose between them not on the basis of their origin, but according to how good they are as explanations: how hard to vary.
Like the rejection of empiricism, and of the idea that knowledge is ‘justified true belief’, understanding that political policies are conjectures entails the rejection of a previously unquestioned philosophical assumption. Again, Popper was a key advocate of this rejection. He wrote:
The question about the sources of our knowledge . . . has always been asked in the spirit of: ‘What are the best sources of our knowledge – the most reliable ones, those which will not lead us into error, and those to which we can and must turn, in case of doubt, as the last court of appeal?’ I propose to assume, instead, that no such ideal sources exist – no more than ideal rulers – and that all ‘sources’ are liable to lead us into error at times. And I propose to replace, therefore, the question of the sources of our knowledge by the entirely different question: ‘How can we hope to detect and eliminate error?’ — ‘Knowledge without Authority’ (1960)
The question ‘How can we hope to detect and eliminate error?’ is echoed by Feynman’s remark that ‘science is what we have learned about how to keep from fooling ourselves’. And the answer is basically the same for human decision-making as it is for science: it requires a tradition of criticism, in which good explanations are sought – for example, explanations of what has gone wrong, what would be better, what effect various policies have had in the past and would have in the future. — page 208

9.5.0 How Popperian epistemology applies to political philosophy?

We can’t derive knowledge from any ‘unique’ source. All theories are guesses that are bound to have mistakes which we would have to correct. Hence, the question should not be: Who should rule? Who is the best ruler? Because this mimics mistake of thinking that there is a better or worse source of knowledge. Instead we should ask: How do we find and fix errors?

Political philosophy traditionally centred on a collection of issues that Popper called the ‘who should rule?’ question. Who should wield power? Should it be a monarch or aristocrats, or priests, or a dictator, or a small group, or ‘the people’, or their delegates? And that leads to derivative questions such as ‘How should a king be educated?’ ‘Who should be enfranchised in a democracy?’ ‘How does one ensure an informed and responsible electorate?’
Popper pointed out that this class of questions is rooted in the same misconception as the question ‘How are scientific theories derived from sensory data?’ which define empiricism. It is seeking a system that derives or justified the right choice of leader or government, from existing data – such as inherited entitlements, the opinion of the majority, the manner in which a person has been educated, and so on. — page 209

9.5.1 What is the other mistake that ‘Who should rule?’ philosophy commits?

It makes violent change of powers seem worthwhile — for if only we get the right people to rule all will be great. This works both ways: if your are the ‘right’ ruler, you can use violence to suppress the opposition, because they oppose the righteousness itself (i.e. you).

Ideas have consequences, and the ‘who should rule?’ approach to political philosophy is not just a mistake of academic analysis: it has been part of practically every bad political doctrine in history. If the political process is seen as an engine for putting the right rulers in power, then it justified violence, for until that right system is in place, no ruler is legitimate; and once it is in place, and its designated rulers are ruling, opposition to them is opposition to rightness. The problem then becomes how to thwart anyone who is working against the rulers or their policies. By the same logic, everyone who thinks that existing rulers or policies are bad must infer that the ‘who should rule?’ question has been answered wrongly, and therefore that the power of the rulers is not legitimate, and that opposing it is legitimate, by force if necessary. Thus the very question ‘Who should rule?’ begs for violent, authoritarian answers, and has often received them. It leads those in power into tyranny, and to the entrenchment of bad rulers and bad policies; it leads their opponents to violent destructiveness and revolution.
Advocates of violence usually have in mind that none of those things need happen if only everyone agreed on who should rule. But that means agreeing about what is right, and, given agreement on that, rulers would then have nothing to do. And, in any case, such agreement is neither possible nor desirable: people are different, and have unique ideas; problems are inevitable, and progress consists of solving them.
Popper therefore applies his basic ‘how can we detect and eliminate errors?’ to political philosophy in the form how can we rid ourselves of bad governments without violence? Just as science seeks explanations that are experimentally testable, so a rational political system makes it as easy as possible to detect, and persuade others, that a leader or policy is bad, and to remove them without violence if they are. Just as the institutions of science are structured so as to avoid entrenching theories, but instead to expose them to criticism and testing, so political institutions should not make it hard to oppose rulers and policies, non-violently, and should embody traditions of peaceful, critical discussion of them and of the institutions themselves and everything else. Thus, systems of government are to be judged not for their prophetic ability to choose and install good leaders and policies, but for their ability to remove bad ones that are already there.
That entire stance is fallibilism in action. It assumes that rulers and policies are always going to be flawed – that problems are inevitable. But it also assumes that improving upon them is possible: problems are soluble. The ideal towards which this is working is not that nothing unexpected will go wrong, but that when it does it will be an opportunity for further progress. — page 210

Optimism = Wealth

9.6.0 What is David’s definition of wealth?

Wealth The repertoire of physical transformations that one is capable of causing. — page 221

9.7.0 What is the principle of optimism?

The Principle of Optimism All evils are caused by insufficient knowledge. — page 212

If something doesn’t go against the laws of physics then it can be done. If laws of physics don’t prohibit a problem to be solved, it will be solved, it is just a matter of knowing how.

Optimism is, in the first instance, a way of explaining failure, not prophesying success. It says that there is no fundamental barrier, no law of nature or supernatural decree, preventing progress. Whenever we try to improve things and fail, it is not because the spiteful (or unfathomably benevolent) gods are thwarting us or punishing us for trying, or because we have reached a limit on the capacity of reason to make improvements, or because it is best that we fail, but always because we did not know enough, in time. But optimism is also a stance towards the future, because nearly all failures, and nearly all successes, are yet to come.
Optimism follows from the explicability of the physical world, as I explained in Chapter 3. If something is permitted by the laws of physics, then the only thing that can prevent it from being technologically possible is not knowing how. Optimism also assumes that none of the prohibitions imposed by the laws of physics are necessarily evils. So, for instance, the lack of the impossible knowledge of prophecy is not an insuperable obstacle to progress. Nor are insoluble mathematical problems, as I explained in Chapter 8.
That means that in the long run there are no insuperable evils, and in the short run the only insuperable evils are parochial ones. There can be no such thing as a disease for which it is impossible to discover a cure, other than certain types of brain damage – those that have dissipated the knowledge that constitutes the patient’s personality. For a sick person is a physical object, and the task of transforming this object into the same person in good health is one that no law of physics rules out. Hence there is a way of achieving such a transformation – that is to say, a cure. It is only a matter of knowing how. If we do not, for the moment, know how to eliminate a particular evil, or we know in theory but do not yet have enough time or resources (i.e. wealth), then, even so, it is universally true that either the laws of physics forbid eliminating it in a given time with the available resources or there is a way of eliminating it in the time and with those resources. — page 212

9.7.1 What is the main assumption that optimism makes?

Every problem that is constrained by the laws of physics is inherently uninteresting and non-vital for the progress to continue. We have all we need to solve relevant problems and progress.

Optimism implies all the other necessary conditions for knowledge to grow, and for knowledge-creating civilizations to last, and hence for the beginning of infinity We have, as Popper put it, a duty to be optimistic – in general, and about civilization in particular. One can argue that saving civilization will be difficult That does not mean that there is a low probability of solving the associated problems. When we say that a mathematical problem is hard to solve, we do not mean that it is unlikely to be solved. All sorts of factors determine whether mathematicians even address a problem, and with what effort. If an easy problem is not deemed to be interesting or useful, they might leave it unsolved indefinitely while hard problems are solved all the time. — page 214

9.8.0 What would be an example of optimistic culture in our history?

David gives an example of Athens during Pericles time. Its culture and aspirations caused one of the first Enlightenments we know of. It shows that sustainable progress is deeply connected to certain moral choices.

As far as I know, no historian has investigated the history of optimism, but my guess is that whenever it has emerged in a civilization there has been a mini-enlightenment: a tradition of criticism resulting in an efflorescence of many of the patterns of human progress with which we are familiar, such as art, literature, philosophy, science, technology and the institutions of an open society. The end of pessimism is potentially a beginning of infinity. Yet I also guess that in every case – with the single, tremendous exception (so far) of our own Enlightenment – this process was soon brought to an end and the reign of pessimism was restored.
The best-known mini-enlightenment was the intellectual and political tradition of criticism in ancient Greece which culminated in the so called ‘Golden Age’ of the city-state of Athens in the fifty century BCE. Athens was one of the first democracies, and was home to an astonishing number of people who are regarded to this day as major figure in the history of ideas, such as the philosophers Socrates, Plato and Aristotle, the playwrights Aeschylus, Aristophanes, Euripides and Sophocles, and the historians Herodotus, Thucydides and Xenophon. The Athenian philosophical tradition continued a tradition of criticism dating back to Thales of Miletus over a century earlier and which had included Xenophanes of Colophon (570–480 BCE), one of the first to question anthropocentric theories of the gods. Athens grew wealthy through trade, attracted creative people from all over the known world, became one of the foremost military powers of the age, and built a structure, the Parthenon, which is to this day regarded as one of the great architectural achievements of all time. At the height of the Golden Age, the Athenian leader Pericles tried to explain what made Athens successful. Though he no doubt believed that the city’s patron goddess, Athena, was on their side, he evidently did not consider ‘the goddess did it’ to be a sufficient explanation for the Athenians’ success. Instead, he listed specific attributes of Athenian civilization. We do not know exactly how much of what he described was flatter or wishful thinking, but, in assessing the optimism of a civilization, what that civilization aspired to be must be even more important than what it had yet succeeded in becoming.
The first attribute that Pericles cited was Athens’ democracy. And he explained why. Not because ‘the people should rule’, but because it promotes ‘wise action’. It involves continual discussion, which is a necessary condition for discovering the right answer, which is in turn a necessary condition for progress:
Instead of looking upon discussion as a stumbling-block in the way of action, we think it an indispensable preliminary to any wise action at all. — Pericles, ‘Funeral Oration’, c. 431 BCE
He also mentioned freedom as a cause of success. A pessimistic civilization considers it immoral to behave in ways that have not been tried many times before, because it is blind to the possibility that the benefit of doing so might offset the risks. So it is intolerant and conformist. But Athens took the opposite view. Pericles also contrasted his city’s openness to foreign visitors with the closed, defensive attitude of rival cities: again, he expected that Athens would benefit from contact with new, unforeseeable ideas, even though, as he acknowledged, this policy gave enemy spies access to the city too. He even seems to have regarded the lenient treatment of children as a source of military strength:
In education, where our rivals from their very cradles by a painful discipline seek after manliness, in Athens we live exactly as we please, and yet are just as ready to encounter every legitimate danger.
A pessimistic civilization prides itself on its children’s conformity to the proper patterns of behaviour, and bemoans every real or imagined novelty. — page 216

9.8.1 What is another example that David mentions in the book?

Florence had a similar rise that sadly was quickly suppressed.

Another short-lived enlightenment happened in the Italian city-state of Florence in the fourteenth century. This was the time of the early Renaissance, a cultural movement that revived the literature, art and science of ancient Greece and Rome after more than a millennium of intellectual stagnation in Europe. It became an enlightenment when the Florentines began to believe that they could improve upon that ancient knowledge. This era of dazzling innovation, known as the Golden Age of Florence, was deliberately fostered by the Medici family, who were in effect the city’s rulers – especially Lorenzo de’ Medici, known as ‘the Magnificent’ who was in charge from 1469 to 1492. Unlike Pericles, the Medici were not devotees of democracy: Florence’s enlightenment began not in politics but in art, and then philosophy, science and technology, and in those field it involved the same openness to criticism and desire for innovation both in ideas and in action. Artists, instead of being restricted to traditional themes and styles, became free to depict what they considered beautiful, and to invent new styles. Encouraged by the Medici, the wealthy of Florence competed with each other in the innovativeness of the artists and scholars whom they sponsored – such as Leonardo da Vinci, Michelangelo and Botticelli. Another denizen of Florence at this time was Niccolò Machiavelli, the first secular political philosopher since antiquity.
The Medici were soon promoting the new philosophy of ‘humanism’, which valued knowledge above dogma, and virtues such as intellectual independence, curiosity, good taste and friendship over piety and humility. They sent agents all over the known world to obtain copies of ancient books, many of which had not been seen in the West since the fall of the Western Roman Empire. The Medici library made copies which it supplied to scholars in Florence and elsewhere. Florence became a powerhouse of newly revived ideas, new interpretations of ideas, and brand-new ideas. — page 218

😴 10 — A Dream of Socrates

This is an easy chapter for David’s standard. It is about a dialogue between Hermes and Socrates and there is not a lot to summarize. Chapter’s main focuses are:

Are we fallible?
Can we derive knowledge from something?
What should be the basis of morality?

Summary

We are fallible and we can’t escape it. Our senses, our thoughts, everything; all is fallible.

We can’t derive knowledge from anything. We can only guess and then criticize to improve.

The basis of morality is in preserving the means of error-correction. They are the only way to progress, constraining progress is immoral for it prolongs suffering.

You can practice chapter questions as flashcards here.

Socrates and Hermes

10.1.0 Is objective knowledge attainable?

No! But we can get closer to it by correcting our mistakes.

socrates: Very well: obviously I can’t be sure of anything. But I don’t want to be. I can think of nothing more boring – no offence meant, wise Apollo – than to attain the state of being perfectly secure in one’s beliefs, which some people seem to yearn for. I see no use for it – other than to provide a semblance of an argument when one doesn’t have a real one. Fortunately that mental state has nothing to do with what I do yearn for, which is to discover the truth of how the world is, and why – and, even more, of how it should be.
hermes: Congratulations, Socrates, on your epistemological wisdom. The knowledge that you seek – objective knowledge – is hard to come by, but attainable. That mental state that you do not seek – justified belief – is sought by many people, especially priests and philosophers. But, in truth, beliefs cannot be justified except in relation to other beliefs, and even then only fallibly. So the quest for their justification can lead only to an infinite regress – each step of which would itself be subject to error.
socrates: Again, I know this. hermes: Indeed. And, as you have rightly remarked, it doesn’t count as a ‘revelation’ if I tell you what you already know. Yet – notice that that remark is precisely what people who seek justified belief do not agree with. socrates: What? I’m sorry, but that was too convoluted a comment for my allegedly wise mind to comprehend. Please explain what I am to notice about those people who seek ‘justified belief’.
hermes: Merely this. Suppose they just happen to be aware of the explanation of something. You and I would say that they know it. But to them, no matter how good an explanation it is, and no matter how true and important and useful it may be, they still do not consider it to be knowledge. It is only if a god then comes along and reassures them that it is true (or if they imagine such a god or other authority) that they count it as knowledge. So, to them it does count as a revelation if the authority tells them what they are already fully aware of.
socrates: I see that. And I see that they are foolish, because, for all they know, the ‘authority’ [gestures at hermes] may be toying with them. Or trying to teach them some important lesson. Or they may be misunderstanding the authority. Or they may be mistaken in their belief that it is an authority —
hermes: Yes. So the thing they call ‘knowledge’, namely justified belief, is a chimera. It is unattainable to humans except in the form of self-deception; it is unnecessary for any good purpose; and it is undesired by the wisest among mortals.
socrates: I know. — page 226

10.2.0 Can we trust our senses?

No, for they are fallible! They can be wrong just as our thoughts can. We must rely on our good explanations of the world, while always criticizing them.

hermes: [Ignores the question.] Hence, also, I wasn’t referring to justified belief when I asked whether you are sure that I am before your eyes. I was only questioning how you can claim to be ‘seeing clearly’ what is before your eyes when you also claim to be asleep!
socrates: Oh! Yes, you have caught me in an error – but surely only a trivial one. Indeed, you may not be literally before my eyes. Perhaps you are at home on Olympus, sending me a mere likeness of yourself. But in that case you are controlling that likeness and I am seeing it, and referring to it as ‘you’, so I am seeing ‘you’.
hermes: But that is not what I asked. I asked what is here before your eyes. In reality.
socrates: All right. Before my eyes, in reality, there is – a small room. Or, if you want a literal reply, what is before my eyes is – eyelids, since I expect that they are shut. Yet I see from your expression that you want even more precision. Very well: before my eyes are the inside surfaces of my eyelids.
hermes: And can you see those? In other words, is it really ‘easy to see’ what is before your eyes?
socrates: Not at the moment. But that is only because I am dreaming.
hermes: Is it only because you are dreaming? Are you saying that if you were awake you would now be seeing the inside surfaces of your eyelids?
[Small exchange on whether Socrates can ever be sure of anything he senses: Are senses fallible?] …
socrates: Ah! Now you are trying to catch me in a circularity. You want me to say that one can tell that conditions are good for seeing when one can easily see what is there.
hermes: I want you not to say so.
socrates: It seems to me that you have been asking questions about me – what is in front of me, what I can easily see, whether I am sure, and so on. But I seek fundamental truths, of which I estimate that not a single one is predominantly about me. So let me stress again: I am not sure what is in front of my eyes – ever – with my eyes open or closed, asleep or awake. Nor can I be sure what is probably in front of my eyes, for how could I estimate the probability that I am dreaming when I think I am awake? Or that my whole previous life has been but a dream in which it has pleased one of you immortals to imprison me?
hermes: Indeed.
socrates: I might even be a victim of a mundane deception, such as those of conjurers. We know that a conjurer is deceiving us because he shows us something that cannot be – and then asks for money! But if he were to forgo his fee and show me something that can be but is not, how could I ever know? Perhaps this entire vision of you is not a dream after all but some cunning conjurer’s trick. On the other hand, perhaps you really are here in person and I am awake after all. None of this can I ever be sure is so, or not so. I can, however, conceive of knowing some of it.
hermes: Precisely. And is the same true of your moral knowledge? In regard to what is right and wrong, could you be mistaken, or misled, by the equivalent of mirages or tricks?
socrates: That seems harder to imagine. For in regard to moral knowledge I need my senses very little: it is mainly just my own thoughts. I reason about what is right and wrong, or what makes a person virtuous or wicked. I can be mistaken, of course, in these mental deliberations, but not so easily deceived by outside tricks or illusions, for they affect only our senses and not our reason.
[Hermes shows that Socrates can be tricked by his thinking just as by his senses. For instance, by Athenian societal dogmas that he learned as a kid, and so on. Hence, neither senses nor thinking are protected from fallibility. Nothing is.] — page 227

10.3.0 What should be the basis of morality?

Preserving the means of error correction, for this is the only way to improve. Constraining progress is equal to enabling suffering, which we should take personally.

Making an error is not immoral as long as we can fix it. Disabling the ‘fixing’ process is immoral.

hermes: Perhaps. Now consider: what would happen if the fallible Athenian voters made a mistake and enacted a law that was very unwise and unjust –
socrates: Which, alas, they often do –
hermes: Imagine a specific case, for the sake of argument. Suppose that they were somehow firmly persuaded that thieving is a high virtue from which many practical benefit flow and that they abolished all laws forbidding it. What would happen?
socrates: Everyone would start thieving. Very soon those who were best at thieving (and at living among thieves) would become the wealthiest citizens. But most people would no longer be secure in their property (even most thieves), and all the farmers and artisans and traders would soon fin it impossible to continue to produce anything worth stealing. So disaster and starvation would follow, while the promised benefit would not, and they would all realize that they had been mistaken.
hermes: Would they? Let me remind you again of the fallibility of human nature, Socrates. Given that they were firmly persuaded that thievery was beneficial wouldn’t their first reaction to those setbacks be that there was not enough thievery going on? Wouldn’t they enact laws to encourage it still further?
socrates: Alas, yes – at first Yet, no matter how firml they were persuaded, these setbacks would be problems in their lives, which they would want to solve. A few among them would eventually begin to suspect that increased thievery might not be the solution after all. So they would think about it more. They would have been convinced of the benefit of thievery by some explanation or other. Now they would try to explain why the supposed solution didn’t seem to be working. Eventually they would fin an explanation that seemed better. So gradually they would persuade others of that – and so on until a majority again opposed thievery.
hermes: Aha! So salvation would come about through persuasion. socrates: If you like. Thought, explanation and persuasion. And now they would understand better why thievery is harmful, through their new explanations.
hermes: By the way, the little story we have just imagined is exactly how Athens really does look, from my point of view.
socrates: [somewhat resentfully] How you must laugh at us!
hermes: Not at all, Athenian. As I said, I honour you. Now, let us consider what would happen if, instead of legalizing thievery, their error had been to ban debate. And to ban philosophy and politics and elections and that whole constellation of activities, and to consider them shameful.
socrates: I see. That would have the effect of banning persuasion. And hence it would block off that path to salvation that we have discussed. This is a rare and deadly sort of error: it prevents itself from being undone.
hermes: Or at least it makes salvation immensely more difficult yes. This is what Sparta looks like, to me.
socrates: I see. And to me too, now that you point it out. In the past I have often pondered the many differences between our two cities, for I must confess that there was – and still is – much that I admire about the Spartans. But I had never realized before now that those differences are all superficial. Beneath their evident virtues and vices, beneath even the fact that they are bitter enemies of Athens, Sparta is the victim – and the servant – of a profound evil. This is a momentous revelation, noble Apollo, better than a thousand declarations of the Oracle, and I cannot adequately express my gratitude.
hermes: [Nods in acknowledgement.]
socrates: I also see why you urge me always to bear human fallibility in mind. In fact, since you mentioned that some moral truths follow logically from epistemological considerations, I am now wondering whether they all do. Could it be that the moral imperative not to destroy the means of correcting mistakes is the only moral imperative? That all other moral truths follow from it?
hermes: [Is silent.]
socrates: As you wish. — page 234

10.4.0 Do we derive knowledge from our senses?

No we do not, this is an empirical mistake! All our knowledge is in form of creative guesses.

hermes: Yes. Nevertheless, you have conceded that even those things that you thought were the easiest to see literally are in fact not easy to see at all without prior knowledge about them. In fact nothing is easy to see without prior knowledge. All knowledge of the world is hard to come by. Moreover –
socrates: Moreover, it follows that we do not come by it through seeing. It does not flow into us through our senses.
hermes: Exactly.
socrates: Yet you say that objective knowledge is attainable. So, if it does not come to us through the senses, where it does come from?
hermes: Suppose I were to tell you that all knowledge comes from persuasion.
socrates: Persuasion again! Well, I would reply – with all due respect – that that makes no sense. Whoever persuades me of something must first have discovered it himself, so in such a case the relevant issue is where his knowledge came from –
hermes: Quite right, unless –
socrates: And, in any case, when I learn something through persuasion, it is coming to me via my senses.
hermes: No, there you are mistaken. It only seems that way to you.
socrates: What?
hermes: Well, you are learning things from me now, aren’t you? Are they coming to you through your senses?
…
hermes: Now, if I am only a figment of your imagination, then who has persuaded you?
socrates: Presumably I myself – unless this dream is coming neither from you nor from within myself, but from some other source . . .
hermes: But did you not say that you are open to persuasion by anyone? If dreams emanate from an unknown source, what difference should that make? If they are persuasive, are you not honour-bound as an Athenian to accept them?
socrates: It seems that I am. But what if a dream were to emanate from a malevolent source?
hermes: That makes no fundamental difference either. Suppose that the source purports to tell you a fact. Then, if you suspect that the source is malevolent, you will try to understand what evil it is trying to perpetrate by telling you the alleged fact. But then, depending on your explanation, you may well decide to believe it anyway –
socrates: I see. For instance, if an enemy announces that he is planning to kill me, I may well believe him despite his malevolence.
hermes: Yes. Or you may not. And if your closest friend purports to tell you a fact, you may likewise wonder whether he has been misled by a malevolent third party – or is simply mistaken for any of countless reasons. Thus situations can easily arise in which you disbelieve your closest friend and believe your worst enemy. What matters in all cases is the explanation you create, within your own mind, for the facts, and for the observations and advice in question. But the case here is simpler. As I said, I reveal no facts. I’m only making arguments.
socrates: I see. I have no need to trust the source if the argument itself is persuasive. And no way of using any source unless I also have a persuasive argument. …
socrates: As you wish, flee Hermes. Then let me try to understand your argument about knowledge. I asked where knowledge comes from, and you directed my attention to this very dream. You asked whether it would make any difference to how I regard the knowledge I am learning from you if it turns out not to have been supernaturally inspired after all. And I had to agree that it would not. So am I to conclude that . . . all knowledge originates from the same source as dreams? Which is within ourselves?
hermes: Of course it does. Do you remember what Xenophanes wrote just after he said that objective knowledge is attainable by humans?
socrates: Yes. The passage continues:
But as for certain truth, no man has known it, Nor will he know it; neither of the gods, Nor yet of all things of which I speak. And even if by chance he were to utter The perfect truth, he would himself not know it –
So there he’s saying that, although objective knowledge is attainable, justified belief (‘certain truth’) is not.
hermes: Yes, we’ve covered all that. But your answer is in the next line.
socrates: ‘For all is but a woven web of guesses.’ Guesses!
hermes: Yes. Conjectures. — page 236, 237, 238 and 239

10.4.1 But what about when we hear someone express their idea?

We still guess what they have meant, we interpret they. Of course, our interpretations can be wrong.

socrates: But wait! What about when knowledge does not come from guesswork – as when a god sends me a dream? What about when I simply hear ideas from other people? They may have guessed them, but I then obtain them merely by listening.
hermes: You do not. In all those cases, you still have to guess in order to acquire the knowledge.
socrates: I do?
hermes: Of course. Have you yourself not often been misunderstood, even by people trying hard to understand you?
socrates: Yes.
hermes: Have you, in turn, not often misunderstood what someone means, even when he is trying to tell you as clearly as he can?
socrates: Indeed I have. Not least during this conversation!
hermes: Well, this is not an attribute of philosophical ideas only, but of all ideas. Remember when you all got lost on your way here from the ship? And why?
socrates: It was because – as we realized with hindsight – we completely misunderstood the directions given to us by the captain.
hermes: So, when you got the wrong idea of what he meant, despite having listened attentively to every word he said, where did that wrong idea come from? Not from him, presumably . . .
socrates: I see. It must come from within ourselves. It must be a guess. Though, until this moment, it had never even remotely occurred to me that I had been guessing.
hermes: So why would you expect that anything different happens when you do understand someone correctly?
socrates: I see. When we hear something being said, we guess what it means, without realizing what we are doing. That is beginning to make sense to me. — page 239

10.4.2 But what about your direct immediate experience, like touching a water bottle? Is it also a guess?

Yes indeed. All our senses are just electrical currents in our brain which reconstruct reality within us.

socrates: Marvellous! But now – what about objects that we just experience in the natural world. We reach out and touch an object, and hence experience it out there. Surely that is a different kind of knowledge, a kind which – fallible or not – really does come from without, at least in the sense that our own experience is out there, at the location of the object.
hermes: You loved the idea that all those other different kinds of knowledge originate in the same way, and are improved in the same way. Why is ‘direct’ sensory experience an exception? What if it just seems radically different?
socrates: But surely you are now asking me to believe in a sort of all-encompassing conjuring trick, resembling the fanciful notion that the whole of life is really a dream. For it would mean that the sensation of touching an object does not happen where we experience it happening, namely in the hand that touches, but in the mind – which I believe is located somewhere in the brain. So all my sensations of touch are located inside my skull, where in reality nothing can touch while I still live. And whenever I think I am seeing a vast, brilliantly illuminated landscape, all that I am really experiencing is likewise located entirely inside my skull, where in reality it is constantly dark!
hermes: Is that so absurd? Where do you think all the sights and sounds of this dream are located?
socrates: I accept that they are indeed in my mind. But that is my point: most dreams portray things that are simply not there in the external reality. To portray things that are there is surely impossible without some input that does not come from the mind but from those things themselves.
hermes: Well reasoned, Socrates. But is that input needed in the source of your dream, or only in your ongoing criticism of it?
socrates: You mean that we first guess what is there, and then – what? – we test our guesses against the input from our senses?
hermes: Yes.
socrates: I see. And then we hone our guesses, and then fashion the best ones into a sort of waking dream of reality.*
hermes: Yes. A waking dream that corresponds to reality. But there is more. It is a dream of which you then gain control. You do that by controlling the corresponding aspects of the external reality.
socrates: [Gasps.] It is a wonderfully unified theory, and consistent, as far as I can tell. But am I really to accept that I myself – the thinking being that I call ‘I’ – has no direct knowledge of the physical world at all, but can only receive arcane hints of it through flicker and shadows that happen to impinge on my eyes and other senses? And that what I experience as reality is never more than a waking dream, composed of conjectures originating from within myself?
hermes: Do you have an alternative explanation?
socrates: No! And the more I contemplate this one, the more delighted I become. (A sensation of which I should beware! Yet I am also persuaded.) Everyone knows that man is the paragon of animals. But if this epistemology you tell me is true, then we are infinitely more marvellous creatures than that. Here we sit, for ever imprisoned in the dark, almost-sealed cave of our skull, guessing. We weave stories of an outside world – worlds, actually: a physical world, a moral world, a world of abstract geometrical shapes, and so on – but we are not satisfied with merely weaving, nor with mere stories. We want true explanations. So we seek explanations that remain robust when we test them against those flicker and shadows, and against each other, and against criteria of logic and reasonableness and everything else we can think of. And when we can change them no more, we have understood some objective truth. And, as if that were not enough, what we understand we then control. It is like magic, only real. We are like gods!
hermes: Well, sometimes you discover some objective truth, and exert some control as a result. But often, when you think you have achieved any of that, you haven’t.
socrates: Yes, yes. But having discovered some truths, can we not make better guesses and further criticisms and tests, and so understand more and control more, as Xenophanes says?
hermes: Yes. — page 240

Socrates and Plato

David then presents a dialogue between Socrates and Plato in which the second one severely misinterprets the meaning. Plato inclines to idealism, something that Socrates seemed to oppose and David is making fun of it. There isn’t many good flashcards one can make here, so I just leave the best quotes:

Those two overarching concerns are these: we Athenians are concerned above all with improvement; the Spartans seek only – stasis. — page 247
socrates: My guess is this. The very existence of Athens, however peaceful, is a deadly threat to Sparta’s stasis. And therefore, in the long run, the condition for the continued stasis of Sparta (which means its continued existence, as they see it) is the destruction of progress in Athens (which from our perspective would constitute the destruction of Athens). — page 249

10.5.0 Why David could advise not to read original texts of most scientists, even prominent ones like Einstein, Maxwell, Heisenberg and others?

Original texts are full of misconceptions that authors have due to their times. They also, at times, haven’t fully understood the scope of their own theory, or how universally it applies. Finally, their explanation would rarely be as good as of someone for whom it is a main purpose. Thus, David would advise to read modern textbooks, not original texts of scientists (only if one is not a historian of course).

Courses in philosophy place great weight on reading original texts, and commentaries on them, in order to understand the theories that were in the minds of various great philosophers.
This focus on history is odd, and is in marked contrast to all other academic disciplines (except perhaps history itself). For example, in all the physics courses that I took at university, both as an undergraduate and as a graduate student, I cannot recall a single instance where any original papers or books by the great physicists of old were studied or were even on the reading list. Only when a course touched upon very recent discoveries did we ever read the work of their discoverers. So we learned Einstein’s theory of relativity without ever hearing from Einstein; we knew Maxwell, Boltzmann, Schrödinger, Heisenberg and so on only as names. We read their theories in textbooks whose authors were physicists (not historians of physics) who themselves may well never have read the works of those pioneers.
Why? The immediate reason is that the original sources of scientific theories are almost never good sources. How could they be? All subsequent expositions are intended to be improvements on them, and some succeed, and improvements are cumulative. And there is a deeper reason. The originators of a fundamental new theory initially share many of the misconceptions of previous theories. They need to develop an understanding of how and why those theories are flawed and how the new theory explains everything that they explained. But most people who subsequently learn the new theory have quite different concerns. Often they just want to take the theory for granted and use it to make predictions, or to understand some complex phenomenon in combination with other theories. Or they may want to understand nuances of it that have nothing to do with why it is superior to the old theories. Or they may want to improve it. But what they no longer care about is tracking down and definitivel meeting every last objection that would naturally be made by someone thinking in terms of older, superseded theories. There is rarely any reason for scientists to address the obsolete problem-situations that motivated the great scientists of the past. — page 255

🪢 11 — The Multiverse

In this chapter David explains what quantum physics implies if one takes it seriously — painting an astonishing world. Yet, these are just our best explanations that we should take seriously to understand reality.

Disclaimer: David presents a ‘many-universes interpretation’ of quantum theory which is still a minority view among physicists. He explores why is it so in the next chapter (spoiler: bad philosophy). Many-universes is an ‘interpretation’ of quantum physics just as much as dinosaurs are an interpretation of fossils.

Summary

Familiar to us ‘universes’ are an emergent phenomena of the multiverse. They appear to be autonomous, but when studying behavior of the smallest things (like particles), one must invoke multiverse as an explanation.

Within multiverse, any particle is actually a fungible group of infinite particles. All particles are constantly merging and splitting with its multiverse counterparts.

All objects are made of particles, hence any object is actually a fungible group of infinite objects. Entanglement information explains why we don’t constantly observe splitting and merging of bigger objects than particles.

Fungibility explains how laws of physics could be deterministic, yet cause differentiation within the multiverse.

The physical world is a multiverse, and its structure is determined by how information flows in it. In many regions of the multiverse, information flows in quasi-autonomous streams called histories, one of which we call our ‘universe’. Universes approximately obey the laws of classical (pre-quantum) physics. But we know of the rest of the multiverse, and can test the laws of quantum physics, because of the phenomenon of quantum interference. Thus a universe is not an exact but an emergent feature of the multiverse. One of the most unfamiliar and counter-intuitive things about the multiverse is fungibility. The laws of motion of the multiverse are deterministic, and apparent randomness is due to initially fungible instances of objects becoming different. In quantum physics, variables are typically discrete, and how they change from one value to another is a multiversal process involving interference and fungibility. — page 304

You can practice chapter question as flashcards here.

Preconditions and Fungibility

11.1.0 What are the five preconditions that David establishes to explain the multiverse?

Imagine two pieces of paper, perfectly alike, but entirely independent.

No communication between the universes.
The speed of light as a limit for communication within the universe.
The laws of physics are purely deterministic and symmetrical between the universes.
The same laws of physics apply to all the universes within the multiverse.
No one can bring new information, even random one, because everything is determined by past events and laws of physics.

[Phantom zone and a universe are usually different in fictional stories, David makes them alike to fix some physical issues they had.] So let me eliminate those flaw by imagining, for the moment, that the universes are completely imperceptible to each other. Since we are heading towards real physics, let me also retain the speed-of-light limit on communication, and let the laws of physics be universal and symmetrical (i.e. they make no distinction between the universes). Moreover, they are deterministic: nothing random ever happens, which is why the universes have remained alike – so far.
…
So now we have two perfectly parallel, identical universes, each including an instance of our starship, its crew and its transporter, and of the whole of space. Because of the symmetry between them, it is now misleading to call one of them ‘the ordinary universe’ and the other ‘the phantom zone’. So I shall just call them ‘universes’. The two of them together (which comprise the whole of physical reality in the story so far) are the multiverse. Similarly, it is misleading to speak of the ‘original’ object and its ‘doppelgänger’: they are simply the two instances of the object. — page 263

11.2.0 The laws of physics are deterministic, so how could there be parallel universes? How could universes split if they are given exactly the same conditions? Wouldn’t they always remain exactly alike?

The universes can’t split if they are exactly the same and follow deterministic laws of physics, but they can if they are fungible, configurational entities.

11.2.1 What is fungibility?

Means being entirely identical — interchangeable because they are exactly the same.

The term is borrowed from legal terminology, where it refers to the legal fiction that deems certain entities to be identical for purposes such as paying debts. For example, dollar bills are fungible in law, which means that, unless otherwise agreed, borrowing a dollar does not require one to return the specific banknote that one borrowed. Barrels of oil (of a given grade) are fungible too. Horses are not: borrowing someone’s horse means that one has to return that specific horse; even its identical twin will not do. But the physical fungibility I am referring to here is not about deeming. It means being identical, and that is a very different and counter-intuitive property. — page 265

A helpful example can be of a dollar in a bank account, or an energy you produce while biking. Both are configurational entities:

For example, if the balance in your (electronic) bank account is one dollar, and the bank adds a second dollar as a loyalty bonus and later withdraws a dollar in charges, there is no meaning to whether the dollar they withdrew is the one that was there originally or the one that they had added – or is composed of a little of each. It is not merely that we cannot know whether it was the same dollar, or have decided not to care: because of the physics of the situation there really is no such thing as taking the original dollar, nor such a thing as taking the one added subsequently.
Dollars in bank accounts are what may be called ‘configurational entities: they are states or configuration of objects, not what we usually think of as physical objects in their own right. …
Another example of fungible configurational entities in classical physics is amounts of energy: if you pedal your bicycle until you have built up a kinetic energy of ten kilojoules, and then brake until half that energy has been dissipated as heat, there is no meaning to whether the energy dissipated was the first five kilojoules that you had added or the second, or any combination. But it is meaningful that half the energy that was there has been dissipated. — page 266

11.2.2 Are particles also fungible entities?

Yes they are, elementary particles are configurational entities!

It turns out that, in quantum physics, elementary particles are configurational entities too. The vacuum, which we perceive as empty at everyday scales and even at atomic scales, is not really emptiness, but a richly structured entity known as a ‘quantum field’. Elementary particles are higher-energy configuration of this entity: ‘excitations of the vacuum’. So, for instance, the photons in a laser are configuration of the vacuum inside its ‘cavity’. When two or more such excitations with identical attributes (such as energy and spin) are present in the cavity, there is no such thing as which one was there first nor which one will be the next to leave. There is only such a thing as the attributes of any one of them, and how many of them there are. — page 267

11.2.3 What is the difference between fungibility and being exactly the same?

Deterministic laws never can differentiate exactly the same objects. Fungible objects can get differentiated while obeying deterministic laws (like bank account dollars or kilojoules). Photons and matter are fungible in the same way. So when they go through something that has several possible options — they split.

If the two universes of our fictional multiverse are initially fungible, our transporter malfunction can make them acquire different attributes in the same way that a bank’s computer can withdraw one of two fungible dollars and not the other from an account containing two dollars. The laws of physics could, for instance, say that, when the transporter malfunctions, then in one of the universes and not the other there will be a small voltage surge in the transported objects. The laws, being symmetrical, could not possibly specify which universe the surge will take place in. But, precisely because the universes are initially fungible, they do not have to.
It is a rather counter-intuitive fact that if objects are merely identical (in the sense of being exact copies), and obey deterministic laws that make no distinction between them, then they can never become different; but fungible objects, which on the face of it are even more alike, can. This is the first of those weird properties of fungibility that Leibniz never thought of, and which I consider to be at the heart of the phenomena of quantum physics. — page 267

11.3.0 What is diversity within fungibility?

As we’ve seen with bank account dollars they are fungible — exactly the same in every possible way. Yet, we can introduce some level of ‘diversity’ within them. Nonetheless, they would still be fungible.

For instance, when I schedule a transfer to my friend that would be send in a week, all dollars are still fungible, yet some have a different owner — my friend.

This only sounds paradoxical, the problem of describing such phenomena is of language only, physically this is a viable event.

Suppose that your account contains a hundred dollars and you have instructed your bank to transfer one dollar from this account to the tax authority on a specified date in the future. So the bank’s computer now contains a deterministic rule to that effect. Suppose that you have done this because the dollar already belongs to the tax authority. (Say it had mistakenly sent you a tax refund, and has given you a deadline to repay it.) Since the dollars in the account are fungible, there is no such thing as which one belongs to the tax authority and which belong to you. So we now have a situation in which a collection of objects, though fungible, do not all have the same owner! Everyday language struggles to describe this situation: each dollar in the account shares literally all its attributes with the others, yet it is not the case that all of them have the same owner. So, could we say that in this situation they have no owner? That would be misleading, because evidently the tax authority does own one of them and you do own the rest. Could one say that they all have two owners? Perhaps, but only because that is a vague term. Certainly there is no point in saying that one cent of each of the dollars is owned by the tax authority, because that simply runs into the problem that the cents in the account are all fungible too. But, in any case, notice that the problem raised by this ‘diversity within fungibility’ is one of language only. It is a problem of how to describe some aspects of the situation in words. No one finds the situation itself paradoxical: the computer has been instructed to execute definite rules, and there will never be any ambiguity about what will happen as a result. — page 268

Unpredictability, Sphere of Differentiation and Measure

11.4.0 For what three reasons something could appear unpredictable to the observer?

First, that it is actually random, which is never the case because there is no randomness in the laws of physics — they are perfectly deterministic. Second, we don’t know yet exactly what are the factors causing some phenomena — like the creation of knowledge (we don’t know how the human brain works, so we call it unpredictable). Third, is that initially fungible instance of an object become different (as with dollars, photons or observers).

In principle, a phenomenon could appear unpredictable to observers for one or more of three reasons. The first is that it is affected by some fundamentally random (indeterministic) variable. I have excluded that possibility from our story because there are no such variables in real physics. The second, which is at least partly responsible for most everyday unpredictability, is that the factors affecting the phenomenon, though deterministic, are either unknown or too complex to take account of. (This is especially so when they involve the creation of knowledge, as I discussed in Chapter 9.) The third – which had never been imagined before quantum theory – is that two or more initially fungible instances of the observer become different. That is what those transporter-induced jolts bring about, and it makes their outcomes strictly unpredictable despite being described by deterministic laws of physics. — page 269

11.5.0 Explain how the quantum randomness works.

The double-slit and Mach–Zehnder interferometer experiments imply that with every photon we fire, an enormous amount (seems like infinite) of its instances that we cannot observe are fired as well, and they influence each other in subtle ways of quantum interference.

When firing a single photon through a half-silvered mirror we have a 50% chance that it would bounce off. But since infinite number of instances are fired simultaneously with it, then both possibilities: bounced, not bounced must have happened. What we can’t predict, is which one out of those infinite photons fired will we observe, and hence, will it bounce off or not. (None of the formulas we try make consistently accurate predictions.) We know that both things have happened, what we don’t know is which of the two we’ll get to observe.

Now suppose that scientists on the starship know about the multiverse and understand the physics of the transporter. (Though note that we have not yet given them a way of discovering those things.) Then they know that, when they run the transporter, an infinite number of fungible instances of themselves, all sharing the same history, are doing so at the same time. They know that a voltage surge will occur in half the universes in that history, which means that it will split into two histories of equal measure. Hence they know that, if they use a voltmeter capable of detecting the surge, half of the instances of themselves are going to find that it has recorded one, and the other half are not. But they also know that it is meaningless to ask (not merely impossible to know) which event they will experience. Consequently they can make two closely related predictions. One is that, despite the perfect determinism of everything that is happening, nothing can reliably predict for them whether the voltmeter will detect a surge.
The other prediction is simply that the voltmeter will record a surge with probability one-half. Thus the outcomes of such experiments are subjectively random (from the perspective of any observer) even though everything that is happening is completely determined objectively. This is also the origin of quantum-mechanical randomness and probability in real physics: it is due to the measure that the theory provides for the multiverse, which is in turn due to what kinds of physical processes the theory allows and forbids.
Notice that when a random outcome (in this sense) is about to happen, it is a situation of diversity within fungibility: the diversity is in the variable ‘what outcome they are going to see’. The logic of the situation is the same as in cases like that of the bank account I discussed above, except that this time the fungible entities are people. They are fungible, yet half of them are going to see the surge and the other half not. — page 277

11.6.0 What is the sphere of differentiation?

After the differentiating event the universes would ‘split’. How quickly the difference can spread within the rest of the universe depends on the speed of communication between this differentiating phenomena and the rest of the universe, obviously its limit would be the speed of light.

Typically, the region in which the universes are different will then grow. For instance, when the couple decide to marry [obviously there are universes where they decide otherwise], they send messages to their home planets announcing this. When the messages arrive, the two instances of each of those planets become different. Previously only the two instances of the starship were different, but soon, even before anyone broadcasts it intentionally, some of the information will have leaked out. For instance, people in the starship are moving differently in the two universes as a result of the marriage decision, so light bounces off them differently and some of it leaves the starship through portholes, making the two universes slightly different wherever it goes. The same is true of heat radiation (infra-red light), which leaves the starship through every point on the hull. Thus, starting with the voltage happening in only one universe, a wave of differentiation between the universes spreads in all directions through space. Since information travelling in either universe cannot exceed the speed of light, nor can the wave of differentiation. And since, at its leading edge, it mostly travels at or near that speed, differences in the head start that some directions have over others will become an ever smaller proportion of the total distance travelled, and so the further the wave travels the more nearly spherical it becomes. So I shall call it a ‘sphere of differentiation’. — page 273

11.6.1 Would a radio signal sent from a starship affect nearly all Earth atoms (even if slightly), or just strictly some (leaving most unchanged)? Is there the smallest amount of change, or is it continuous without an end?

This is a question of quantum system vs classical one. Classical systems are continuous, there is no smallest amount of something. On the other hand, quantum systems have a smallest unit, called quanta. Quantum theory is our leading physical model of the world. Hence, there would be some atoms that would be strictly unchanged by the radio signal.

However, although it may seem intuitively reasonable that news of the marriage leaves most things unchanged, there is a different common-sense intuition that seems to prove that it must change everything, if only slightly. Consider what happens when the news reaches a planet – say, in the form of pulse of photons from a communication laser. Even before any human consequences, there is the physical impact of those photons, which one might expect to impart momentum to every atom exposed to the beam – which will be every atom in something like that half of the surface of the planet which is facing the beam. Those atoms would then vibrate a little differently, affecting the atoms below through interatomic forces. As each atom affected others, the effect would spread rapidly through the planet. Soon, every atom in the planet would have been affected – though most of them by unimaginably tiny amounts. Nevertheless, however small such an effect was, it would be enough to break the fungibility between each atom and its other-universe counterpart. Hence it would seem that nothing would be left fungible after the wave of differentiation had passed.
These two opposite intuitions reflect the ancient dichotomy between the discrete and the continuous. The above argument – that everything in the sphere of differentiation must become different – depends on the reality of extremely small physical changes – changes that would be many orders of magnitude too small to be measurable. The existence of such changes follows inexorably from the explanations of classical physics, because in classical physics most fundamental quantities (such as energy) are continuously variable. The opposing intuition comes from thinking about the world in terms of information processing, and hence in terms of discrete variables such as the contents of people’s memories. Quantum theory adjudicates this conflict in favour of the discrete. For a typical physical quantity, there is a smallest possible change that it can undergo in a given situation. For instance, there is a smallest possible amount of energy that can be transferred from radiation to any particular atom. The atom cannot absorb any less than that amount, which is called a ‘quantum’ of energy. Since this was the first distinctive feature of quantum physics to be discovered, it gave its name to the field Let us incorporate it into our fictional physics as well.
Hence it is not the case that all the atoms on the surface of the planet are changed by the arrival of the radio message. In reality, the typical response of a large physical object to very small influence is that most of its atoms remain strictly unchanged, while, to obey the conservation laws, a few exhibit a discrete, relatively large change of one quantum. — page 273

11.7.0 All physical forces diminish in their effects with distance, but one. What force is it?

Knowledge!

The effects of a wave of differentiation usually diminish rapidly with distance – simply because physical effects in general do. The sun, from even a hundredth of a light year away, looks like a cold, bright dot in the sky. It barely affects anything. At a thousand light years, nor does a supernova. Even the most violent of quasar jets, when viewed from a neighbouring galaxy, would be little more than an abstract painting in the sky. There is only one known phenomenon which, if it ever occurred, would have effects that did not fall off with distance, and that is the creation of a certain type of knowledge, namely a beginning of infinity. Indeed, knowledge can aim itself at a target, travel vast distances having scarcely any effect, and then utterly transform the destination. — page 275

Knowledge should be perceived as a physical force, as it can literally transform the reality. When looking at the Earth what force caused the skyscrapers to appear? It’s not just humans, it is humans who knew how to do it — humans that were equipped with knowledge.

11.8.0 How could our ‘fictional’ laws of physics be deterministic (not random), universal (same across all universes) and adhere to the speed of light communication limit?

There must be more universes! 🤯

If ‘fungibility’ of a universe has any limit (like being able to split only one or two times), then the information that the split has happened must spread instantly across the universe, so as to not go over the limit. But we have put speed of light limit to communication! To adhere to our constraints we must add universes to our fungible ‘universe bank account’. In fact, it seems reasonable to say that there is an infinite number of them.

Now, as I have explained, our imaginary laws of physics which say that a voltage surge happens ‘in one universe but not the other’ cannot be deterministic unless the universes are fungible. So, what happens when the transporter is used again, after the universes are no longer fungible? Imagine a second starship, of the same type as the firs and far away. What happens if the second starship runs its transporter immediately after the first one did?
One logically possible answer would be that nothing happens – in other words, the laws of physics would say that, once the two universes are different, all transporters just work normally and never produce a voltage surge again. However, that would also provide a way of communicating faster than light, albeit unreliably and only once. You set up a voltmeter in the transporter room and run the transporter. If the voltage surges, you know that the other starship, however far away, has not yet run its transporter (because, if it had, that would have put a permanent end to such surges everywhere). The laws governing the real multiverse do not allow information to flo in that way. If we want our fictional laws of physics to be universal from the inhabitants’ point of view, the second transporter must do exactly what the first one did. It must cause a voltage surge in one universe and not in the other. But in that case something must determine which universe the second surge will happen in. ‘In one universe but not the other’ is no longer a deterministic specification Also, a surge must not happen if the transporter is run only in the other universe. That would constitute inter-universe communication. It must depend on both instances of the transporter being run simultaneously. Even that could allow some inter-universe communication, as follows. In the universe where a surge has once happened, run the transporter at a prearranged time and observe the voltmeter. If no surge happens, then the transporter in the other universe is switched off. So we are at an impasse. It is remarkable how much subtlety there can be in the apparently straightforward, binary distinction between ‘same’ and different’ – or between ‘affected’ and ‘unaffected’. In the real quantum theory, too, the prohibitions on inter-universe communication and faster-than-light communication are closely connected.
There is a way – I think it is the only way – to meet simultaneously the requirements that our fictional laws of physics be universal and deterministic, and forbid faster-than-light and inter-universe communication: more universes. Imagine an uncountably infinite number of them, initially all fungible. The transporter causes previously fungible ones to become different, as before; but now the relevant law of physics says, ‘The voltage surges in half the universes in which the transporter is used.’ So, if the two starships both run their transporters, then, after the two spheres of differentiation have overlapped, there will be universes of four different kinds: those in which a surge happened only in the first starship, only in the second, in neither, and in both. In other words, in the overlap region there are four different histories, each taking place in one quarter of the universes. — page 275

11.8.1 But probabilities don’t work with infinities! (You should say if you remember 8th chapter.) So what does it mean for something to happen in ‘half of the universes’?

Measure is a way to assign meaning to proportions and average for infinities, think of a ruler with infinite number of points in it.

Our fictional theory has not provided enough structure in its multiverse to give a meaning to ‘half the universes’, but the real quantum theory does. As I explained in Chapter 8, the method that a theory provides for giving a meaning to proportions and averages for infinite sets is called a measure. A familiar example is that classical physics assigns lengths to infinite sets of points arranged in a line. Let us suppose that our theory provides a measure for universes.
Now we are allowed storylines such as the following. In the universes in which the couple married, they spend their honeymoon on a human colonized planet that the starship is visiting. As they are teleporting back up, the voltage surge in half those universes causes someone’s electronic notepad to play a voice message suggesting that one of the newlyweds has already been unfaithful. This sets off a chain of events that ends in divorce. So now our original collection of fungible universes contains three different histories: in one, comprising half the original set of universes, the couple in question are still single; in the second, comprising a quarter of the original set, they are married; and in the third, comprising the remaining quarter, they are divorced.
Thus the three histories do not occupy equal proportions of the multiverse. There are twice as many universes in which the couple never married as there are universes in which they divorced. — page 276

Splitting and Merging of the Universes and Particles

11.9.0 What is an entanglement information?

We cannot interact with objects in other universes, only with ours. The information specifying in which universe we are and which objects are perceptible to us is an entanglement information.

what exactly is the difference between the instance of you that I can interact with and the ones that are imperceptible to me? The latter are ‘in other universes’ – but, remember, universes consist only of the objects in them, so that amounts only to saying I can see the ones that I can see. The upshot is that our laws of physics must also say that every object carries within it information about which instances of it could interact with which instances of other objects (except when the instances are fungible, when there is no such thing as ‘which’). Quantum theory describes such information. It is known as entanglement information. — page 280

11.9.1 What is the aspect of the multiverse that depends on the entanglement information? How does it work?

If universes just split, then we have imagined a stupendously complex universe, that makes no better predictions than just saying that certain events are random (like firing a photon through a half-silvered mirror). But there is one aspect of the multiverse that makes all this construction worth it — merging of the universes. This is exactly what happens in the Mach-Zehnder interferometer experiment.

Whether splitted universes merge back depends on the entanglement information and the sphere of differentiation not spreading — otherwise it is too hard to perform the merge.

So our rule, in short, is that interference can happen only in objects that are unentangled with the rest of the world. This is why, in the interference experiment, the two applications of the transporter have to be ‘in quick succession’. (Alternatively, the object in question has to be sufficiently well isolated for its voltages not to affect its surroundings.) — page 284

This is exactly what happens during the quantum computation. Universes split to perform different parts of the computation, only then to merge and give single answer. Biggest challenge of quantum computers is how to avoid sphere of differentiation growing, and thus, spoiling the computation.

Imagine you have to check each number from one to a million to find an answer. With quantum computers you can split your workload into 10 parts (in real-life it’s obviously more) and then find an answer 10 times faster:

11.9.2 What happens if quantum object gets entangled (i.e. sphere of differentiation grows beyond desired level)?

Universes wouldn’t merge back, they would split further. One must accurately control all affected objects for merge to happen.

Once the object is entangled with the rest of the world in regard to the values X and Y, no operation on the object alone can create interference between those values. Instead, the histories are merely split further, in the usual way:

When two or more values of a physical variable have differently affected something in the rest of the world, knock-on effects typically continue indefinitely, as I have described, with a wave of differentiation entangling more and more objects. If the differential effects can all be undone, then interference between those original values becomes possible again; but the laws of quantum mechanics dictate that undoing them requires fine control of all the affected objects, and that rapidly becomes infeasible. The process of its becoming infeasible is known as decoherence. In most situations, decoherence is very rapid, which is why splitting typically predominates over interference, and why interference – though ubiquitous on microscopic scales – is quite hard to demonstrate unambiguously in the laboratory. — page 284

11.10.0 Describe the Mach-Zehnder interferometer and what it implies.

Mach-Zehnder interferometer is done with one photon and half-silvered mirrors (mirrors that pass only half of light).

When a photon strikes such a mirror, it bounces off in half the universes, and passes straight through in the other half, as shown on next page:

The attributes of travelling in the X or Y directions behave analogously to the two voltages X and Y in our fictitious multiverse. So passing through the semi-silvered mirror is the analogue of the transformation above. And when the two instances of a single photon, travelling in directions X and Y, strike the second semi-silvered mirror at the same time, they undergo the transformation , which means that both instances emerge in the direction X: the two histories rejoin. To demonstrate this, one can use a set-up known as a ‘Mach-Zehnder interferometer’, which performs those two transformations (splitting and interference) in quick succession:

The two ordinary mirrors (the black sloping bars) are merely there to steer the photon from the first to the second semi-silvered mirror.
If a photon is introduced travelling rightwards (X) after the first mirror instead of before as shown, then it appears to emerge randomly, rightwards or downwards, from the last mirror (because then X⇒ X/Y, happens there). The same is true of a photon introduced travelling downwards (Y) after the first mirror. But a photon introduced as shown in the diagram invariably emerges rightwards, never downwards. By doing the experiment repeatedly with and without detectors on the paths, one can verify that only one photon is ever present per history, because only one of those detectors is ever observed to fire during such an experiment. Then, the fact that the intermediate histories X and Y both contribute to the deterministic final outcome X makes it inescapable that both are happening at the intermediate time. — page 285

11.11.0 How splitting and merging of the universes works for particles?

Single particles are constantly splitting and merging. Only for big objects it is a rare event, for particles it is a norm.

In the real multiverse, there is no need for the transporter or any other special apparatus to cause histories to differentiate and to rejoin. Under the laws of quantum physics, elementary particles are undergoing such processes of their own accord, all the time. Moreover, histories may split into more than two – often into many trillions – each characterized by a slightly different direction of motion or difference in other physical variables of the elementary particle concerned. Also, in general the resulting histories have unequal measures. So let us now dispense with the transporter in the fictional multiverse too.
The rate of growth in the number of distinct histories is quite mind-boggling – even though, thanks to interference, there is now a certain amount of spontaneous rejoining as well. — page 287

11.11.1 What this implies?

Particles don’t retain their identities.

Because of this rejoining, the flow of information in the real multiverse is not divided into strictly autonomous subflows – branching, autonomous histories. Although there is still no communication between histories (in the sense of message-sending), they are intimately affecting each other, because the effect of interference on a history depends on what other histories are present.
Not only is the multiverse no longer perfectly partitioned into histories, individual particles are not perfectly partitioned into instances. For example, consider the following interference phenomenon, where X and Y now represent different values of the position of a single particle:

How instances of a particle lose their identity during interference. Has the instance of the particle at X stayed at X or moved to Y? Has the instance of the particle at Y returned to Y or moved to X?
Because these two groups of instances of the particle, initially at different positions, have gone through a moment of being fungible, there is no such thing as which of them has ended up at which final position. This sort of interference is going on all the time, even for a single particle in a region of otherwise empty space. So there is in general no such thing as the ‘same’ instance of a particle at different times. — page 287

11.11.2 What does this mean for the particle’s speed?

It means that there is no such thing as speed, because there is no such thing as a “particular instance”.

For the same reason, there is no such thing as the speed of one instance of the particle at a given location. Speed is defined as distance travelled divided by time taken, but that is not meaningful in situations where there is no such thing as a particular instance of the particle over time. Instead, a collection of fungible instances of a particle in general have several speeds – meaning that in general they will do different things an instant later. (This is another instance of ‘diversity within fungibility’.) — page 289

11.12.0 How Heisenberg uncertainty principle is explained through fungibility?

Fungible objects are defined as objects that are exactly the same. Yet, there might be diversity within them, while they are still fungible. This seems paradoxical, but as David shows in the dollar bank account example it is a problem of language only, not physics.

Objects can be fungible with some of their attributes being different.

Uncertainty principle states exactly that — an electron is a fungible group of electrons with different variables like speed and position.

Because particles constantly merge and split they ‘lose’ their identity — they are a fungible group.

For the same reason, there is no such thing as the speed of one instance of the particle at a given location. Speed is defined as distance travelled divided by time taken, but that is not meaningful in situations where there is no such thing as a particular instance of the particle over time. Instead, a collection of fungible instances of a particle in general have several speeds – meaning that in general they will do different things an instant later. (This is another instance of ‘diversity within fungibility’.)
Not only can a fungible collection with the same position have different speeds, a fungible group with the same speed can have different positions. Furthermore, it follows from the laws of quantum physics that, for any fungible collection of instances of a physical object, some of their attributes must be diverse. This is known as the ‘Heisenberg uncertainty principle’, after the physicist Werner Heisenberg, who deduced the earliest version from quantum theory.
Hence, for instance, an individual electron always has a range of different locations and a range of different speeds and directions of motion. — page 289

11.12.1 Explain then, how behavior of a particle looks like.

An electron is subjectively a particle, yet as a multiversal object it is a fungible group of particles, that resemble waves.

Hence, for instance, an individual electron always has a range of different locations and a range of different speeds and directions of motion. As a result, its typical behaviour is to spread out gradually in space. Its quantum-mechanical law of motion resembles the law governing the spread of an ink blot – so if it is initially located in a very small region it spreads out rapidly, and the larger it gets the more slowly it spreads. The entanglement information that it carries ensures that no two instances of it can ever contribute to the same history. (Or, more precisely, at times and places where there are histories, it exists in instances which can never collide.) If a particle’s range of speeds is centred not on zero but on some other value, then the whole of the ‘ink-blot’ moves, with its centre obeying approximately the laws of motion in classical physics. In quantum physics this is how motion, in general, works. — page 289

11.12.2 How this explains the structure of an atom?

The spreading out ink blot of an electron (that is negatively charged) is attracted to the proton that is positively charged. The further an electron is from the proton, the more energy it has. The energy levels have ‘ranges’ that usually define orbits around proton which are discrete. {I am not sure about this, I would have to check with David. — Source}

Even though electron ink blot should spread out with time, it doesn’t because proton attracts it. This is how atoms and solid matter are formed. The Heisenberg uncertainty principle leads to a ‘cloud-like’ behavior of electrons, so there is not a lot of space, hence, this is why two atoms can’t (only in rare cases they can) go through one another! If there was a lot of space between they would’ve easily penetrate each other.

This explains how particles in the same history can be fungible too, in something like an atomic laser. Two ‘ink-blot’ particles, each of which is a multiversal object, can coincide perfectly in space, and their entanglement information can be such that no two of their instances are ever at the same point in the same history.
Now, put a proton into the middle of that gradually spreading cloud of instances of a single electron. The proton has a positive charge, which attracts the negatively charged electron. As a result, the cloud stops spreading when its size is such that its tendency to spread outwards due to its uncertainty-principle diversity is exactly balanced by its attraction to the proton. The resulting structure is called an atom of hydrogen.
…
Thanks to the strong internal interference that it is continuously undergoing, a typical electron is an irreducibly multiversal object, and not a collection of parallel-universe or parallel-histories objects. That is to say, it has multiple positions and multiple speeds without being divisible into autonomous sub-entities each of which has one speed and one position. Even different electrons do not have completely separate identities. So the reality is an electron field throughout the whole of space, and disturbances spread through this field as waves, at the speed of light or below. This is what gave rise to the often-quoted misconception among the pioneers of quantum theory that electrons (and likewise all other particles) are ‘particles and waves at the same time’. There is a field (or ‘waves’) in the multiverse for every individual particle that we observe in a particular universe. — page 290

Multiverse Intuition and Remaining Details

11.13.0 What is the metaphor that David uses to explain the multiverse?

A history is part of the multiverse in the same sense that a geological stratum is part of the Earth’s crust. One history is distinguished from the others by the values of physical variables, just as a stratum is distinguished from others by its chemical composition and by the types of fossils found in it and so on. A stratum and a history are both channels of information flow. They preserve information because, although their contents change over time, they are approximately autonomous – that is to say, the changes in a particular stratum or history depend almost entirely on conditions inside it and not elsewhere. It is because of that autonomy that a fossil found today can be used as evidence of what was present when that stratum was formed. Similarly, it is why, within a history, using classical physics, one can successfully predict some aspects of the future of that history from its past.
…
However, there is one big difference between the ways in which strata and histories emerge from their respective underlying phenomena. Although not every atom in the Earth’s crust can be unambiguously assigned to a particular stratum, most of the atoms that form a stratum can. In contrast, every atom in an everyday object is a multiversal object, not partitioned into nearly autonomous instances and nearly autonomous histories, yet everyday objects such as starships and betrothed couples, which are made of such particles, are partitioned very accurately into nearly autonomous histories with exactly one instance, one position, one speed of each object in each history. — page 292

11.14.0 If particles are irreducible multiversal objects that undergo constant merging and splitting, why humans and planets don’t?

Things we observe in our daily life are emergent phenomena of a multiverse. They seem autonomous because they mostly are, since interference is suppressed due to entanglement.

My ‘succession of speculations’ was based on universes, and on instances of objects, and then on corrections to those ideas in order to describe the multiverse. But the real multiverse is not ‘based on’ anything, nor is it a correction to anything. Universes, histories, particles and their instances are not referred to by quantum theory at all – any more than are planets, and human beings and their lives and loves. Those are all approximate, emergent phenomena in the multiverse.
…
every atom in an everyday object is a multiversal object, not partitioned into nearly autonomous instances and nearly autonomous histories, yet everyday objects such as starships and betrothed couples, which are made of such particles, are partitioned very accurately into nearly autonomous histories with exactly one instance, one position, one speed of each object in each history. That is because of the suppression of interference by entanglement. As I explained, interference almost always happens either very soon after splitting or not at all. That is why the larger and more complex an object or process is, the less its gross behaviour is affected by interference. At that ‘coarse-grained’ level of emergence, events in the multiverse consist of autonomous histories, with each coarse-grained history consisting of a swathe of many histories differing only in microscopic details but affecting each other through interference. Spheres of differentiation tend to grow at nearly the speed of light, so, on the scale of everyday life and above, those coarse-grained histories can justly be called ‘universes’ in the ordinary sense of the word. Each of them somewhat resembles the universe of classical physics. And they can usefully be called ‘parallel’ because they are nearly autonomous. To the inhabitants, each looks very like a single-universe world. — page 292

11.15.0 Explain how quantum computers work.

Some of my own research in physics has been concerned with the theory of quantum computers. These are computers in which the information-carrying variables have been protected by a variety of means from becoming entangled with their surroundings. This allows a new mode of computation in which the flow of information is not confined to a single history. In one type of quantum computation, enormous numbers of different computations, taking place simultaneously, can affect each other and hence contribute to the output of a computation. This is known as quantum parallelism.
In a typical quantum computation, individual bits of information are represented in physical objects known as ‘qubits’ – quantum bits – of which there is a large variety of physical implementations but always with two essential features. First, each qubit has a variable that can take one of two discrete values, and, second, special measures are taken to protect the qubits from entanglement – such as cooling them to temperatures close to absolute zero. A typical algorithm using quantum parallelism begins by causing the information-carrying variables in some of the qubits to acquire both their values simultaneously. Consequently, regarding those qubits as a register representing (say) a number, the number of separate instances of the register as a whole is exponentially large: two to the power of the number of qubits. Then, for a period, classical computations are performed, during which waves of differentiation spread to some of the other qubits – but no further, because of the special measures that prevent this. Hence, information is processed separately in each of that vast number of autonomous histories. Finally, an interference process involving all the affected qubits combines the information in those histories into a single history. Because of the intervening computation, which has processed the information, the final state is not the same as the initial one, as in the simple interference experiment I discussed above:
…
Only certain types of parallel computation can be performed with the help of the multiverse in this way. They are the ones for which the mathematics of quantum interference happens to be just right for combining into a single history the information that is needed for the final result.
In such computations, a quantum computer with only a few hundred qubits could perform far more computations in parallel than there are atoms in the visible universe. — page 295

FoR: 9.1.0 What is a quantum computer? What makes it a distinctly different paradigm of computation?19

FoR: 9.8.0 How does Shor’s algorithm work? What does it do?20

FoR: 9.8.1 What are the implications of Shor’s algorithm on the interpretations of the quantum interference?21

11.16.0 Why when a large object is influenced by something sufficiently small an object is usually strictly unaffected?

It is because an object is a fungible group with slight differentiation within the group, most small influences are not enough to differentiate the group.

Consider a mirror that is actually a fungible group of mirrors. Imagine the group having a simple differentiation into only 5 groups of vibrational quanta energies: μ-2; μ-1; μ; μ+1; μ+2. When a photon with one quanta of energy hits it, the new energy of mirrors are: μ-1; μ; μ+1; μ+2; μ+3. Four out of five of such versions of mirrors existed before within the fungible mirror we observe in the first place, so for them no change happens. Only the fifth possibility of μ+3 hasn’t existed before, and hence, we’ll observe effect only for it.

Yet in reality, this is more like trillionth of a trillionth mirror, so we ‘never’ observe a change.

I mentioned above that, when a large object is affected by a small influence, the usual outcome is that the large object is strictly unaffected. I can now explain why. For example, in the Mach–Zehnder interferometer, shown earlier, two instances of a single photon travel on two different paths. On the way, they strike two different mirrors. Interference will happen only if the photon does not become entangled with the mirrors – but it will become entangled if either mirror retains the slightest record that it has been struck (for that would be a differential effect of the instances on the two different paths). Even a single quantum of change in the amplitude of the mirror’s vibration on its supports, for instance, would be enough to prevent the interference (the subsequent merging of the photon’s two instances). [Yet we don’t observe it.]
…
Remember that, at a sufficiently fine level of detail, what we crudely see as a single history of the mirror, resting passively or vibrating gently on its supports, is actually a vast number of histories with instances of all its atoms continually splitting and rejoining. In particular, the total energy of the mirror takes a vast number of possible values around the average, ‘classical’ one. Now, what happens when a photon strikes the mirror, changing that total energy by one quantum?
Oversimplifying for a moment, imagine just five of those countless instances of the mirror, with each instance having a different vibrational energy ranging from two quanta below the average to two quanta above it. Each instance of the photon strikes one instance of the mirror and imparts one additional quantum of energy to it. So, after that impact, the average energy of the instances of the mirror will have increased by one quantum, and there will now be instances with energies ranging from one quantum below the old average to three above. But since, at this fine level of detail, there is no autonomous history associated with any of those values of the energy, it is not meaningful to ask whether an instance of the mirror with a particular energy after the impact is the same one that previously had that energy. The objective physical fact is only that, of the five instances of the mirror, four have energies that were present before, and one does not. Hence, only that one – whose energy is three quanta higher than the previous average – carries any record of the impact of the photon. And that means that in only one-fifth of the universes in which the photon struck has the wave of differentiation spread to the mirror, and only in those will subsequent interference between instances of that photon that have or have not hit the mirror be suppressed.
With realistic numbers, that is more like one in a trillion trillion which means that there is only a probability of one in a trillion trillion that interference will be suppressed. This is considerably lower than the probability that the experiment will give inaccurate results due to imperfect measuring instruments, or that it will be spoiled by a lightning strike. — page 297

11.17.0 How discrete changes happen according to quantum physics? Consider an atom absorbing a photon with its energy.

Now let us look at the arrival of that single quantum of energy, to see how that discrete change can possibly happen without any discontinuity. Consider the simplest possible case: an atom absorbs a photon, including all its energy. This energy transfer does not take place instantaneously. (Forget anything that you may have read about ‘quantum jumps’: they are a myth.) There are many ways in which it can happen but the simplest is this. At the beginning of the process, the atom is in (say) its ‘ground state’, in which its electrons have the least possible energy allowed by quantum theory. That means that all its instances (within the relevant coarse-grained history) have that energy. Assume that they are also fungible. At the end of the process, all those instances are still fungible, but now they are in the ‘excited state’, which has one additional quantum of energy. What is the atom like halfway through the process? Its instances are still fungible, but now half of them are in the ground state and half in the excited state. It is as if a continuously variable amount of money changed ownership gradually from one discrete owner to another.
This mechanism is ubiquitous in quantum physics, and is the general means by which transitions between discrete states happen in a continuous way. In classical physics, a ‘tiny effect’ always means a tiny change in some measurable quantities. In quantum physics, physical variables are typically discrete and so cannot undergo tiny changes. Instead, a ‘tiny effect’ means a tiny change in the proportions that have the various discrete attributes. — page 298

💩 12 — A Physicist’s History of Bad Philosophy

If multiverse is the only satisfactory explanation of the quantum physics experiments, why only a minority of physicist’s have embraced it?

Bad philosophy is the answer. Specifically, explanationless science.

Summary

Explanationless science is impossible, for we always have implicit theories of the world to navigate life. Popper has shown it: all observation is theory laden. Explanationless science is actually about hiding your implicit theories. Keeping them immune from criticism, hence delaying the growth of knowledge.

Therefore most ‘explanationless’ studies would just reveal correlations (for causation requires an explanation), and give implicit support, to our implicit, biased theories of the world. By giving such ‘rational basis’ to our parochial ‘rules of thumb’ theories of the world we get **further imprisoned in them. Accepting such nonsense as that slavery is genetically determined. Explanationless science makes it easy to fool ourselves.

Yet, bad philosophy actively prevents growth of knowledge, not merely delays it. Explanationless science incentivizes scientists to make mistakes in observation and its ‘suggestive interpretation’ because it yields more exciting results. Thus, it actively promotes error creation — the opposite of good science.

The best way to oppose explanationless science is through bold, willful progress.

You can practice chapter questions as flashcards here.

Bad Philosophy and Quantum Physics

12.1.0 What was the ‘rule of thumb’ that aligned Shrödinger’s and Heisenberg’s theories of quantum physics?

Although Schrödinger’s and Heisenberg’s theories seemed to describe very dissimilar worlds, neither of which was easy to relate to existing conceptions of reality, it was soon discovered that, if a certain simple rule of thumb was added to each theory, they would always make identical predictions. Moreover, these predictions turned out to be very successful.
With hindsight, we can state the rule of thumb like this: whenever a measurement is made, all the histories but one cease to exist. The surviving one is chosen at random, with the probability of each possible outcome being equal to the total measure of all the histories in which that outcome occurs. — page 307

12.1.1 What was the impact of this rule of thumb?

At that point, disaster struck. Instead of trying to improve and integrate those two powerful but slightly flawed explanatory theories, and to explain why the rule of thumb worked, most of the theoretical-physics community retreated rapidly and with remarkable docility into instrumentalism. If the predictions work, they reasoned, why worry about the explanation? So they tried to regard quantum theory as being nothing but a set of rules of thumb for predicting the observed outcomes of experiments, saying nothing (else) about reality. This move is still popular today, and is known to its critics (and even to some of its proponents) as the ‘shut-up-and-calculate interpretation of quantum theory’.
This meant ignoring such awkward facts as (1) the rule of thumb was grossly inconsistent with both theories; hence it could be used only in situations where quantum effects were too small to be noticed. Those happened to include the moment of measurement (because of entanglement with the measuring instrument, and consequent decoherence, as we now know). And (2) it was not even self-consistent when applied to the hypothetical case of an observer performing a quantum measurement on another observer. And (3) both versions of quantum theory were clearly describing some sort of physical process that brought about the outcomes of experiments. Physicists, both through professionalism and through natural curiosity, could hardly help wondering about that process. But many of them tried not to. Most of them went on to train their students not to. This counteracted the scientific tradition of criticism in regard to quantum theory. — page 307

12.2.0 What is the definition of a bad philosophy?

Let me define ‘bad philosophy’ as philosophy that is not merely false, but actively prevents the growth of other knowledge. — page 307

12.3.0 In what sense an electron is both a wave and a particle under the multiverse theory?

This idea is mostly used under the Copenhagen interpretation of quantum physics, which is wrong. Yet, this can make sense under the multiverse theory. It would apply not only to electrons but to all matter.

‘particle–wave duality’: the photon is both an extended (non-zero volume) and a localized (zero-volume) object at the same time, and one can choose to observe either attribute but not both. Often this is expressed in the saying ‘It is both a wave and a particle simultaneously.’ Ironically, there is a sense in which those words are precisely true: in that experiment the entire multiversal photon is indeed an extended object (wave), while instances of it (particles, in histories) are localized. Unfortunately, that is not what is meant in the Copenhagen interpretation. — page 309

12.4.0 What is the Copenhagen interpretation of quantum physics?

It explains the quantum physics experiments through consciousness and observation. Shrödinger’s equation under it describes potential places of a particle appearing. It explains that a wave of ‘potentialities’ collapses into a single particle because we observe it. Hence consciousness impacts the behavior of objects at a quantum level.

The physicist Niels Bohr (another of the pioneers of quantum theory) then developed an ‘interpretation’ of the theory which later became known as the ‘Copenhagen interpretation’. It said that quantum theory, including the rule of thumb, was a complete description of reality. Bohr excused the various contradictions and gaps by using a combination of instrumentalism and studied ambiguity. He denied the ‘possibility of speaking of phenomena as existing objectively’ – but said that only the outcomes of observations should count as phenomena. He also said that, although observation has no access to ‘the real essence of phenomena’, it does reveal relationships between them, and that, in addition, quantum theory blurs the distinction between observer and observed. As for what would happen if one observer performed a quantum-level observation on another, he avoided the issue – which became known as the ‘paradox of Wigner’s friend’, after the physicist Eugene Wigner.
In regard to the unobserved processes between observations, where both Schrödinger’s and Heisenberg’s theories seemed to be describing a multiplicity of histories happening at once, Bohr proposed a new fundamental principle of nature, the ‘principle of complementarity’. It said that accounts of phenomena could be stated only in ‘classical language’ – meaning language that assigned single values to physical variables at any one time – but classical language could be used only in regard to some variables, including those that had just been measured. One was not permitted to ask what values the other variables had. Thus, for instance, in response to the question ‘Which path did the photon take?’ in the Mach–Zehnder interferometer, the reply would be that there is no such thing as which path when the path is not observed. In response to the question ‘Then how does the photon know which way to turn at the final mirror, since this depends on what happened on both paths?’, the reply would be an equivocation called ‘particle–wave duality’: the photon is both an extended (non-zero volume) and a localized (zero-volume) object at the same time, and one can choose to observe either attribute but not both. Often this is expressed in the saying ‘It is both a wave and a particle simultaneously.’ Ironically, there is a sense in which those words are precisely true: in that experiment the entire multiversal photon is indeed an extended object (wave), while instances of it (particles, in histories) are localized. Unfortunately, that is not what is meant in the Copenhagen interpretation. There the idea is that quantum physics defies the very foundations of reason: particles have mutually exclusive attributes, period. And it dismisses criticisms of the idea as invalid because they constitute attempts to use ‘classical language’ outside its proper domain (namely describing outcomes of measurements).
Later, Heisenberg called the values about which one was not permitted to ask potentialities, of which only one would become actual when a measurement was completed. How can potentialities that do not happen affect actual outcomes? That was left vague. What caused the transition between ‘potential’ and ‘actual’? The implication of Bohr’s anthropocentric language – which was made explicit in most subsequent presentations of the Copenhagen interpretation – was that the transition is caused by human consciousness. Thus consciousness was said to be acting at a fundamental level in physics. — page 308

12.4.1 What is the brief criticism of the Copenhagen interpretation that David gives?

The main critique is that Copenhagen interpretation focuses on the human observation as a key to experimental results. Additionally, it abandons the basics of reason by giving mutually exclusive variables to the same object.

For decades, various versions of all that were taught as fact vagueness, anthropocentrism, instrumentalism and all – in university physics courses. Few physicists claimed to understand it. None did, and so students’ questions were met with such nonsense as ‘If you think you’ve understood quantum mechanics then you don’t.’ Inconsistency was defended as ‘complementarity’ or ‘duality’; parochialism was hailed as philosophical sophistication. Thus the theory claimed to stand outside the jurisdiction of normal (i.e. all) modes of criticism – a hallmark of bad philosophy.
Its combination of vagueness, immunity from criticism, and the prestige and perceived authority of fundamental physics opened the door to countless systems of pseudo-science and quackery supposedly based on quantum theory. Its disparagement of plain criticism and reason as being ‘classical’, and therefore illegitimate, has given endless comfort to those who want to defy reason and embrace any number of irrational modes of thought. Thus quantum theory – the deepest discovery of the physical sciences – has acquired a reputation for endorsing practically every mystical and occult doctrine ever proposed. — page 310

12.5.0 What is positivism?

Positivism The bad philosophy that everything not ‘derived from observation’ should be eliminated from science. — page 325

12.5.1 What is logical positivism?

Logical positivism The bad philosophy that statements not verifiable by observation are meaningless. — page 325

Hence, it asserts its own meaninglessness.

12.6.0 What is postmodernism? What is its criticism?

If according to postmodernism everything is relative: what exactly is it studying then?

One currently influential philosophical movement goes under various names such as postmodernism, deconstructionism and structuralism, depending on historical details that are unimportant here. It claims that because all ideas, including scientific theories, are conjectural and impossible to justify, they are essentially arbitrary: they are no more than stories, known in this context as ‘narratives’. Mixing extreme cultural relativism with other forms of anti-realism, it regards objective truth and falsity, as well as reality and knowledge of reality, as mere conventional forms of words that stand for an idea’s being endorsed by a designated group of people such as an elite or consensus, or by a fashion or other arbitrary authority. And it regards science and the Enlightenment as no more than one such fashion, and the objective knowledge claimed by science as an arrogant cultural conceit.
Perhaps inevitably, these charges are true of postmodernism itself: it is a narrative that resists rational criticism or improvement, precisely because it rejects all criticism as mere narrative. Creating a successful postmodernist theory is indeed purely a matter of meeting the criteria of the postmodernist community – which have evolved to be complex, exclusive and authority-based. Nothing like that is true of rational ways of thinking: creating a good explanation is hard not because of what anyone has decided, but because there is an objective reality that does not meet anyone’s prior expectations, including those of authorities. — page 314

Psychology and Explanationless science

12.7.0 Why can’t we split predictions from explanations of the theory?

Because we need explanations to know when to apply the theory, and identify whether there are mistakes in our process or not.

one legacy of empiricism that continues to cause confusion, and has opened the door to a great deal of bad philosophy, is the idea that it is possible to split a scientific theory into its predictive rules of thumb on the one hand and its assertions about reality (sometimes known as its ‘interpretation’) on the other. This does not make sense, because – as with conjuring tricks – without an explanation it is impossible to recognize the circumstances under which a rule of thumb is supposed to apply. And it especially does not make sense in fundamental physics, because the predicted outcome of an observation is itself an unobserved physical process. — page 314

12.7.1 Why people try to exclude explanation from theories?

Because it holds one’s implicit explanation (theory) immune from criticism.

12.8.0 What is behaviorism?

Instrumentalism applied to psychology. It favors stimulus-response rules of thumb over good explanations.

12.8.1 Consider a psychology study that is measuring people’s happiness and its relation to genes. What would be David’s critique of it?

First, because we don’t understand qualia we can’t objectively tell how happy someone is, only their subjective perception of it. So at best we have a proxy for happiness.

Second, how could we ever explain how exactly certain genes are related to the proxy of happiness. Is it objectively coding for happiness? Or one’s tendency to report it higher? Or one’s beauty?

Behavioral psychology can’t answer any of those questions. At best it could say that those two are correlated. Yet, everything is correlated with everything, especially when you start creating arbitrary proxies.

I have mentioned behaviourism, which is instrumentalism applied to psychology. It became the prevailing interpretation in that field for several decades, and, although it is now largely repudiated, research in psychology continues to downplay explanation in favour of stimulus-response rules of thumb. Thus, for instance, it is considered good science to conduct behaviouristic experiments to measure the extent to which a human psychological state such as, say, loneliness or happiness is genetically coded (like eye colour) or not (such as date of birth). Now, there are some fundamental problems with such a study from an explanatory point of view. First, how can we measure whether different people’s ratings of their own psychological state are commensurable? That is to say, some proportion of the people claiming to have happiness level 8 might be quite unhappy but also so pessimistic that they cannot imagine anything much better. And some of the people who claim only level 3 might in fact be happier than most, but have succumbed to a craze that promises extreme future happiness to those who can learn to chant in a certain way. And, second, if we were to find that people with a particular gene tend to rate themselves happier than people without it, how can we tell whether the gene is coding for happiness? Perhaps it is coding for less reluctance to quantify one’s happiness. Perhaps the gene in question does not affect the brain at all, but only how a person looks, and perhaps better-looking people are happier on average because they are treated better by others. There is an infinity of possible explanations. But the study is not seeking explanations.
…
Science simply cannot resolve that issue until we have explanatory theories about what objective attributes people are referring to when they speak of their happiness, and also about what physical chain of events connects genes to those attributes. — page 316

12.9.0 How measurement in genuine science is different from the explanation-free science?

There must be some independent and objective test of the proxy. For example, you can use pre-voting poll as a proxy for actual voting, because you could test the accuracy of proxy by the voting results.

Genuine science has a way to test the accuracy of a proxy.

All scientific measurements use chains of proxies. But, as I explained in Chapters 2 and 3, each link in the chain is an additional source of error, and we can avoid fooling ourselves only by criticizing the theory of each link – which is impossible unless an explanatory theory links the proxies to the quantities of interest. That is why, in genuine science, one can claim to have measured a quantity only when one has an explanatory theory of how and why the measurement procedure should reveal its value, and with what accuracy.
There are circumstances under which there is a good explanation linking the measurable proxy such as marking checkboxes with a quantity of interest, and in such cases there need be nothing unscientific about the study. For example, political opinion surveys may ask whether respondents are ‘happy’ with a given politician facing re-election, under the theory that this gives information about which checkbox the respondents will choose in the election itself. That theory is then tested at the election. There is no analogue of such a test in the case of happiness: there is no independent way of measuring it. — page 317

12.9.1 How could you know how to test the accuracy of any variable?

By having explanatory theories about them.

If you want to measure a number of visitors of a museum, checking both leaving and entering number allows you to calculate accuracy of the report. That would be a good science. But you could do that only with the explanatory theory of the museum’s interior!

Suppose you have been commissioned to measure the average number of people who visit the City Museum each day. It is a large building with many entrances. Admission is free, so visitors are not normally counted. You engage some assistants. …
Each morning your assistants take up their stations at the doors. They mark a sheet of paper whenever someone enters through their door. After the museum closes, they count all their marks, and you add together all their counts. You do this every day for a specified period, take the average, and that is the number that you report to your client.
However, in order to claim that your count equals the number of visitors to the museum, you need some explanatory theories. For instance, you are assuming that the doors you are observing are precisely the entrances to the museum, and that they lead only to the museum. If one of them leads to the cafeteria or the museum shop as well, you might be making a large error if your client does not consider people who go only there to be ‘visitors to the museum’. … So you need quite a sophisticated explanatory theory of what the client means by ‘visitors to the museum’ before you can devise a strategy for counting them.
Suppose you count the number of people coming out as well. If you have an explanatory theory saying that the museum is always empty at night, and that no one enters or leaves other than through the doors, and that visitors are never created, destroyed, split or merge, and so on, then one possible use for the outgoing count is to check the ingoing one: you would predict that they should be the same. Then, if they are not the same, you will have an estimate of the accuracy of your count. That is good science. In fact reporting your result without also making an accuracy estimate makes your report strictly meaningless. But unless you have an explanatory theory of the interior of the museum – which you never see – you cannot use the outgoing count, or anything else, to estimate your error. — page 322

12.10.0 Why explanationless science prevents knowledge growth?

Explanationless science always has explanations, they are just hidden, for as Popper has shown we always have theories. (All observation is theory laden.) Such implicit explanations are rules of thumb — simplistic, biased theories of the world. As they are left uncriticized, knowledge growth doesn’t happen.

Rules of thumb could be correlated for infinite possible reasons, explanationless science would never tell us whether we can do something about it or not.

For example, there would be a study showing that slavery is genetically correlated (say in 1860s). Explanationless science won’t distinguish whether it is a causation, but it would provide an illusion of supporting the implicit theories. Implicit theories that slavery is determined by genes. This entrenches us in our existing parochial errors: making it easy to fool ourselves.

In explanationless science, one may acknowledge that actual happiness and the proxy one is measuring are not necessarily equal. But one nevertheless calls the proxy ‘happiness’ and moves on. One chooses a large number of people, ostensibly at random (though in real life one is restricted to small minorities such as university students, in a particular country, seeking additional income), and one excludes those who have detectable extrinsic reasons for happiness or unhappiness (such as recent lottery wins or bereavement). So one’s subjects are just ‘typical people’ – though in fact one cannot tell whether they are statistically representative without an explanatory theory. Next, one defines the ‘heritability’ of a trait as its degree of statistical correlation with how genetically related the people are. Again, that is a non-explanatory definition: according to it, whether one was a slave or not was once a highly ‘heritable’ trait in America: it ran in families. More generally, one acknowledges that statistical correlations do not imply anything about what causes what. But one adds the inductivist equivocation that ‘they can be suggestive, though.’
Then one does the study and finds that ‘happiness’ is, say, 50 per cent ‘heritable’. This asserts nothing about happiness itself, until the relevant explanatory theories are discovered (at some time in the future – perhaps after consciousness is understood and AIs are commonplace technology). Yet people find the result interesting, because they interpret it via everyday meanings of the words ‘happiness’ and ‘heritable’. Under that interpretation – which the authors of the study, if they are scrupulous, will nowhere have endorsed – the result is a profound contribution to a wide class of philosophical and scientific debates about the nature of the human mind. Press reports of the discovery will reflect this. The headline will say, ‘New Study Shows Happiness 50% Genetically Determined’ – without quotation marks around the technical terms.
…
Using the same logic on the slavery example, one could have concluded in 1860 that, say, 95 per cent of slavery is genetically determined and therefore beyond the power of political action to remedy. — page 318

12.10.1 Why bother knowing why exactly slavery is genetically correlated: “the effect is real”?

Knowing why is crucial, for we could know whether it requires gene editing, or simply better political institutions. Without explanation, and hence causation, we could never know, we would err towards the simpler answer — imprisoning ourselves in the parochial error.

The purpose of science is to set us free from such parochial errors, and explanatory testable theories is our best guess of how to do so.

But who cares how the genes cause the effect – whether by conferring good looks or otherwise? The effect itself is real.
The effect is real, but the experiment cannot detect how much of it one can alter without genetic engineering, just by knowing how. That is because the way in which those genes affect happiness may itself depend on knowledge. For instance, a cultural change may affect what people deem to be ‘good looks’, and that would then change whether people tend to be made happier by virtue of having particular genes. Nothing in the study can detect whether such a change is about to happen. Similarly, it cannot detect whether a book will be written one day which will persuade some proportion of the population that all evils are due to lack of knowledge, and that knowledge is created by seeking good explanations. If some of those people consequently create more knowledge than they otherwise would have, and become happier than they otherwise would have been, then part of the 50 per cent of happiness that was ‘genetically determined’ in all previous studies will no longer be so.
…
Notice that this is a form of bad science that may well have conformed to all the best practices of scientific method – proper randomizing, proper controls, proper statistical analysis. All the formal rules of ‘how to keep from fooling ourselves’ may have been followed. And yet no progress could possibly be made, because it was not being sought: explanationless theories can do no more than entrench existing, bad explanations. — page 319

12.11.0 How explanationless science incentivizes errors?

Besides delaying growth of knowledge by leaving implicit theories of the world (rules of thumb) uncriticized. It also incentivizes scientists to make errors, first in observation and second in ‘suggestive interpretation’ because they would yield more exciting results.

[Coming back to our museum example.] Now, suppose you are doing your study using explanationless science instead – which really means science with unstated, uncriticized explanations, just as the Copenhagen interpretation really assumed that there was only one unobserved history connecting successive observations. Then you might analyse the results as follows. For each day, subtract the count of people entering from the count of those leaving. If the difference is not zero, then – and this is the key step in the study – call that difference the ‘spontaneous-human-creation count’ if it is positive, or the ‘spontaneous-human-destruction count’ if it is negative. If it is exactly zero, call it ‘consistent with conventional physics’.
The less competent your counting and tabulating are, the more often you will find those ‘inconsistencies with conventional physics’. Next, prove that non-zero results (the spontaneous creation or destruction of human beings) are inconsistent with conventional physics. Include this proof in your report, but also include a concession that extraterrestrial visitors would probably be able to harness physical phenomena of which we are unaware. Also, that teleportation to or from another location would be mistaken for ‘destruction’ (without trace) and ‘creation’ (out of thin air) in your experiment and that therefore this cannot be ruled out as a possible cause of the anomalies.
When headlines appear of the form ‘Teleportation Possibly Observed in City Museum, Say Scientists’ and ‘Scientists Prove Alien Abduction is Real,’ protest mildly that you have claimed no such thing, that your results are not conclusive, merely suggestive, and that more studies are needed to determine the mechanism of this perplexing phenomenon.
…
The substance of scientific theories is explanation, and explanation of errors constitutes most of the content of the design of any non-trivial scientific experiment.
As the above example illustrates, a generic feature of experimentation is that the bigger the errors you make, either in the numbers or in your naming and interpretation of the measured quantities, the more exciting the results are, if true. So, without powerful techniques of error-detection and -correction – which depend on explanatory theories – this gives rise to an instability where false results drown out the true.— page 322, 323

12.12.0 What is the best way to oppose bad philosophy?

Progress.

Bad philosophy cannot easily be countered by good philosophy argument and explanation – because it holds itself immune. But it can be countered by progress. People want to understand the world, no matter how loudly they may deny that. And progress makes bad philosophy harder to believe. That is not a matter of refutation by logic or experience, but of explanation. — page 324

🤝 13 — Choices

As optimists we would want the progress to continue indefinitely. Hence, the questions are: How to design a society to maximize progress? How to make social decisions? Is there a source of knowledge we could use?

David starts with the narrow problem of apportionment. Proceeds more universally with group decision making, and finishes with decisive conclusion on how to make decisions as a society.

Summary

Decision-making is an act of creating options, explanations of the world and choosing between them through criticism. All decision-making is about knowledge creation, knowledge of which options to have, and which to choose. Be it scientific or societal issues, knowledge creation process is universal. Hence, to run society rationally we must apply our best epistemological theories to it.

There is no source of knowledge we could access, individually or collectively. We can only guess (conjecture) solutions to our problems and criticize them. The better political system is at testing our best solutions and removing the broken ones without violence, the better political system it universally is.

For this reason plurality voting systems are better than proportional representation ones. They consensus power in one party which allows it to implement its best solution to the problem, not an average of several options no one would choose in the first place. Failure of the tested solution would have real repercussions — loss of power entirely.

Not only proportional representation gives the most power to the third largest party, it mainly relies on compromise — an erroneous theory of how knowledge creation works. If all decision-making is knowledge creation, does anyone expect an average of the two options to work for science? Do we get the mean of the Newtonian and Einsteinian theories to progress? No. So why expect this to work for decision-making?

You can practice chapter questions as flashcards here.

Apportionment Problem

13.1.0 What is the apportionment problem?

in the US House of Representatives, how many seats should each state be allotted? This is known as the apportionment problem, because the US Constitution requires seats to be ‘apportioned among the several States . . . according to their respective Numbers [i.e. their populations]’. So, if your state contained 1 per cent of the US population, it would be entitled to 1 per cent of the seats in the House. This was intended to implement the principle of representative government – that the legislature should represent the people. — page 326

13.1.1 What is the apportionment rule?

At present there are 435 seats in the House of Representatives; so, if 1 per cent of the US population did live in your state, then by strict proportionality the number of representatives to which it would be entitled – known as its quota – would be 4.35. When the quotas are not whole numbers, which of course they hardly ever are, they have to be rounded somehow. The method of rounding is known as an apportionment rule. — page 326

13.1.2 What is the quota violation?

An apportionment rule is said to ‘stay within the quota’ if the number of seats that it allocates to each state never differs from the state’s quota by as much as a whole seat. For instance, if a state’s quota is 4.35 seats, then to ‘stay within the quota’ a rule must assign that state either four seats or five. It may take all sorts of information into account in choosing between four and five, but if it is capable of assigning any other number it is said to ‘violate quota’. — page 327

13.1.3 What are the reallocation schemes?

Simple apportionment rules yield quota violations, schemes to reallocate seats to stay within quota are called reallocation schemes.

13.1.4 What is the problem with reallocation schemes?

They always seem to be unfair towards one or the other state. Even if in the long-run biases cancel out, none of schemes seem to fulfil the fairness criterion.

Such strategies are known as reallocation schemes. They are indeed capable of staying within the quota. So, what is wrong with them? In the jargon of the subject, the answer is apportionment paradoxes – or, in ordinary language, unfairness and irrationality.
For example, the last reallocation scheme that I described is unfair by being biased against the inhabitants of the least populous state. [When you round down the least populated state to achieve quota balance.] They bear the whole cost of correcting the rounding errors.
…
That must mean that the ‘minimum total deviation from quota’ is not the right measure of representativeness. [Because some states don’t receive a seat whatsoever. So first seat seems to be more important than an Nth one.] But what is the right measure? What is the right trade-off between being slightly unfair to many people and very unfair to a few people? — page 328

13.1.5 What is the population paradox?

This is one of the example of problems with the reallocation schemes that seem bluntly unfair.

population paradox: a state whose population has increased since the last census can lose a seat to one whose population has decreased. — page 330

13.2.0 What is the Balinski and Young’s Theorem?

Balinski and Young’s Theorem
Every apportionment rule that stays within the quota suffers from the population paradox. — page 334

Arrow’s Impossibility Theorem

13.3.0 What is Arrow’s Impossibility Theorem?

It is a theory that no collective decision-making with more than two options can yield logically consistent results. Arrow has proved it by establishing five basic axioms of any democratic voting and then showing their unsoundness. Simplest example. Great explanation of the proof.

13.3.1 What five axioms did it have?

He first laid down five elementary axioms that any rule defining the ‘will of the people’ – the preferences of a group – should satisfy, and these axioms seem, at first sight, so reasonable as to be hardly worth stating. One of them is that the rule should define a group’s preferences only in terms of the preferences of that group’s members. Another is that the rule must not simply designate the views of one particular person to be ‘the preferences of the group’ regardless of what the others want. That is called the ‘no-dictator’ axiom. A third is that if the members of the group are unanimous about something – in the sense that they all have identical preferences about it – then the rule must deem the group to have those preferences too. Those three axioms are all expressions, in this situation, of the principle of representative government.
Arrow’s fourth axiom is this. Suppose that, under a given definition of ‘the preferences of the group’, the rule deems the group to have a particular preference – say, for pizza over hamburger. Then it must still deem that to be the group’s preference if some members who previously disagreed with the group (i.e. they preferred hamburger) change their minds and now prefer pizza. This constraint is similar to ruling out a population paradox. A group would be irrational if it changed its ‘mind’ in the opposite direction to its members.
The last axiom is that if the group has some preference, and then some members change their minds about something else, then the rule must continue to assign the group that original preference. For instance, if some members have changed their minds about the relative merits of strawberries and raspberries, but none of their preferences about the relative merits of pizza and hamburger have changed, then the group’s preference between pizza and hamburger must not be deemed to have changed either. This constraint can again be regarded as a matter of rationality: if no members of the group change any of their opinions about a particular comparison, nor can the group. — page 336

13.4.0 What Arrow’s Impossibility Theorem implies?

No group decision-making is rational. In fact, any decision-making that follows the procedures he has described is irrational, even when applied to individuals. Hence, all decision-making is irrational! (Similar to Hume’s Induction problem, which turned out to be not a problem at all.)

Arrow proved that the axioms that I have just listed are, despite their reasonable appearance, logically inconsistent with each other. No way of conceiving of ‘the will of the people’ can satisfy all five of them. This strikes at the assumptions behind social-choice theory at an arguably even deeper level than the theorems of Balinski and Young. First, Arrow’s axioms are not about the apparently parochial issue of apportionment, but about any situation in which we want to conceive of a group having preferences. Second, all five of these axioms are intuitively not just desirable to make a system fair, but essential for it to be rational. Yet they are inconsistent.
It seems to follow that a group of people jointly making decisions is necessarily irrational in one way or another. It may be a dictatorship, or under some sort of arbitrary rule; or, if it meets all three representativeness conditions, then it must sometimes change its ‘mind’ in a direction opposite to that in which criticism and persuasion have been effective. So it will make perverse choices, no matter how wise and benevolent the people who interpret and enforce its preferences may be – unless, possibly, one of them is a dictator (see below). So there is no such thing as ‘the will of the people’. There is no way to regard ‘society’ as a decision-maker with self-consistent preferences. This is hardly the conclusion that social-choice theory was supposed to report back to the world. — page 337

13.4.1 Explain how Arrow’s decision making procedure applies to individuals.

Arrow’s theorem applies not only to collective decision-making but also to individuals, as follows. Consider a single, rational person faced with a choice between several options. If the decision requires thought, then each option must be associated with an explanation – at least a tentative one – for why it might be the best. To choose an option is to choose its explanation. So how does one decide which explanation to adopt?
Common sense says that one ‘weighs’ them – or weighs the evidence that their arguments present. …
Consider that supposed weighing process. Each piece of evidence, including each feeling, prejudice, value, axiom, argument and so on, depending on what ‘weight’ it had in that person’s mind, would contribute that amount to that person’s ‘preferences’ between various explanations. Hence for the purposes of Arrow’s theorem each piece of evidence can be regarded as an ‘individual’ participating in the decision-making process, where the person as a whole would be the ‘group’.
Now, the process that adjudicates between the different explanations would have to satisfy certain constraints if it were to be rational. For instance, if, having decided that one option was the best, the person received further evidence that gave additional weight to that option, then the person’s overall preference would still have to be for that option – and so on. Arrow’s theorem says that those requirements are inconsistent with each other, and so seems to imply that all decision-making – all thinking – must be irrational. Unless, perhaps, one of the internal agents is a dictator, empowered to override the combined opinions of all the other agents. But this is an infinite regress: how does the ‘dictator’ itself choose between rival explanations about which other agents it would be best to override? — page 340

{ I am not sure this is a fair ‘extrapolation’ to individual decision making. David introduces ‘weighing’ procedure, yet it wasn’t implied in Arrow’s model. We of course know that weighing decision making is wrong. So David seems to add this ‘weighing construction’ only then to say that it is wrong, hence Arrow’s model is wrong. But what is the criticism of Arrow’s model without this ‘weighing construction’? }

13.5.0 What is the criticism of the Arrow’s model and its implications?

Arrow’s decision-making model is wrong, hence it is not surprising that it yields irrationalities. Decision-making is knowledge creation, all knowledge creation consists of guessing options and criticizing them, choosing the last man standing.

Arrow’s model disregards the most important part: creating new options to choose from. There is no creativity in its model. It is of course understandable: we still don’t know how to model creativity, but this unforgivable. Absence of creativity is a serious constraint that refutes using the entire theory.

There is something very wrong with that entire conventional model of decision-making, both within single minds and for groups as assumed in social-choice theory. It conceives of decision-making as a process of selecting from existing options according to a fixed formula (such as an apportionment rule or electoral system). But in fact that is what happens only at the end of decision-making – the phase that does not require creative thought. In terms of Edison’s metaphor, the model refers only to the perspiration phase, without realizing that decision-making is problem-solving, and that without the inspiration phase nothing is ever solved and there is nothing to choose between. At the heart of decision-making is the creation of new options and the abandonment or modification of existing ones.
To choose an option, rationally, is to choose the associated explanation. Therefore, rational decision-making consists not of weighing evidence but of explaining it, in the course of explaining the world. One judges arguments as explanations, not justifications, and one does this creatively, using conjecture, tempered by every kind of criticism. — page 341

13.5.1 How is this a Zeno’s mistake?

Zeno’s mistake: confusing abstract concept with the physical one because of the same name.

Just because Arrow has created some abstract model and named it decision-making, it doesn’t mean that it is actually what is happening when people make decisions (which is knowledge creation in light disguise).

Under Arrow’s ‘decision-making model’ options are fixed and one cannot create new ones, but this is not what happens in real-life. Creation of new options is the biggest part of decision-making.

Similarly, a voter may look through lists of the various parties’ policies, and may even assign each issue a ‘weight’ to represent its importance; but one can do that only after one has thought about one’s political philosophy, and has explained to one’s own satisfaction how important that makes the various issues, what policies the various parties are likely to adopt in regard to those issues, and so on.
The type of ‘decision’ considered in social-choice theory is choosing from options that are known and fixed, according to preferences that are known, fixed and consistent. The quintessential example is a voter’s choice, in the polling booth, not of which candidate to prefer but of which box to check. As I have explained, this is a grossly inadequate, and inaccurate, model of human decision-making. In reality, the voter is choosing between explanations, not checkboxes, and, while very few voters choose to affect the checkboxes themselves, by running for office, all rational voters create their own explanation for which checkbox they personally should choose.
So it is not true that decision-making necessarily suffers from those crude irrationalities – not because there is anything wrong with Arrow’s theorem or any of the other no-go theorems, but because social-choice theory is itself based on false assumptions about what thinking and deciding consist of. It is Zeno’s mistake. It is mistaking an abstract process that it has named decision-making for the real-life process of the same name. — page 342

13.6.0 What is David’s criticism of compromises?

Decision-making is knowledge creation. Knowledge creation is about creating hard to vary options and criticizing them, choosing the last man standing. Imagine how ridiculous it sounds to do a compromise between two rival ideas. For example Newtonian and Einsteinian physics. Do we create a third option that is ‘somewhere in between the two’?! No, this is nonsense. We criticize and choose the last man standing. We also can’t that easily create a better third theory, for it requires creativity, not just taking averages of two values.

Good ‘options’ are hard to vary, be it a law of physics, or choosing which house to buy. Compromise is easy to vary. If compromise is useful, than your options weren’t well thought of.

Disagree and commit.

It is in the nature of good explanations – being hard to vary – that there is only one of them. Having created it, one is no longer tempted by the alternatives. They have been not outweighed, but out-argued, refuted and abandoned. During the course of a creative process, one is not struggling to distinguish between countless different explanations of nearly equal merit; typically, one is struggling to create even one good explanation, and, having succeeded, one is glad to be rid of the rest.
Another misconception to which the idea of decision-making by weighing sometimes leads is that problems can be solved by weighing – in particular, that disputes between advocates of rival explanations can be resolved by creating a weighted average of their proposals. But the fact is that a good explanation, being hard to vary at all without losing its explanatory power, is hard to mix with a rival explanation: something halfway between them is usually worse than either of them separately. Mixing two explanations to create a better explanation requires an additional act of creativity. That is why good explanations are discrete – separated from each other by bad explanations and why, when choosing between explanations, we are faced with discrete options.
In complex decisions, the creative phase is often followed by a mechanical, perspiration phase in which one ties down details of the explanation that are not yet hard to vary but can be made so by non-creative means. — page 342

Plurality Voting vs Proportional Representation and Popper

13.7.0 What is the most important problem of the proportional representation system?

The third party receives disproportionate power — they become the kingmakers.

A more important one – which is shared by even the mildest of proportional systems – is that they assign disproportionate power in the legislature to the third-largest party, and often to even smaller parties. It works like this. It is rare (in any system) for a single party to receive an overall majority of votes. Hence, if votes are reflected proportionately in the legislature, no legislation can be passed unless some of the parties cooperate to pass it, and no government can be formed unless some of them form a coalition. Sometimes the two largest parties manage to do this, but the most common outcome is that the leader of the third-largest party holds the ‘balance of power’ and decides which of the two largest parties shall join it in government, and which shall be sidelined, and for how long. That means that it is correspondingly harder for the electorate to decide which party, and which policies, will be removed from power. — page 339

This is well illustrated on the Germany example:

In Germany (formerly West Germany) between 1949 and 1998, the Free Democratic Party (FDP) was the third largest.* Though it never received more than 12.8 per cent of the vote, and usually much less, the country’s proportional-representation system gave it power that was insensitive to changes in the voters’ opinions. On several occasions it chose which of the two largest parties would govern, twice changing sides and three times choosing to put the less popular of the two (as measured by votes) into power. The FDP’s leader was usually made a cabinet minister as part of the coalition deal, with the result that for the last twenty-nine years of that period Germany had only two weeks without an FDP foreign minister. In 1998, when the FDP was pushed into fourth place by the Green Party, it was immediately ousted from government, and the Greens assumed the mantle of kingmakers. And they took charge of the Foreign Ministry as well. This disproportionate power that proportional representation gives the third-largest party is an embarrassing feature of a system whose whole raison d’être, and supposed moral justification, is to allocate political influence proportionately. — page 339

13.7.1 What is the other problem with the proportional representation system?

It fails to learn anything. Its main feature: compromise, is its main flaw. Good explanations are anything but compromises. True knowledge is never created based on compromises.

Compromise is a third option, something that no one chose in the first place. No one believes it is a better explanation or solution of the problem. Hence, once it fails everyone retreats to their past explanations, learning nothing.

Proportional representation is often defended on the grounds that it leads to coalition governments and compromise policies. But compromises – amalgams of the policies of the contributors – have an undeservedly high reputation. Though they are certainly better than immediate violence, they are generally, as I have explained, bad policies. If a policy is no one’s idea of what will work, then why should it work? But that is not the worst of it. The key defect of compromise policies is that when one of them is implemented and fails, no one learns anything because no one ever agreed with it. Thus compromise policies shield the underlying explanations which do at least seem good to some faction from being criticized and abandoned. — page 346

Another problem is that its elections don’t hold real stakes:

Under a proportional system, small changes in public opinion seldom count for anything, and power can easily shift in the opposite direction to public opinion. What counts most is changes in the opinion of the leader of the third-largest party. This shields not only that leader but most of the incumbent politicians and policies from being removed from power through voting. They are more often removed by losing support within their own party, or by shifting alliances between parties. So in that respect the system badly fails Popper’s criterion. Under plurality voting, it is the other way round. The all-or-nothing nature of the constituency elections, and the consequent low representation of small parties, makes the overall outcome sensitive to small changes in opinion. When there is a small shift in opinion away from the ruling party, it is usually in real danger of losing power completely. — page 347

13.8.0 What is Popper’s criterion for judging political systems?

Popper’s criterion Good political institutions are those that make it as easy as possible to detect whether a ruler or policy is a mistake, and to remove rulers or policies without violence when they are. — page 352

13.8.1 What is the purpose of democratic elections under Popper’s view?

It is not to ask population as an oracle of what is the right choice. Population isn’t a better ‘source of knowledge’ than politicians are; there is no ultimate source of knowledge. The point of elections is to ask population which experiments they would like to run, which explanations they consider refuted.

So let us reconsider collective decision-making in terms of Popper’s criterion instead. Instead of wondering earnestly which of the self-evident yet mutually inconsistent criteria of fairness, representativeness and so on are the most self-evident, so that they can be entrenched, we judge such criteria, along with all other actual or proposed political institutions, according to how well they promote the removal of bad rulers and bad policies. To do this, they must embody traditions of peaceful, critical discussion – of rulers, policies and the political institutions themselves.
In this view, any interpretation of the democratic process as merely a way of consulting the people to find out who should rule or what policies to implement misses the point of what is happening. An election does not play the same role in a rational society as consulting an oracle or a priest, or obeying orders from the king, did in earlier societies. The essence of democratic decision-making is not the choice made by the system at elections, but the ideas created between elections. And elections are merely one of the many institutions whose function is to allow such ideas to be created, tested, modified and rejected. The voters are not a fount of wisdom from which the right policies can be empirically ‘derived’. They are attempting, fallibly, to explain the world and thereby to improve it. They are, both individually and collectively, seeking the truth – or should be, if they are rational. … In particular, what voters are doing in elections is not synthesizing a decision of a superhuman being, ‘Society’. They are choosing which experiments are to be attempted next, and (principally) which are to be abandoned because there is no longer a good explanation for why they are best. The politicians, and their policies, are those experiments. — page 345

13.9.0 What is plurality voting system?

The one with the most votes gets all the power. This usually digresses into two party system, and over-representation of power for the ruling party.

The system used to elect members of the legislatures of most countries in the British political tradition is that each district (or ‘constituency’) in the country is entitled to one seat in the legislature, and that seat goes to the candidate with the largest number of votes in that district. This is called the plurality voting system (‘plurality’ meaning ‘largest number of votes’) – often called the ‘first-past-the-post’ system, because there is no prize for any runner-up, and no second round of voting (both of which feature in other electoral systems for the sake of increasing the proportionality of the outcomes). Plurality voting typically ‘over-represents’ the two largest parties, compared with the proportion of votes they receive. Moreover, it is not guaranteed to avoid the population paradox, and is even capable of bringing one party to power when another has received far more votes in total. — page 346

13.9.1 Explain why plurality voting system is better than proportional representation?

Digression to two party system and over-representation of power is not a bug, but a crucial feature.

Consensus of power allows ruling party to run experiments without ‘compromises’. They can choose their first option — solution to the problem that they consider to be the best.

Once they run experiment, society will judge whether it was successful at solving the problem or not. There are clearly responsible actors, and failure would have significant consequences — loss of power. Entire loss of power means that government will be changed, hence, fulfilling Popper’s political criterion with flying colors.

Under plurality voting system society can test the best provided solutions and learn.

Let me trace the mechanism of that advantage more explicitly. Following a plurality-voting election, the usual outcome is that the party with the largest total number of votes has an overall majority in the legislature, and therefore takes sole charge. All the losing parties are removed entirely from power. This is rare under proportional representation, because some of the parties in the old coalition are usually needed in the new one. Consequently, the logic of plurality is that politicians and political parties have little chance of gaining any share in power unless they can persuade a substantial proportion of the population to vote for them. That gives all parties the incentive to find better explanations, or at least to convince more people of their existing ones, for if they fail they will be relegated to powerlessness at the next election.
In the plurality system, the winning explanations are then exposed to criticism and testing, because they can be implemented without mixing them with the most important claims of opposing agendas. Similarly, the winning politicians are solely responsible for the choices they make, so they have the least possible scope for making excuses later if those are deemed to have been bad choices. If, by the time of the next election, they are less convincing to the voters than they were, there is usually no scope for deals that will keep them in power regardless. — page 347

🌼 14 — Why are Flowers Beautiful?

Is there an objective beauty? Can we create knowledge about abstract phenomena, like philosophy and aesthetics?

Summary

People believed that we can’t create knowledge about abstract phenomena because we can’t sense them — they are abstract, not physical. Thankfully, we don’t derive knowledge from our senses. We guess theories and criticize them, choosing the last man standing.

Obviously we can create theories about abstract things. All our theories are explanations. Explanations can rely on physical things, which certainly can be wrong, hence the entire explanation can be wrong. It doesn’t matter whether the explanation is about an abstract or a physical phenomena. If there is wrong, there is right, there is better and worse, there is objectivity. Thus, there is objectivity in abstractions: just as one can be mathematically wrong, one can have wrong moral and aesthetic theories.

If something is a regularity in nature there must be an explanation for it, it can’t be just a coincidence. Science searches for such explanations.

You can practice chapter questions as flashcards here.

14.1.0 Why most people believe that philosophical knowledge cannot be created?

It is a mistake of an empirical thinking: if I can’t derive it from my experiences, I can’t learn it. Hence all abstract phenomena become ‘unlearnable’. Surprisingly, most empiricists deny knowledge creation about abstract things like morality and philosophy, but not math! So there is a problem for them: How can we create knowledge about abstract concepts of math, but not of philosophy and morality?

This puzzled philosophers like Plato, who guessed that we are inborn with some geometrical and mathematical concepts, like a perfect circle or triangle. Thankfully, we now know that this is not the case, any knowledge creation is about guessing theories and criticizing them.

Why can’t our guesses be about abstract things, like philosophy and math? They certainly can. And we certainly can criticize such guesses, choosing one over the other.

Hence, morality, aesthetics, philosophy and math are all accessible for knowledge creation. They all are abstract phenomena, of which we can learn by the same old method of guessing and criticizing.

Moreover, our guesses are explanations, be it of abstract phenomena or not. Explanations can rely on physical facts, and those certainly can be wrong. Hence, the explanations themselves can be wrong, and our theories about abstract things. So, if there is such thing as being wrong about abstract thing, there is such thing as being right. It is not entirely relative, so there is an objective truth about the subject.

{But what if our explanation doesn’t invoke any physical things. Can it be wrong if we use only abstract concepts in our explanations?}

Mathematics has its proofs (so the argument goes), and science has its experimental tests; but if you choose to believe that Mozart was an inept and cacophonous composer then neither logic nor experiment nor anything else objective will ever contradict you.
However, it would be a mistake to dismiss the possibility of objective beauty for that sort of reason, for it is none other than the relic of empiricism that I discussed in Chapter 9 – the assertion that philosophical knowledge in general cannot exist. It is true that, just as one cannot deduce moral maxims from scientific theories, likewise nor can one deduce aesthetic values. But that would not prevent aesthetic truths from being linked to physical facts through explanations, as moral ones are. Wheeler was very nearly asserting such a link in that quotation. Facts can be used to criticize aesthetic theories, as they can moral theories. — page 354

David illustrates this on the example of extraterrestrial art:

For instance, there is the criticism that, since most arts depend on parochial properties of human senses (such as which range of colours and sounds they can detect), they cannot be attaining anything objective. Extraterrestrial people whose senses detected radio waves but not light or sound would have art that was inaccessible to us, and vice versa. And the reply to that criticism might be, first, that perhaps our arts are merely scratching the surface of what is possible: they are indeed parochial, but they are a first approximation to something universal. Or, second, that deaf composers on Earth have composed, and appreciated, great music; why could deaf extraterrestrials (or humans who were born deaf) not learn to do the same – if by no other means than by downloading a set of deaf-composer aesthetics into their brains? Or, third, what is the difference between using radio telescopes to understand the physics of quasars and using prosthetic senses (wired into the brain to create new qualia) to appreciate extraterrestrial art? — page 354

14.2.0 What is the guiding principle in science regarding the regularities in nature?

If something is a regularity in nature there must be an explanation for it! Science searches for such explanations.

Surely this is not coincidence: it is a regularity in nature. So it must have an explanation. — page 355

14.3.0 What is David’s argument for why there is an objective beauty?

Flowers evolved in shape and color to attract insect. They had to communicate between species, with very different genetic codes. So it cannot be just simple sexual attraction, both insects and flowers must converge on something. David would say they converge on objective, universal beauty. And this would explain why humans are attracted to flowers as well.

And therefore my guess is that the easiest way to signal across such a gap with hard-to-forge patterns designed to be recognized by hard-to-emulate pattern-matching algorithms is to use objective standards of beauty. So flowers have to create objective beauty, and insects have to recognize objective beauty. Consequently the only species that are attracted by flowers are the insect species that co-evolved to do so — and humans.
…
Could it be that what humans find attractive in flowers – or in art – is indeed objective, but it is not objective beauty? Perhaps it is something more mundane – something like a liking for bright colours, strong contrasts, symmetrical shapes. Humans seem to have an inborn liking for symmetry. It is thought to be a factor in sexual attractiveness, and it may also be useful in helping us to classify things and to organize our environment physically and conceptually. So a side effect of these inborn preferences might be a liking for flowers, which happen to be colourful and symmetrical. However, some flowers are white (at least to us – they may have colours that we cannot see and insects can), but we still find their shapes beautiful. All flowers do contrast with their background in some sense – that is a precondition for being used for signalling – but a spider in the bath contrasts with its background even more, and there is no widespread consensus that such a sight is beautiful. As for symmetry: again, spiders are quite symmetrical, while some flowers, such as orchids, are very unsymmetrical, yet we do not find them any less attractive for that. So I do not think that symmetry, colour and contrast are all that we are seeing in flowers when we imagine that we are seeing beauty. — page 363

💄 15 — The Evolution of Culture

We have considered what is the most rational way to make decisions as a society. Yet, regardless of arguments our culture decides whether we follow them or not. Hence:

What is culture? What are memes?
What are types of societies?
What are types of memes?
What social norms endorse knowledge creation?

Summary

Culture is a collection of memes, explicit and implicit. Memes, just as genes are replicators. Meme type is defined by how they replicate.

Rational memes survive by being useful to their holders because are deep truths of reality. They stay alive by promoting criticism of themselves and their rivals. They evolve towards deeper truths of reality.

Anti-rational memes survive by disabling creativity in their holders. They suppress critical thinking, and abuse human shortcomings to stay alive. They evolve further away from deep truths.

If society is dominated by rational memes it is dynamic, if by anti-rational ones it is static. Hence, static societies have a barely visible progress to their inhabitants. The only thing that is stable in a dynamic society is progress.

Western democratic societies are young dynamic societies. Only partial areas of it are dominated by rational memes, like physical sciences. Other areas, like gender preconceptions, or societal norms aren’t. Consider the societal repercussions a man would endure by putting some paint on their face — that is a lip balm. Such reaction doesn’t withstand any criticism.

Noticing anti-rational memes is hard because their evolution optimizes for it. One should be worrisome whenever coercion or authority appeal is present, for somewhere around must be anti-rational memes. Noticing such memes is the hardest part, eliminating them would take only a sliver of criticism.

Practice this chapter questions as flashcards here.

Culture and Memes

15.1.0 What is a culture?

A set explicit and implicit ideas (i.e. memes) that cause their holders to behave alike. Memes are long-lived if they have been accurately replicated through time, otherwise they are short-lived.

A culture is a set of ideas that cause their holders to behave alike in some ways. By ‘ideas’ I mean any information that can be stored in people’s brains and can affect their behaviour. …
Most of the ideas that define them, including the inexplicit ones, have a long history of being passed from one person to another. That makes these ideas memes – ideas that are replicators. …
Thus a culture is in practice defined not by a set of strictly identical memes, but by a set of variants that cause slightly different characteristic behaviours. …
long-lived memes: exceptional ideas that have been accurately replicated many times in succession. — page 369, 370

15.2.0 How memes evolve?

Just as genes: through variation and selection.

People tell each other amusing stories – some fictional, some factual. They are not jokes, but some become memes: they are interesting enough for the listeners to retell them to other people, and some of those people retell them in turn. But they rarely recite them word for word; nor do they preserve every detail of the content. Hence an often retold story will come to exist in different versions. Some of those versions will be retold more often than others – in some cases because people find them amusing. When that is the main reason for retelling them, successive versions that remain in circulation will tend to be ever more amusing. So the conditions are there for evolution: repeated cycles of imperfect copying of information, alternating with selection. — page 372

15.3.0 What is the process that memes must go through to get replicated?

Memes must be expressed as a behavior to get copied. Once spread, they must be stored sufficiently in recipient’s memory to cause the repetition of behavior, hence further replication.

Like genes, all memes contain knowledge (often inexplicit) of how to cause their own replication. This knowledge is encoded in strands of DNA or remembered by brains respectively. In both cases, the knowledge is adapted to causing itself to be replicated: it causes that more reliably than nearly all its variants do. In both cases, this adaptation is the outcome of alternating rounds of variation and selection. …
Each meme has to be expressed as behaviour every time it is replicated. For it is that behaviour, and only that behaviour (given the environment created by all the other memes), that effects the replication. That is because a recipient cannot see the representation of the meme in the holder’s mind. A meme cannot be downloaded like a computer program. If it is not enacted, it will not be copied.
The upshot of this is that memes necessarily become embodied in two different physical forms alternately: as memories in a brain, and as behaviour
…
To be a meme, an idea has to contain quite sophisticated knowledge of how to cause humans to do at least two independent things: assimilate the meme faithfully, and enact it. That some memes can replicate themselves with great fidelity for many generations is a token of how much knowledge they contain. — page 375, 377

15.3.1 How is it different from genes?

Genes don’t need to be expressed as behavior, its replicator is the gene itself.

In organisms that reproduce by dividing, either all the genes are copied into the next generation or (if the individual fails to reproduce) none are. In sexual reproduction, a full complement of genes randomly chosen from both parents is copied, or none are. In all cases, the DNA duplication process is automatic: genes are copied indiscriminately. One consequence is that some genes can be replicated for many generations without ever being ‘expressed’ (causing any behaviour) at all. Whether your parents ever broke a bone or not, genes for repairing broken bones will (barring unlikely mutations) be passed on to you and your descendants. — page 375

15.4.0 Why memes are selfish?

Memes optimize only their own replication, not benefits to their holder.

memes are ‘selfish’. They do not necessarily evolve to benefit their holders, or their society – or, again, even themselves, except in the sense of replicating better than other memes. (Though now most other memes are their rivals, not just variants of themselves.) The successful meme variant is the one that changes the behaviour of its holders in such a way as to make itself best at displacing other memes from the population. This variant may well benefit its holders, or their culture, or the species as a whole. But if it harms them, or destroys them, it will spread anyway. Memes that harm society are a familiar phenomenon. You need only consider the harm done by adherents of political views, or religions, that you especially abhor. Societies have been destroyed because some of the memes that were best at spreading through the population were bad for a society. — page 378

Static and Dynamic Societies

15.5.0 What is a static society?

‘static societies’: societies changing on a timescale unnoticed by the inhabitants. — page 380

Their pace of change is so slow because they are dominated by anti-rational memes.

15.5.1 How static societies are created?

It is a hard problem: How to have little if any change in memes in the society? It cannot be suppressing the ideas once they are created, for one cannot predict the creation to prepare in advance for it. It is also very difficult to put the Genie back in the bottle.

The main method is to suppress the creativity itself. Suppress not products, but the factories that produce them.

The primary method is always – and can only be – to disable the source of new ideas, namely human creativity. So static societies always have traditions of bringing up children in ways that disable their creativity and critical faculties. That ensures that most of the new ideas that would have been capable of changing the society are never thought of in the first place. — page 382

15.5.2 How can creativity be suppressed?

Static societies teach that ones value depends on how well they conform to the existing memes. They reduce ones life to physically embodying such memes.

people growing up in such a society acquire a set of values for judging themselves and everyone else which amounts to ridding themselves of distinctive attributes and seeking only conformity with the society’s constitutive memes. They not only enact those memes: they see themselves as existing only in order to enact them. So, not only do such societies enforce qualities such as obedience, piety and devotion to duty, their members’ sense of their own selves is invested in the same standards. People know no others. So they feel pride and shame, and form all their aspirations and opinions, by the criterion of how thoroughly they subordinate themselves to the society’s memes. — page 382

All of this ‘efficiency’ of static society is just ‘knowledge creation’ in memes.

How do memes ‘know’ how to achieve all such complex, reproducible effects on the ideas and behaviour of human beings? They do not, of course, know: they are not sentient beings. They merely contain that knowledge implicitly. How did they come by that knowledge? It evolved. The memes exist, at any instant, in many variant forms, and those are subject to selection in favour of faithful replication. For every long-lived meme of a static society, millions of variants of it will have fallen by the wayside because they lacked that tiny extra piece of information, that extra degree of ruthless efficiency in preventing rivals from being thought of or acted upon, that slight advantage in psychological leverage, or whatever it took to make it spread through the population better than its rivals and, once it was prevalent, to get it copied and enacted with just that extra degree of fidelity. If ever a variant happened to be a little better at inducing behaviour with those self-replicating properties, it soon became prevalent. As soon as it did, there were again many variants of that variant, which were again subject to the same evolutionary pressure. Thus, successive versions of the meme accumulated knowledge that enabled them ever more reliably to inflict their characteristic style of damage on their human victims. Like genes, they may also confer benefits, though, even then, they are unlikely to do so optimally. Just as genes for the eye implicitly ‘know’ the laws of optics, so the long-lived memes of a static society implicitly possess knowledge of the human condition, and use it mercilessly to evade the defences and exploit the weaknesses of the human minds that they enslave. — page 383

15.5.3 It is a common belief that primitive (static) societies had a lot of happy people. What is the criticism within static memes viewpoint?

Static societies are characterized by the suppression of knowledge growth. Seeking growth of knowledge (scientific or not) is an essential part of pursuing happiness. Thus, not only pursuit of knowledge, but also pursuit of happiness was suppressed.

This directly contradicts the widely held belief that individuals in primitive societies were happy in a way that has not been possible since – that they were unconstrained by social convention and other imperatives of civilization, and hence were able to achieve self-expression and fulfilment of their needs and desires. But primitive societies (including tribes of hunter-gatherers) must all have been static societies, because if ever one ceased to be static it would soon cease to be primitive, or else destroy itself by losing its distinctive knowledge. In the latter case, the growth of knowledge would still be inhibited by the raw violence which would immediately replace the static society’s institutions. For once violence is mediating changes, they will typically not be for the better. Since static societies cannot exist without effectively extinguishing the growth of knowledge, they cannot allow their members much opportunity to pursue happiness. (Ironically, creating knowledge is itself a natural human need and desire, and static societies, however primitive, ‘unnaturally’ suppress it.) From the point of view of every individual in such a society, its creativity-suppressing mechanisms are catastrophically harmful. Every static society must leave its members chronically baulked in their attempts to achieve anything positive for themselves as people, or indeed anything at all, other than their meme-mandated behaviours. It can perpetuate itself only by suppressing its members’ self-expression and breaking their spirits, and its memes are exquisitely adapted to doing this. — page 386

15.6.0 What sort of meme can cause itself to be replicated for long periods in a rapidly changing environment?

A truthful one!

To be transferred to a single person, a meme need seem useful only to that person. To be transferred to a group of similar people under unchanging circumstances, it need be only a parochial truth. But what sort of idea is best suited to getting itself adopted many times in succession by many people who have diverse, unpredictable objectives? A true idea is a good candidate. But not just any truth will do. It must seem useful to all those people, for it is they who will be choosing whether to enact it or not. … And the best way to seem useful to diverse people under diverse, unpredictable circumstances is to be useful. Such an idea is, or embodies, a truth in the broadest sense: factually true if it is an assertion of fact, beautiful if it is an artistic value or behaviour, objectively right if it is a moral value, funny if it is a joke, and so on. — page 387

But because replication of memes is so imprecise, it cannot be just a truthful idea, it must be a deep truth.

The ideas with the best chance of surviving through many generations of change are truths with reach – deep truths. People are fallible; they often have preferences for false, shallow, useless or morally wrong ideas. But which false ideas they prefer differs from one person to another, and changes with time. Under changed circumstances, a specious falsehood or parochial truth can survive only by luck. But a true, deep idea has an objective reason to be considered useful by people with diverse purposes over long periods. For instance, Newton’s laws are useful for building better cathedrals, but also for building better bridges and designing better artillery. Because of this reach, they get themselves remembered and enacted by all sorts of people, many of them vehemently opposed to each other’s objectives, over many generations. This is the kind of idea that has a chance of becoming a long-lived meme in a rapidly changing society. — page 388

Rational and Anti-rational Memes

15.7.0 What are rational and anti-rational memes?

Rational meme An idea that relies on the recipients’ critical faculties to cause itself to be replicated.
Anti-rational meme An idea that relies on disabling the recipients’ critical faculties to cause itself to be replicated. — page 396

15.7.1 What is the eventual progression of rational and anti-rational memes?

With time rational memes evolve closer to truth, and anti-rational ones further from it.

If a certain type of hobgoblin has the property that, if children fear it, they will grow up to make their children fear it, then the behaviour of telling stories about that type of hobgoblin is a meme. …
suppose it is an anti-rational meme. Evoking unpleasant emotions will then be useful in doing the harm that it needs to do namely disabling the listener’s ability to be rid of the hobgoblin and entrenching the compulsion to think and therefore speak of it. The more accurately the hobgoblin’s attributes exploit genuine, widespread vulnerabilities of the human mind, the more faithfully the anti-rational meme will propagate. If the meme is to survive for many generations, it is essential that its implicit knowledge of these vulnerabilities be true and deep. But its overt content – the idea of the hobgoblin’s existence – need contain no truth. On the contrary, the non-existence of the hobgoblin helps to make the meme a better replicator, because the story is then unconstrained by the mundane attributes of any genuine menace, which are always finite and to some degree combatable. And that will be all the more so if the story can also manage to undermine the principle of optimism. Thus, just as rational memes evolve towards deep truths, anti-rational memes evolve away from them. — page 389

15.7.2 How each type of meme is compatible with static and dynamic society?

The prevailing type of memes in the society defines the society. Memes replication strategy makes it harder to survive for its opposites. So in a dynamic society, which is dominated by rational memes, it is harder for anti-rational ones to survive, the opposite is true in a static society.

a rational meme’s natural home is a dynamic society – more or less any dynamic society – because there the tradition of criticism (optimistically directed at problem-solving) will suppress variants of the meme with even slightly less truth. Moreover, the rapid progress will subject these variants to continually varying criteria of criticism, which again only deeply true memes have a chance of surviving. An anti-rational meme’s natural home is a static society – not any static society, but preferably the one in which it evolved – for all the converse reasons. And therefore each type of meme, when present in a society that is broadly of the opposite kind, is less able to cause itself to be replicated. — page 390

15.8.0 What is a dynamic society?

Dynamic culture/society One that is dominated by rational memes. — page 396

15.9.0 How static and dynamic society framework applies to us nowadays?

We are a ‘young’ dynamic society, that is, rational memes dominate only in some areas, such as physical sciences, Western political and economic institutions.

Many other parts of our life’s are still filled with anti-rationalities. Consider the severe social repercussions a man would face by putting some red paint on his lips. Why? How exactly having an XY chromosome makes it ‘impossible’ for one to appreciate styling of ones face? Or ones nails?

One need look no further than our clothing styles, and the way we decorate our homes, to find evidence. Consider how you would be judged by other people if you went shopping in pyjamas, or painted your home with blue and brown stripes. That gives a hint of the narrowness of the conventions that govern even these objectively trivial and inconsequential choices about style, and the steepness of the social costs of violating them. Is the same thing true of the more momentous patterns in our lives, such as careers, relationships, education, morality, political outlook and national identity? — page 392

15.10.0 How one can notice and eliminate anti-rational memes?

Anti-rational memes hide, they professionally abuse human blunders, so noticing them is the hardest part. Elimination requires just a sliver of criticism.

Be worrisome when you don’t question some idea. Be worrisome of conditions for anti-rational memes to foster, like coercion, appeals to authority and so on.

Memes hide, but, just as with the optical blind spot, there is nothing to prevent our using a combination of explanation and observation to detect a meme and discover its implicit content indirectly.
For example, whenever we find ourselves enacting a complex or narrowly defined behaviour that has been accurately repeated from one holder to the next, we should be suspicious. If we find that enacting this behaviour thwarts our efforts to attain our personal objectives, or is faithfully continued when the ostensible justifications for it disappear, we should become more suspicious. If we then find ourselves explaining our own behaviour with bad explanations, we should become still more suspicious. …
Another thing that should make us suspicious is the presence of the conditions for anti-rational meme evolution, such as deference to authority, static subcultures and so on. Anything that says ‘Because I say so’ or ‘It never did me any harm,’ anything that says ‘Let us suppress criticism of our idea because it is true,’ suggests static-society thinking. We should examine and criticize laws, customs and other institutions with an eye to whether they set up conditions for anti-rational memes to evolve. Avoiding such conditions is the essence of Popper’s criterion. — page 395

🙊 16 — The Evolution of Creativity

We have evolved to be creative. This is what makes us unique, this is why we have landed a man on the moon and have zoos with us as visitors, not inhabitants. Yet, innovation can’t be an evolutionary pressure, for our tools were evolving only every thousands of years.

What use was creativity? What was the evolutionary pressure for it?

Summary

There was an evolutionary pressure for creativity not because it yields innovation, but because we use it to understand and replicate memes. Those that better understood societal norms, adhered better, and were sexually rewarded, hence the evolutionary pressure.

Ability to innovate is just a lucky by-product of understanding societal norms, for understanding nature is about the same problem of finding a hidden explanation.

Understanding societal norms cannot be done through induction, or any other deriving from external environment. Just as with scientific knowledge, it is about guessing and criticizing. Knowledge always comes from the individual, not into them. Even reading a book isn’t about putting knowledge ‘into you’, all the understanding that you get comes from within you, from your guesses and explanations about the books content.

You can practice chapter questions as flashcards here.

16.1.0 We have evolved to have creativity, but what was the evolutionary pressure for it? What use was creativity?

It cannot be innovation, for then we would notice tool change on the generational timescale, not every few thousands of years. Our ancestors didn’t use it for innovation.

If we did not know better, the natural answer would be that they were using it as we do today, for innovation and for understanding the world, in order to improve their lives. For instance, individuals who could improve stone tools would have ended up with better tools, and hence with better food and more surviving offspring. They would also have been able to make better weapons, thus denying the holders of rival genes access to food and mates – and so on. Yet if that had happened, the palaeontological record would show those improvements happening on a timescale of generations. But it does not.
…
Before the beginning of agriculture, about 12,000 years ago, many thousands of years passed between noticeable changes. It is as though each small genetic improvement in creativity produced just one noticeable innovation and then nothing more ****— page 399

One guess is that those with more creativity could easier manipulate the group and get social status. But that cannot be full explanation, for that still doesn’t explain why someone didn’t use creativity for innovation to gain social acceptance.

A more plausible variant of the sexual-selection theory is that people chose mates according to social status, rather than favouring creativity directly. Perhaps the most creative individuals were able to gain status more effectively though intrigue or other social manipulation. This could have given them an evolutionary advantage without producing any progress of which we would see evidence. However, all such theories still face the problem of explaining why, if creativity was being used intensively for any purpose, it was not also used for functional purposes. Why would a chief who had gained power through creative intrigue not be thinking about better spears for hunting? — page 401

16.1.1 Whatever creativity was used for in the first place, made its use for functional purposes (of innovation) rare. What could it be? What is David’s explanation?

People used creativity to uniquely conform to the rituals; ‘unique’ display of obedience was rewarded.

From the discussion in the previous chapter, one might guess that it was because the tribes or families in which people were living were static societies, in which any noticeable innovation would reduce one’s status and hence presumably one’s eligibility to mate. So how does one gain status, specifically by exercising more creativity than anyone else, without becoming noticeable as a taboo-violator?
I think there is only one way: it is to enact that society’s memes more faithfully than the norm. To display exceptional conformity and obedience. To refrain exceptionally well from innovation. A static society has no choice but to reward that sort of conspicuousness. — page 402

16.2.0 How do you replicate a meaning (i.e. meme)?

Replicating a meme, as we have discussed, is hard. One cannot ‘copy’ it from someone’s brain. It can’t be mere ‘imitation’, for that is a remedy of induction. In fact, there is no other way but to guess it. All what humans are doing when trying to replicate a meaning (a meme) is making guesses about it and criticizing them.

It cannot be ‘imitation’ because just as with induction we would be lost within the infinite complexity of things to ‘imitate’:

Popper used to begin his lecture course on the philosophy of science by asking the students simply to ‘observe’. Then he would wait in silence for one of them to ask what they were supposed to observe. This was his way of demonstrating one of many flaws in the empiricism that is still part of common sense today. So he would explain to them that scientific observation is impossible without pre-existing knowledge about what to look at, what to look for, how to look, and how to interpret what one sees. And he would explain that, therefore, theory has to come first. It has to be conjectured, not derived.
Popper could have made the same point by asking his audience to imitate, rather than merely to observe. The logic would have been the same: under what explanatory theory should they ‘imitate’? Whom should they imitate? Popper? In that case, should they walk to the podium, push him out of the way, and stand where he had been standing? If not, should they at least turn to face the rear of the room, to imitate where he was facing? Should they imitate his heavy Austrian accent, or should they speak in their normal voices, because he was speaking in his normal voice? … There are infinitely many possible interpretations of ‘imitate Popper’, each defining a different behaviour for the imitator. Many of those ways would look very different from each other. Each way corresponds to a different theory of what ideas, in Popper’s mind, were causing the observed behaviour.
So there is no such thing as ‘just imitating the behaviour’ – still less, therefore, can one discover those ideas by imitating it. One needs to know the ideas before one can imitate the behaviour. So imitating behaviour cannot be how we acquire memes. — page 403

16.2.1 If we can’t merely imitate, due to infinite ambiguity: What process apes use to replicate memes? Such as how to crack a nut, or fish a termite?

David errs on the side of behavior parsing. A technique that was proposed by animal-behavior researcher Richard Byrne:

In a remarkable series of observational and theoretical studies, the evolutionary psychologist and animal-behaviour researcher Richard Byrne has shown how they achieve this by a process that he calls behaviour parsing (which is analogous to the grammatical analysis or ‘parsing’ of human speech or computer programs).
Humans and computers separate continuous streams of sounds or characters into individual elements such as words, and then interpret those elements as being connected by the logic of a larger sentence or program. Similarly, in behaviour parsing (which evolved millions of years before human language parsing), an ape parses a continuous stream of behaviour that it witnesses into individual elements, each of which it already knows – genetically – how to imitate. The individual elements can be inborn behaviours, such as biting; or behaviours learned by trial and error, such as grasping a nettle without being stung; or previously learned memes. As for connecting these elements together in the right way without knowing why, it turns out that, in every known case of complex behaviours in non-humans, the necessary information can be obtained merely by watching the behaviour many times and looking out for simple statistical patterns – such as which right-hand behaviour often goes with which left-hand behaviour, and which elements are often omitted. It is a very inefficient method, requiring a lot of watching of behaviours that a human could mimic almost immediately by understanding their purpose. Also, it allows only a few fixed options for connecting the behaviours together, so only relatively simple memes can be replicated. Apes can copy certain individual actions instantly – the ones of which they have pre-existing knowledge through their mirror-neuron system – but it takes them years to learn a repertoire of memes that involve combinations of actions. — page 407

16.3.0 We have discussed two problems in this chapter: The first is why human creativity was evolutionarily advantageous at a time when there was almost no innovation. The second is how human memes can possibly be replicated, given that they have content that the recipient never observes. What is the solution to both of these problems?

Creativity. We rely on creativity to understand memes. Those that better understood societal norms deviated less and had sexual advantage.

I think that both those puzzles have the same solution: what replicates human memes is creativity; and creativity was used, while it was evolving, to replicate memes. In other words, it was used to acquire existing knowledge, not to create new knowledge. But the mechanism to do both things is identical, and so in acquiring the ability to do the former, we automatically became able to do the latter. It was a momentous example of reach, which made possible everything that is uniquely human.
A person acquiring a meme faces the same logical challenge as a scientist. Both must discover a hidden explanation. For the former, it is an idea in the minds of other people; for the latter, a regularity or law of nature. Neither person has direct access to this explanation. But both have access to evidence with which explanations can be tested: the observed behaviour of people who hold the meme, and physical phenomena conforming to the law. — page 411

The problem with inductivism and solutions alike to imitation is that they assume that external environment presents both a problem and a solution to it. But solution comes from the individual, not into the individual:

Popper wrote:
The inductivist or Lamarckian approach operates with the idea of instruction from without, or from the environment. But the critical or Darwinian approach only allows instruction from within – from within the structure itself . . .
I contend that there is no such thing as instruction from without the structure. We do not discover new facts or new effects by copying them, or by inferring them inductively from observation, or by any other method of instruction by the environment. We use, rather, the method of trial and the elimination of error. As Ernst Gombrich says, ‘making comes before matching’: the active production of a new trial structure comes before its exposure to eliminating tests. — The Myth of the Framework
Popper could just as well have written, ‘We do not acquire new memes by copying them, or by inferring them inductively from observation, or by any other method of imitation of, or instruction by, the environment.’ The transmission of human-type memes – memes whose meaning is not mostly predefined within the receiver – cannot be other than a creative activity on the part of the receiver.
Memes, like scientific theories, are not derived from anything. They are created afresh by the recipient. They are conjectural explanations, which are then subjected to criticism and testing before being tentatively adopted. — page 411

16.4.0 How does creativity help one to be less innovative?

We use creativity to understand memes (norms) of the society. In static societies adherence to norms is rewarded, innovation disrupts status quo and is punished. Hence, we used creativity to be more obedient, and thus, less innovative.

🗿 17 — Unsustainable

What is sustainable? There hasn’t been a better time to be born than nowadays, yet, do we want to sustain such lifestyle? Do we want to forever engrave our current mistakes? Or do we want to correct them?

Sustainability in the parochial sense can never be sustained — it will be crushed by a first unforeseeable problem. What then, must be the desired policy? How can we sustain ourselves?

Summary

Sustainability in keeping the things as they are is a mistake. What will we sustain? Our current mistakes? Mortality, slavery and starvation? Even if one desires to engrave nowadays lifestyle, ‘good resource management’ can’t be a solution. For it solves existing problems, not unexpected ones like COVID.

The only thing that is sustainable is progress. No policy escapes problems, so we must choose one that deals with them in the best way. Progress and increase of wealth will always put us at the best possible position to deal with problems.

How do we increase wealth? By promoting knowledge creation and hence, people. Each person is a universal constructor, a lottery ticket to solve some important problem.

To survive we need more wealth, we need more knowledge and we need more people. Progress is the only sustainable option.

You can practice chapter questions as flashcards here.

17.1.0 What are the two opposite ways of perceiving statues in the Easter Island?

First, is that this is a relic of a civilization that died out because it didn’t know how to solve its problems. It was a static society that persevered in its parochialism (of building statues as an answer to all its problems) and got what it deserved.

Second, is that this is an “astonishing stone sculptures ... vivid evidence of the technological and artistic skills of the people who once lived here”. Easter Island itself is a “miniature world” inhabitants of which didn’t use their resources properly and got what they deserved.

17.1.1 What are the conclusions we draw from these views?

First view: If we persevere in parochialism, we just like islanders, will die. Yet we are not a static society. We are in the unique place in history, for the first time progress is stable.

Second view: A variation of the Spaceship Earth idea. We are provided with great goods and benefits, which, if not carefully used would be depleted.

17.1.2 Elaborate on the first view.

We are not like them. We have a stable progress. We don’t need to be parochially constrained by resources, which is actually a costrain in knowledge. We have the tools to solve our problems, it is up to us to use them or not.

the central message of his series – which is also a theme of this book – that our civilization is unique in history for its capacity to make progress. He wanted to celebrate its values and achievements, and to attribute the latter to the former, and to contrast our civilization with the alternative as epitomized by ancient Easter Island.
…
He remarked, ‘People often ask about Easter Island, How did men come here? They came here by accident: that is not in question. The question is, Why could they not get off?’ And why, he might have added, did others not follow to trade with them (there was a great deal of trade among Polynesians other than Easter Islanders), or to rob them, or to learn from them? Because they did not know how.
…
The statues were all made alike because Easter Island was a static society. It never took that first step in the ascent of man – the beginning of infinity.
Of the hundreds of statues on the island, built over the course of several centuries, fewer than half are at their intended destinations. The rest, including the largest, are in various stages of completion, with as many as 10 per cent already in transit on specially built roads. Again there are conflicting explanations, but, according to the prevailing theory, it is because there was a large increase in the rate of statue building just before it stopped for ever. In other words, as disaster loomed, the islanders diverted ever more effort not into addressing the problem – for they did not know how to do that – but into making ever more and bigger (but very rarely better) monuments to their ancestors. And what were those roads made of? Trees.
… [Bronowski’s] whole purpose in going to Easter Island was to point out the profound difference between our civilization and civilizations like the one that built those statues. We are not like them was his message. We have taken the step that they did not. — page 419, 420

17.1.3 Elaborate on the second view.

The main lesson we should learn is of good resource management, to sustain us it our ways.

Attenborough’s argument rests on the opposite claim: we are like them and are following headlong in their footsteps. And so he drew an extended analogy between the Easter Island civilization and ours, feature for feature, and danger for danger:
A warning of what the future could hold can be seen on one of the remotest places on Earth . . . When the first Polynesian settlers landed here they found a miniature world that had ample resources to sustain them. They lived well ... — The State of the Planet (BBC TV, 2000)
A miniature world: there, in three words, is Attenborough’s reason for travelling all the way to Easter Island and telling its story. He believed that it holds a warning for the world because Easter Island was itself a miniature world – a Spaceship Earth – that went wrong. It had ‘ample resources’ to sustain its population, just as the Earth has seemingly ample resources to sustain us. (Imagine how amazed Malthus would have been had he known that the Earth’s resources would still be called ‘ample’ by pessimists in the year 2000.) Its inhabitants ‘lived well’, just as we do. And yet they were doomed, just as we are doomed unless we change our ways. If we do not, here is ‘what the future could hold’:
The old culture that had sustained them was abandoned and the statues toppled. What had been a rich, fertile world in miniature had become a barren desert.

But the problem is, that even though there hasn’t been a better time to be alive, it is still full of suffering compared to future societies that would continue progress. Why do we need to be mortal? Why do we need to be bound to one planet? Why do we need to have slavery?

So Attenborough’s idea is to sustain what? Death and suffering? This failure of imagination of things could be better is stunning.

Thus Attenborough’s Easter Island is a variant of Spaceship Earth: humans are sustained jointly by the ‘rich, fertile’ biosphere and the cultural knowledge of a static society. In this context, ‘sustain’ is an interestingly ambiguous word. It can mean providing someone with what they need. But it can also mean preventing things from changing – which can be almost the opposite meaning, for the suppression of change is seldom what human beings need.
The knowledge that currently sustains human life in Oxfordshire does so only in the first sense: it does not make us enact the same, traditional way of life in every generation. In fact it prevents us from doing so. For comparison: if your way of life merely makes you build a new, giant statue, you can continue to live afterwards exactly as you did before. That is sustainable. But if your way of life leads you to invent a more efficient method of farming, and to cure a disease that has been killing many children, that is unsustainable. The population grows because children who would have died survive; meanwhile, fewer of them are needed to work in the fields. And so there is no way to continue as before. You have to live the solution, and to set about solving the new problems that this creates. It is because of this unsustainability that the island of Britain, with a far less hospitable climate than the subtropical Easter Island, now hosts a civilization with at least three times the population density that Easter Island had at its zenith, and at an enormously higher standard of living. Appropriately enough, this civilization has knowledge of how to live well without the forests that once covered much of Britain. — page 421

17.2.0 What is the main mistake of Diamond’s, Engel’s and Marx’s explanation of humanity’s history?

They treat humans mechanically, as predictive observers of the history, not its creators. But this is a reductive mistake. Humanity’s history is shaped by people’s choices and created knowledge. Knowledge determines which resources are of use, and which aren’t.

Knowledge is a powerful force in the universe. It should be of no surprise that it is the biggest explanatory variable of humanity’s history, for eventually it will be of universe’s history.

Coincidentally, one of the things that was most false about the Soviet ideology was the very idea that there is an ultimate explanation of history in mechanical, non-human terms, as proposed by Marx, Engels and Diamond. Quite generally, mechanical reinterpretations of human affairs not only lack explanatory power, they are morally wrong as well, for in effect they deny the humanity of the participants, casting them and their ideas merely as side effects of the landscape. Diamond says that his main reason for writing Guns, Germs and Steel was that, unless people are convinced that the relative success of Europeans was caused by biogeography, they will for ever be tempted by racist explanations. Well, not readers of this book, I trust! Presumably Diamond can look at ancient Athens, the Renaissance, the Enlightenment – all of them the quintessence of causation through the power of abstract ideas – and see no way of attributing those events to ideas and to people; he just takes it for granted that the only alternative to one reductionist, dehumanizing reinterpretation of events is another.
In reality, the difference between Sparta and Athens, or between Savonarola and Lorenzo de’ Medici, had nothing to do with their genes; nor did the difference between the Easter Islanders and the imperial British. They were all people – universal explainers and constructors. But their ideas were different. Nor did landscape cause the Enlightenment. It would be much truer to say that the landscape we live in is the product of ideas. The primeval landscape, though packed with evidence and therefore opportunity, contained not a single idea. It is knowledge alone that converts landscapes into resources, and humans alone who are the authors of explanatory knowledge and hence of the uniquely human behaviour called ‘history’. — page 428

17.3.0 How societal pessimists and optimists differ in their perception of people?

Pessimistic - people are wasters of valuable resources.

Optimistic - people are creators, universal transformers.

[What stands out] is the contrast between two different conceptions of what people are. In the pessimistic conception, they are wasters: they take precious resources and madly convert them into useless coloured pictures. This is true of static societies: those statues really were what my colleague thought colour televisions are – which is why comparing our society with the ‘old culture’ of Easter Island is exactly wrong. In the optimistic conception – the one that was unforeseeably vindicated by events – people are problem-solvers: creators of the unsustainable solution and hence also of the next problem. In the pessimistic conception, that distinctive ability of people is a disease for which sustainability is the cure. In the optimistic one, sustainability is the disease and people are the cure.— page 435

17.4.0 What is the main insight David draws about what is sustainable and not?

Problems are inevitable, with or without ‘good resource management’. Even if we do everything that ‘Malthusians’ propose and become ‘sustainable’ in their sense, we won’t be. First, why desire sustainability of this level? (There still is mortality, slavery and starvation.) Second, there will always be unforeseeable problems, like COVID. You can’t escape them by having ‘sustainable resource management’. In fact, nothing can escape them, we will always face unexpected problems. Hence, the question becomes not of “How to escape problems?”, but of “How to prepare for problems?”. So I ask: Are we in a better position to deal with unexpected problems with less or more wealth?

We will always have problems, foreseeable and not; ‘sustainability’ that Malthusians propose will crumble in the face of the first unexpected challenge, so it is unsustainable. The only thing that is sustainable is progress. We can’t escape problems, our best hope is to constantly solve them.

So there is no resource-management strategy that can prevent disasters, just as there is no political system that provides only good leaders and good policies, nor a scientific method that provides only true theories. But there are ideas that reliably cause disasters, and one of them is, notoriously, the idea that the future can be scientifically planned. The only rational policy, in all three cases, is to judge institutions, plans and ways of life according to how good they are at correcting mistakes: removing bad policies and leaders, superseding bad explanations, and recovering from disasters.
…
Prevention and delaying tactics are useful, but they can be no more than a minor part of a viable strategy for the future. Problems are inevitable, and sooner or later survival will depend on being able to cope when prevention and delaying tactics have failed. Obviously we need to work towards cures. But we can do that only for diseases that we already know about. So we need the capacity to deal with unforeseen, unforeseeable failures. For this we need a large and vibrant research community, interested in explanation and problem-solving. We need the wealth to fund it, and the technological capacity to implement what it discovers.
…
There is as yet no serious sign of retreat into a sustainable lifestyle (which would really mean achieving only the semblance of sustainability), but even the aspiration is dangerous. For what would we be aspiring to? To forcing the future world into our image, endlessly reproducing our lifestyle, our misconceptions and our mistakes. But if we choose instead to embark on an open-ended journey of creation and exploration whose every step is unsustainable until it is redeemed by the next – if this becomes the prevailing ethic and aspiration of our society – then the ascent of man, the beginning of infinity, will have become, if not secure, then at least sustainable. — page 436, 437, 441

17.4.1 Illustrate this point on the climate change example.

Consider what would the situation be if climate change have happened hundred years prior to nowadays.

Consider, therefore: what if the relevant parameters had been just slightly different and the moment of disaster had been in, say, 1902 – Veblen’s time – when carbon-dioxide emissions were already orders of magnitude above their pre-Enlightenment values. Then the disaster would have happened before anyone could have predicted it or known what was happening. Sea levels would have risen, agriculture would have been disrupted, millions would have begun to die, with worse to come. And the great issue of the day would have been not how to prevent it but what could be done about it. — page 438

They would have insufficient wealth to deal with the problem, causing suffering of billions:

They had no supercomputers then. Because of Babbage’s failures and the scientific community’s misjudgements – and, perhaps most importantly, their lack of wealth – they lacked the vital technology of automated computing altogether. Mechanical calculators and roomfuls of clerks would have been insufficient. But, much worse: they had almost no atmospheric physicists. In fact the total number of physicists of all kinds was a small fraction of the number who today work on climate change alone. From society’s point of view, physicists were a luxury in 1902, like colour televisions were in the 1970s. Yet, to recover from the disaster, society would have needed more scientific knowledge, and better technology, and more of it – that is to say, more wealth. For instance, in 1900, building a sea wall to protect the coast of a low-lying island would have required resources so enormous that the only islands that could have afforded it would have been those with either large concentrations of cheap labour or exceptional wealth, as in the Netherlands, much of whose population already lived below sea level thanks to the technology of dyke-building.
This is a challenge that is highly susceptible to automation. But people were in no position to address it in that way. All relevant machines were underpowered, unreliable, expensive, and impossible to produce in large numbers. An enormous effort to construct a Panama canal had just failed with the loss of thousands of lives and vast amounts of money, due to inadequate technology and scientific knowledge. And, to compound those problems, the world as a whole had very little wealth by today’s standards. Today, a coastal defence project would be well within the capabilities of almost any coastal nation – and would add decades to the time available to find other solutions to rising sea levels. — page 438

⬆️ 18 — The Beginning

“Ah this is not an end, this is not even the beginning of the end, but it is, perhaps, the end of the beginning.” — Winston Churchill

Summary

It is up to us, humans, to decide whether to engrave our current lifestyle, to imprison ourselves in the parochial error of mortality, starvation and slavery. Or to correct errors, progress and explore the stars, the universe and everything. We have all the tools we need. Everything that isn’t prohibited by the laws of physics is possible, in fact, it has happened somewhere in the multiverse. The question is: Which of these universes you will choose to be in?

I’m glad you got so far. I hope to see you further, up in the stars, the universe and everything.

Mark

18.1.0 What is David’s criticism of the singularity idea?

Humans are universal explainers, that means they can understand anything that is understood. So humans would be able to keep up with the technological progress.

One might counter-argue that even if in principle humans can understand anything (i.e. we are Turing complete), in practice our time and memory is constrained, so there is limit to the things we can understand. That would be true if not one issue. We can create brain implants that increase our computational speed and memory. In fact, we can implant our best computers, so our speed is ‘up-to-date’. As long as one doesn’t show a physical law that prohibits this, it is only a matter of knowing how.

One might persist, by saying that that would be not a human, but something else. So, what is a human then? Constraining us in our biological body is of course parochial. People are more cyborgs than one can imagine, for just because your phone is not connected to you by a wire, it doesn’t mean you don’t use it as a tool, as any cyborg would. We will have to answer what does it mean to be human. It would rely on our best explanations and theories, which would be improved through criticism.

In 1993 the mathematician Vernor Vinge wrote an influential essay entitled ‘The Coming Technological Singularity’, in which he estimated that, within about thirty years, predicting the future of technology would become impossible – an event that is now known simply as ‘the Singularity’. Vinge associated the approaching Singularity with the achievement of AI, and subsequent discussions have centred on that. I certainly hope that AI is achieved by then, but I see no sign yet of the theoretical progress that I have argued must come first. On the other hand, I see no reason to single out AI as a mould-breaking technology: we already have billions of humans.
Most advocates of the Singularity believe that, soon after the AI breakthrough, superhuman minds will be constructed and that then, as Vinge put it, ‘the human era will be over.’ But my discussion of the universality of human minds rules out that possibility. Since humans are already universal explainers and constructors, they can already transcend their parochial origins, so there can be no such thing as a superhuman mind as such. There can only be further automation, allowing the existing kind of human thinking to be carried out faster, and with more working memory, and delegating ‘perspiration’ phases to (non-AI) automata. A great deal of this has already happened with computers and other machinery, as well as with the general increase in wealth which has multiplied the number of humans who are able to spend their time thinking. This can indeed be expected to continue. For instance, there will be ever-more-efficient human–computer interfaces, no doubt culminating in add-ons for the brain. But tasks like internet searching will never be carried out by super-fast AIs scanning billions of documents creatively for meaning, because they will not want to perform such tasks any more than humans do. Nor will artificial scientists, mathematicians and philosophers ever wield concepts or arguments that humans are inherently incapable of understanding. Universality implies that, in every important sense, humans and AIs will never be other than equal.
Similarly, the Singularity is often assumed to be a moment of unprecedented upheaval and danger, as the rate of innovation becomes too rapid for humans to cope with. But this is a parochial misconception. During the first few centuries of the Enlightenment, there has been a constant feeling that rapid and accelerating innovation is getting out of hand. But our capacity to cope with, and enjoy, changes in our technology, lifestyle, ethical norms and so on has been increasing too, with the weakening and extinction of some of the anti-rational memes that used to sabotage it. In future, when the rate of innovation will also increase due to the sheer increasing clock rate and throughput of brain add-ons and AI computers, then our capacity to cope with that will increase at the same rate or faster: if everyone were suddenly able to think a million times as fast, no one would feel hurried as a result. Hence I think that the concept of the Singularity as a sort of discontinuity is a mistake. Knowledge will continue to grow exponentially or even faster, and that is astounding enough. — page 456

Our world, which is so much larger, more unified, more intricate and more beautiful than that of Eratosthenes, and which we understand and control to an extent that would have seemed godlike to him, is nevertheless just as mysterious, yet open, to us now as his was to him then. We have lit only a few candles here and there. We can cower in their parochial light until something beyond our ken snuffs us out, or we can resist. We already see that we do not live in a senseless world. The laws of physics make sense: the world is explicable. There are higher levels of emergence and higher levels of explanation. Profound abstractions in mathematics, morality and aesthetics are accessible to us. Ideas of tremendous reach are possible. But there is also plenty in the world that does not and will not make sense until we ourselves work out how to rectify it. Death does not make sense. Stagnation does not make sense. A bubble of sense within endless senselessness does not make sense. Whether the world ultimately does make sense will depend on how people – the likes of us – chose to think and to act.
Many people have an aversion to infinity of various kinds. But there are some things that we do not have a choice about. There is only one way of thinking that is capable of making progress, or of surviving in the long run, and that is the way of seeking good explanations through creativity and criticism. What lies ahead of us is in any case infinity. All we can choose is whether it is an infinity of ignorance or of knowledge, wrong or right, death or life. — the final page

The end?

By Mark Kagach. Dedicated to my family and humanity: Where would I be without you?

Feeling insignificant because the universe is large has exactly the same logic as feeling inadequate for not being a cow. — page 35
on any clear night, the chances are that your roof will be struck by evidence falling from the sky which, if you only knew what to look for and how, would win you a Nobel prize. — page 61
the existence of an unsolved problem in physics is no more evidence for a supernatural explanation than the existence of an unsolved crime is evidence that a ghost committed it — page 97
the problem has been not that the world is so complex that we cannot understand why it looks as it does, but it is that it is so simple that we cannot yet understand it — page 104
This also illustrates the emptiness of reductionism in philosophy. For if I ask you for advice about what objectives to pursue in life, it is no good telling me to do what the laws of physics mandate. I shall do that in any case. — page 122
There are the direct limitations imposed by the universal laws of physics – we cannot exceed the speed of light, and so on. Then there are the limitations of epistemology: we cannot create knowledge other than by the fallible method of conjecture and criticism; errors are inevitable, and only error-correcting processes can succeed or continue for long. — page 192
So, a computation or a proof is a physical process in which objects such as computers or brains physically model or instantiate abstract entities like numbers or equations, and mimic their properties. It is our window on the abstract. It works because we use such entities only in situations where we have good explanations saying that the relevant physical variables in those objects do indeed instantiate those abstract properties. — page 188
Are you denying your own existence now? When sophists do that, I usually take them at their word and stop arguing with them. — page 236
Here was an eminent physicist joking that he might be considered mad. Why? For claiming that his own equation – the very one for which he had won the Nobel prize – might be true. — page 310
We laugh at this silliness now – when we have microscopes that can see atoms – but the role of philosophy should have been to laugh at it then. — page 312
Hence in, say, palaeontology, we do not speak of the existence of dinosaurs millions of years ago as being ‘an interpretation of our best theory of fossils’: we claim that it is the explanation of fossils. — page 315
Next, one defines the ‘heritability’ of a trait as its degree of statistical correlation with how genetically related the people are. Again, that is a non-explanatory definition: according to it, whether one was a slave or not was once a highly ‘heritable’ trait in America: it ran in families. More generally, one acknowledges that statistical correlations do not imply anything about what causes what. But one adds the inductivist equivocation that ‘they can be suggestive, though.’ — page 318
Using the same logic on the slavery example, one could have concluded in 1860 that, say, 95 per cent of slavery is genetically determined and therefore beyond the power of political action to remedy. — page 319
analyse the results as follows. For each day, subtract the count of people entering from the count of those leaving. If the difference is not zero, then – and this is the key step in the study – call that difference the ‘spontaneous-human-creation count’ if it is positive, or the ‘spontaneous-human-destruction count’ if it is negative. If it is exactly zero, call it ‘consistent with conventional physics’.
The less competent your counting and tabulating are, the more often you will find those ‘inconsistencies with conventional physics’. Next, prove that non-zero results (the spontaneous creation or destruction of human beings) are inconsistent with conventional physics. Include this proof in your report, but also include a concession that extraterrestrial visitors would probably be able to harness physical phenomena of which we are unaware. Also, that teleportation to or from another location would be mistaken for ‘destruction’ (without trace) and ‘creation’ (out of thin air) in your experiment and that therefore this cannot be ruled out as a possible cause of the anomalies.
When headlines appear of the form ‘Teleportation Possibly Observed in City Museum, Say Scientists’ and ‘Scientists Prove Alien Abduction is Real,’ protest mildly that you have claimed no such thing, that your results are not conclusive, merely suggestive, and that more studies are needed to determine the mechanism of this perplexing phenomenon. — page 322
their beauty is not primarily in that content. It is in the form. — page 356
A static society involves – in a sense consists of – a relentless struggle to prevent knowledge from growing. — page 385
We should examine and criticize laws, customs and other institutions with an eye to whether they set up conditions for anti-rational memes to evolve. — page 396
the desirable future is one where we progress from misconception to ever better (less mistaken) misconception. I have often thought that the nature of science would be better understood if we called theories ‘misconceptions’ from the outset, instead of only after we have discovered their successors. Thus we could say that Einstein’s Misconception of Gravity was an improvement on Newton’s Misconception, which was an improvement on Kepler’s. The neo-Darwinian Misconception of Evolution is an improvement on Darwin’s Misconception, and his on Lamarck’s. — page 446
Infinite ignorance is a necessary condition for there to be infinite potential for knowledge. Rejecting the idea that we are ‘nearly there’ is a necessary condition for the avoidance of dogmatism, stagnation and tyranny. — page 447

if large numbers of observations conform to the theory, and none deviates from it, the theory is supposed to be justified - made more believable, probable or reliable. … The inductivist analysis of my discussion of shadows would therefore go something like this: ‘We make a series of observations of shadows, and see interference phenomena (stage 1). The results conform to what would be expected if there existed parallel universes which affect one another in certain ways. But at first no one notices this. Eventually (stage 2) someone forms the generalization that interference will always be observed under the given circumstances, and thereby induces the theory that parallel universes are responsible. With every further observation of interference (stage 3) we become a little more convinced of that theory. After a sufficiently long sequence of such observations, and provided that none of them ever contradicts the theory, we conclude (stage 4) that the theory is true. Although we can never be absolutely sure, we are for practical purposes convinced. — The Fabric of Reality, page 59

First, a generalized prediction is rarely a candidate for a new theory and our deepest theories seldom are generalizable. With the multiverse example we certainly have not observed first one universe, then second, then third, and then concluded there are trillions of them! {3.1 Induction and baby weight.}

Second, inductivism uses the same observations to ‘justify’ theories. Consider Russell’s chicken:

The chicken noticed that the farmer came every day to feed it. It predicted that the farmer would continue to bring food every day. Inductivists think that the chicken had ‘extrapolated’ its observations into a theory, and that each feeding time added justification to that theory. Then one day the farmer came and wrung the chicken’s neck. — The Fabric of Reality, page 60

Should the chicken believe that its feeding theory would become more certain (i.e. more justified) with each day (observation)? What if the chicken generalized a diametrically opposite theory? The same observations would then support it as well:

However, this line of criticism lets inductivism off far too lightly. It does illustrate the fact that repeated observations cannot justify theories, but in doing so it entirely misses (or rather, accepts) a more basic misconception: namely, that the inductive extrapolation of observations to form new theories is even possible. In fact, it is impossible to extrapolate observations unless one has already placed them within an explanatory framework. For example, in order to ‘induce’ its false prediction, Russell’s chicken must first have had in mind a false explanation of the farmer’s behaviour. Perhaps it guessed that the farmer harboured benevolent feelings towards chickens. Had it guessed a different explanation - that the farmer was trying to fatten the chickens up for slaughter, for instance - it would have ‘extrapolated’ the behaviour differently. Suppose that one day the farmer starts bringing the chickens more food than usual. How one extrapolates this new set of observations to predict the farmer’s future behaviour depends entirely on how one explains it. According to the benevolent-farmer theory, it is evidence that the farmer’s benevolence towards chickens has increased, and that therefore the chickens have even less to worry about than before. But according to the fattening-up theory, the behaviour is ominous - it is evidence that slaughter is imminent. — The Fabric of Reality, page 60

Depending on the chicken’s mood when farmer first time brought more food it would conclude that either it is about to die, or live a happy, long life. Induction provides no mechanism to distinguish theories.

{So chicken won’t be able to tell without explanatory knowledge which patterns to take seriously, and which ones not.}

the belief that we can start with pure observations alone, without anything in the nature of a theory, is absurd; as may be illustrated by the story of the man who dedicated his life to natural science, wrote down everything he could observe, and bequeathed his priceless collection of observations to the Royal Society to be used as inductive evidence. This story should show us that though beetles may profitably be collected, observations may not.
Twenty-five years ago I tried to bring home the same point to a group of physics students in Vienna by beginning a lecture with the following instructions: ‘Take pencil and paper; carefully observe, and write down what you have observed!’ They asked, of course, what I wanted them to observe. Clearly the instruction, ‘Observe!’ is absurd. (It is not even idiomatic, unless the object of the transitive verb can be taken as understood.) Observation is always selective. It needs a chosen object, a definite task, an interest, a point of view, a problem. And its description presupposes a descriptive language, with property words; it presupposes similarity and classification, which in their turn presuppose interests, points of view, and problems. ‘A hungry animal’, writes Katz, ‘divides the environment into edible and inedible things. An animal in flight sees roads to escape and hiding places . . . Generally speaking, objects change . . . according to the needs of the animal.’ We may add that objects can be classified, and can become similar or dissimilar, only in this way—by being related to needs and interests. This rule applies not only to animals but also to scientists. For the animal a point of view is provided by its needs, the task of the moment, and its expectations; for the scientist by his theoretical interests, the special problem under investigation, his conjectures and anticipations, and the theories which he accepts as a kind of background: his frame of reference, his ‘horizon of expectations’. — Conjectures and Refutations, page 61

Science aims to understand the reality, not merely predict it. Imagine we are given an oracle that can predict an outcome of any experiment but provides no explanations. For instrumentalists, science would be over!

1.3 On predicting a fair roulette:
One can also consider a fair roulette. It would be impossible to predict, but does it mean that science can’t understand it? Certainly not. With good explanation one can understand why predicting a fair roulette is impossible.

Prediction - even perfect, universal prediction - is simply no substitute for explanation. — The Fabric of Reality, page 5

It sounds superficially plausible because prediction is required to refute theories. It is a necessary part of the scientific method, but not its goal.

To say that the purpose of science is to make predictions is to confuse means with ends. Is the purpose of a spaceship to burn fuel? It is not. Its purpose is to travel from point A to point B and carry some load. The purpose of science is to explain the world and we do so by testing predictions of our most promising theories.

although prediction is not the purpose of science, it is part of the characteristic method of science. The scientific method involves postulating a new theory to explain some class of phenomena and then performing a crucial experimental test, an experiment for which the old theory predicts one observable outcome and the new theory another. One then rejects the theory whose predictions turn out to be false. Thus the outcome of a crucial experimental test to decide between two theories does depend on the theories’ predictions, and not directly on their explanations. This is the source of the misconception that there is nothing more to a scientific theory than its predictions. — The Fabric of Reality, page 6

Popperian epistemology consists of 5 stages: (1) Problem; (2) Conjectured solutions; (3) Criticism; (4) Replacement of wrong theories; (5) New problem.

after a problem presents itself (stage 1), the next stage always involves conjecture: proposing new theories, or modifying or reinterpreting old ones, in the hope of solving the problem (stage 2). The conjectures are then criticized which, if the criticism is rational, entails examining and comparing them to see which offers the best explanations, according to the criteria inherent in the problem (stage 3). When a conjectured theory fails to survive criticism - that is, when it appears to offer worse explanations than other theories do - it is abandoned. If we find ourselves abandoning one of our originally held theories in favour of one of the newly proposed ones (stage 4), we tentatively deem our problem-solving enterprise to have made progress. I say ‘tentatively’, because subsequent problem solving will probably involve altering or replacing even these new, apparently satisfactory theories, and sometimes even resurrecting some of the apparently unsatisfactory ones. — The Fabric of Reality, page 64

The distinguishing characteristic between scientific problem-solving is the use of experiments to rule out opposing theories (stage 3 in Popperian epistemology). How do we know that general relativity is better than Newtonian physics? We create an experiment where theories diverge in their predictions and then conduct it. Its outcome would refute one of the theories, so we conclude that the last man standing is our best explanation of reality, so far. It is a mistake to say that general relativity has been justified by the experiment, its outcome is just in-line with what the theory predicted. It doesn’t imply that the theory is the ultimate truth. In fact, theories can never be the ultimate truth, because they all are just our guesses, and we are fallible.

Scientific problem-solving always includes a particular method of rational criticism, namely experimental testing. Where two or more rival theories make conflicting predictions about the outcome of an experiment, the experiment is performed and the theory or theories that made false predictions are abandoned. The very construction of scientific conjectures is focused on finding explanations that have experimentally testable predictions. Ideally we are always seeking crucial experimental tests experiments whose outcomes, whatever they are, will falsify one or more of the contending theories. — The Fabric of Reality, page 65

Besides it being the means of realizing the Turing principle, it will decide the fate of universe (or big part of it). If one wants to predict and understand the universe, one would have to understand life, its culture, morality and technology, for its decisions will be the shape such of massive astrophysical objects as stars, planets and galaxies.

the point I am making here does not depend on our being able to predict what will happen, but only on the proposition that what will happen will depend on what knowledge our descendants have, and on how they choose to apply it. Thus one cannot predict the future of the Sun without taking a position on the future of life on Earth, and in particular on the future of knowledge. The colour of the Sun ten billion years hence depends on gravity and radiation pressure, on convection and nucleosynthesis. It does not depend at all on the geology of Venus, the chemistry of Jupiter, or the pattern of craters on the Moon. But it does depend on what happens to intelligent life on the planet Earth. It depends on politics and economics and the outcomes of wars. It depends on what people do: what decisions they make, what problems they solve, what values they adopt, and on how they behave towards their children. …
even if the human race will in the event fail in its efforts to survive, does the pessimistic theory apply to every extraterrestrial intelligence in the universe? If not - if some intelligent life, in some galaxy, will ever succeed in surviving for billions of years - then life is significant in the gross physical development of the universe. — The Fabric of Reality, page 184

In a broad sense it is something that causes the environment to replicate itself — like a song, or a molecule.

Such molecules are called replicators. More generally, a replicator is any entity that causes certain environments to copy it. Not all replicators are biological, and not all replicators are molecules. For example, a self-copying computer program (such as a computer virus) is a replicator. A good joke is another replicator, for it causes its listeners to retell it to further listeners. Richard Dawkins has coined the term meme (rhyming with ‘cream’) for replicators that are human ideas, such as jokes. — The Fabric of Reality, page 170

No. The replicator can’t replicate itself without the environment. A song can be spread only by its listeners (i.e. environment). The replicator causes its replication, if it is replaced by a random object an environment won’t spread it (depending on the song people will share it or not, not any song goes).

Not everything that can be copied is a replicator. A replicator causes its environment to copy it: that is, it contributes causally to its own copying. … What it means in general to contribute causally to some thing is an issue to which I shall return, but what I mean here is that the presence and specific physical form of the replicator makes a difference to whether copying takes place or not. In other words, the replicator is copied if it is present, but if it were replaced by almost any other object, even a rather similar one, that object would not be copied. … The presence of the gene in its proper form and location makes a difference to whether copying takes place, which makes it a replicator, though there are countless other causes contributing to its replication as well. — The Fabric of Reality, page 172

Indeed a virtual-reality generator that takes billions of years to compute is of little use. Usefulness of virtual reality and its real-life applications are crucial criterions for determining its profoundness.

Yet, rendering of certain properties was remarkably useful for evolution and science. It is because the rendered property does not have to be ‘100% truthful or accurate’, it just has to be a better guess than the previous one. And this is another aspect of reality that allows renderings to be useful — we can successively improve our guesses (be it genes or theories). It also shows that one can improve based on the imperfect information (be it a gene or a theory), in fact, it will always be imperfect.

The previous successes of virtual rendering (in evolution and science) with its resource and information limitations imply the same for the universal virtual-reality generator — it is possible to build and use it with reasonable amount of resources.

On criticism:

to be at all useful or significant in the overall scheme of things, universality as I have defined it up to now is not sufficient. It merely means that the universal computer can eventually do what any other computer can. In other words, given enough time it is universal. But what if it is not given enough time? Imagine a universal computer that could execute only one computational step in the whole lifetime of the universe. Would its universality still be a profound property of reality? Presumably not. To put that more generally, one can criticize this narrow notion of universality because it classifies a task as being in a computer’s repertoire regardless of the physical resources that the computer would expend in performing the task. Thus, for instance, we have considered a virtual-reality user who is prepared to go into suspended animation for billions of years, while the computer calculates what to show next. In discussing the ultimate limits of virtual reality, that is the appropriate attitude for us to take. But when we are considering the usefulness of virtual reality - or what is even more important, the fundamental role that it plays in the fabric of reality - we must be more discriminating. — The Fabric of Reality, page 196

Counterargument and its implications:

Thus the fact that there are complex organisms, and that there has been a succession of gradually improving inventions and scientific theories (such as Galilean mechanics, Newtonian mechanics, Einsteinian mechanics, quantum mechanics, ... ) tells us something more about what sort of computational universality exists in reality. It tells us that the actual laws of physics are, thus far at least, capable of being successively approximated by theories that give ever better explanations and predictions, and that the task of discovering each theory, given the previous one, has been computationally tractable, given the previously known laws and the previously available technology. The fabric of reality must be, as it were, layered, for easy self-access. Likewise, if we think of evolution itself as a computation, it tells us that there have been sufficiently many viable organisms, coded for by DNA, to allow better-adapted ones to be computed (i.e. to evolve) using the resources provided by their worse-adapted predecessors. So we can infer that the laws of physics, in addition to mandating their own comprehensibility through the Turing principle, ensure that the corresponding evolutionary processes, such as life and thought, are neither too time-consuming nor require too many resources of any other kind to occur in reality.
So, the laws of physics not only permit (or, as I have argued, require) the existence of life and thought, they require them to be, in some appropriate sense, efficient. To express this crucial property of reality, modern analyses of universality usually postulate computers that are universal in an even stronger sense than the Turing principle would, on the face of it, require: not only are universal virtual-reality generators possible, it is possible to build them so that they do not require impracticably large resources to render simple aspects of reality. — The Fabric of Reality, page 196

The view that science always explains things reductively — analyzing them into smaller components and appealing to past events as causes.

First, it disregards emergence — sometimes low-level complexity can yield high-level simplicity. For instance a cat is easier to predict and explain, than an interaction of trillions of atoms. Second, it believes that knowledge is always created by breaking down things into smaller components. However, this is false, we frequently understand things by appealing to high-level sciences. Let’s consider a particular copper atom at the tip of the nose of Winston Churchill’s statue in London. Why is it there? Breaking down the statue to its subatomic particles and trying to explain their position by previous particle interactions only leads to an infinite regress. Eventually we would arrive at the Big Bang, yet, we would still have no explanation or understanding of why that copper atom is there. Nonetheless, if we appeal to history and culture (emergent phenomena) the why is rather obvious:

It is because Churchill served as prime minister in the House of Commons nearby; and because his ideas and leadership contributed to the Allied victory in the Second World War; and because it is customary to honour such people by putting up statues of them; and because bronze, a traditional material for such statues, contains copper, and so on. — The Fabric of Reality, page 22

{1.6 For more details watch this.}
Second, reductionism assumes that knowledge is always created by appealing to earlier events (i.e. stating causes). Yet, this is false even in fundamental physics! How could we know so much about the initial state of the universe if we have no idea of what was before it? How could we understand the time so well?! What was before it?

To answer this question in The Fabric of Reality David uses Dr. Johnson criterion:

Dr Johnson’s criterion (My formulation) If it can kick back, it exists. A more elaborate version is: If, according to the simplest explanation, an entity is complex and autonomous, then that entity is real. — The Fabric of Reality, page 96

However, his latest version for criterion of reality is: Something is real or exist in so far as it appears in our best explanations of reality. Is any explanation ruined by denying numbers existence? Mathematics appears in our best explanations of reality, and hence, it exists.

It seems intuitive that mathematical entities exist in a different way than physical ones, but explaining how exactly is something we are yet to form a good theory about.

FoR: 10.1.1 How do we understand abstract entities?
Because they are intangible, we can’t conduct experiments as in regular science. In mathematics, proof plays the role of both experiment and explanation.
FoR: 10.1.2 What is the difference between proof and experiment?
It is believed that once we prove something, we know with absolute certainty that it is true. Experiments can never imply this, its knowledge is always fallible.

We can perform a proof in the privacy of our own minds, or we can perform a proof trapped inside a virtual-reality generator rendering the wrong physics. Provided only that we follow the rules of mathematical inference, we should come up with the same answer as anyone else. And again, the prevailing view is that, apart from the possibility of making blunders, when we have proved something we know with absolute certainty that it is true. — The Fabric of Reality, page 224

FoR: 10.1.3 Does proof imply that we know with absolute certainty that something is true?
The main thesis of the chapter: it does not.

Where does the certainty of a mathematical proof come from, if no one can perceive the abstract entities that the proof refers to? — The Fabric of Reality, page 227

Most mathematicians believe that scientific and mathematical knowledge comes from different sources. The source of the latter is mathematical intuition, it provides absolute certainty that scientists can never have, and allows us to argue about abstract entities that no one ever saw.

FoR: 10.2.1 What is the problem with such source? Give example.

Mathematicians can’t agree on what it exactly means!

Obviously this is a recipe for infinite, unresolvable - controversy. — The Fabric of Reality, page 227

A prominent example are imaginary numbers. Some mathematicians used it to prove theorems about the distribution of prime numbers, others appealed to the invalidity of such tools. Reasoning of people that used imaginary numbers was as follows:

Why, they thought, should one not define new abstract entities to have any properties one likes? Surely the only legitimate grounds for forbidding this would be that the required properties were logically inconsistent. ... Admittedly, no one had proved that the system of imaginary numbers was self-consistent. But then, no one had proved that the ordinary arithmetic of the natural numbers was self-consistent either. — The Fabric of Reality, page 228

Similar debates have been about infinities.

FoR: 10.2.2 Do we need to experience a perfect circle to understand it?

We don’t. Just as physicists understand distant stars they have never been to, so do mathematicians that have never seen perfect circles.

FoR: 10.2.3 How our imperfect physical circles are related to the abstracted ones? How do we understand the latter ones?

We use our creativity and best explanations to understand abstract entities. This implies we could always be wrong, just as with studying anything physical.

The reliability of the knowledge of a perfect circle that one can gain from a diagram of a circle depends entirely on the accuracy of the hypothesis that the two resemble each other in the relevant ways. Such a hypothesis, referring to a physical object (the diagram), amounts to a physical theory and can never be known with certainty. But that does not, as Plato would have it, preclude the possibility of learning about perfect circles from experience; it just precludes the possibility of certainty. That should not worry anyone who is looking not for certainty but for explanations. … [just as diagrams of perfect circle] The symbols too are physical objects - patterns of ink on paper, say - which denote abstract objects. And again, we are relying entirely upon the hypothesis that the physical behaviour of the symbols corresponds to the behaviour of the abstractions they denote. Therefore the reliability of what we learn by manipulating those symbols depends entirely on the accuracy of our theories of their physical behaviour, and of the behaviour of our hands, eyes, and so on with which we manipulate and observe the symbols. — The Fabric of Reality, page 241

The view that the basic purpose of science is to predict an experiment, not to explain the reality. Explanations for instrumentalists are no more than psychological props — empty words.

The important thing is to be able to make predictions about images on the astronomers’ photographic plates, frequencies of spectral lines, and so on, and it simply doesn’t matter whether we ascribe these predictions to the physical effects of gravitational fields on the motion of planets and photons [as in pre-Einsteinian physics] or to a curvature of space and time. — Gravitation and Cosmology, page 147

FoR: 1.4.1 What is the criticism of instrumentalism?

First, we would be interested in how an oracle works. Second, how would it help us to build a better spaceship? Oracle could be used to test our design, not create it. If it fails we would have no hint of why, just as with physical world. The **oracle is no different, it would only save us time and expenses on building a spaceship. Explaining the failure and improving the design would be on us, it would require understanding. {1.3 On predicting fair roulette. It would be impossible to predict, but does it mean that science can’t understand it? Certainly not. With good explanation one can understand why predicting a fair roulette is impossible.}

Prediction - even perfect, universal prediction - is simply no substitute for explanation. — The Fabric of Reality, page 5

First, let’s clarify how do we know whether some infinities are as big as others. We start with trying to create an algorithm to enumerate each number in the infinite set. For positive integers we start with 0 and then add 1, so the algorithm is: n+1. For all positive even integers it would be: n+2, starting from 0. Using these algorithms, we do ‘one-to-one correspondence’ to prove that an infinite set of positive integers is just as big as an infinite set of even integers:

This is the defining characteristic of an infinite set — some parts of it are as big as a whole.

Infinities are quite counter-intuitive, but it can get worst.

The essence of Cantor’s prove is that some infinities are enumerable (you have a starting point from which you can numerate them, like 0), while others aren’t. As we have shown, the infinity of positive integers is enumerable. Yet, the infinity of real numbers between 0 and 1 is non-enumerable — we start with … What? 0.00000001? Well no, because I could add another zero, and so on forever. Hence, there is no algorithm to list all the real numbers between 0 and 1 (Which has a very interesting implications in the theory of computation, but we’ll cover it in a bit!). This means that the infinite set of real numbers between 0 and 1 is bigger than the infinite set of positive integers. {If you are struggling to understand Cantor’s argument, this video might help.}

Coming back to our virtual-reality generator question. Whatever the repertoire of programs it has, they are listed in its memory. Even with infinite memory the matter of the fact is that they are listed. Yet, the set of all logically possible environments is unlistable (non-enumerable)! We can always find an environment that is not in the memory:

Now let us imagine this infinite set of possible programs arranged in an infinitely long list, and numbered Program 1, Program 2, and so on. … Let me define a class of logically possible environments which I shall call Cantgotu environments, partly in honour of Cantor, Godel and Turing, and partly for a reason I shall explain shortly. They are defined as follows. For the first subjective minute, a Cantgotu environment behaves differently from Environment 1 (generated by Program 1 of our generator). It does not matter how it does behave, so long as it is, to the user, recognizably different from Environment 1. During the second minute it behaves differently from Environment 2 (though it is now allowed to resemble Environment 1 again). During the third minute, it behaves differently from Environment 3, and so on. Any environment that satisfies these rules I shall call a Cantgotu environment. …
Now, since a Cantgotu environment does not behave exactly like Environment 1, it cannot be Environment 1; since it does not behave exactly like Environment 2, it cannot be Environment 2. Since it is guaranteed sooner or later to behave differently from Environment 3, Environment 4 and every other environment on the list, it cannot be any of those either. But that list contains all the environments that are generated by every possible program for this machine. It follows that none of the Cantgotu environments are in the machine’s repertoire. The Cantgotu environments are environments that we can’t go to using this virtual-reality generator.
Clearly there are enormously many Cantgotu environments, because the definition leaves enormous freedom in choosing how they should behave, the only constraint being that during each minute they should not behave in one particular way. It can be proved that, for every environment in the repertoire of a given virtual-reality generator, there are infinitely many Cantgotu environments that it cannot render. — The Fabric of Reality, page 127, 128

Classical computers calculate things using our universe; quantum computers use multiple universes at once to calculate different parts of the task and then share the results.

Quantum computation is more than just a faster, more miniaturized technology for implementing Turing machines. A quantum computer is a machine that uses uniquely quantum-mechanical effects, especially interference, to perform wholly new types of computation that would be impossible, even in principle, on any Turing machine and hence on any classical computer. Quantum computation is therefore nothing less than a distinctively new way of harnessing nature. …
There followed thousands of years of progress in this type of technology - harnessing some of the materials, forces and energies of physics. In the twentieth century information was added to this list when the invention of computers allowed complex information processing to be performed outside human brains. Quantum computation, which is now in its early infancy, is a distinct further step in this progression. It will be the first technology that allows useful tasks to be performed in collaboration between parallel universes. A quantum computer would be capable of distributing components of a complex task among vast numbers of parallel universes, and then sharing the results. — The Fabric of Reality, page 195

This is an algorithm for factorizing large prime numbers. All our current cryptography and cyber security is based on the simple fact that it is very easy to multiple two huge numbers, while extremely hard to factorize them back. This is an intractable calculation for our classical computers, but it isn’t for the quantum ones! All they have to do is to perform certain tasks in parallel and then share with each other the results through interference.

On number of universes cooperating:

When a quantum factorization engine is factorizing a 250-digit number, the number of interfering universes will be of the order of 10^500 - that is, ten to the power of 500. This staggeringly large number is the reason why Shor’s algorithm makes factorization tractable. I said that the algorithm requires only a few thousand arithmetic operations. I meant, of course, a few thousand operations in each universe that contributes to the answer. All those computations are performed in parallel, in different universes, and share their results through interference. — The Fabric of Reality, page 216

Simple example:

Imagine that for some computation we have to divide some big number by every number from 1 to a million. If we use classical computer we have to do it one at a time. If we use quantum computer we can split it into 10 universes (arbitrary choice, we could do 100, 1000 and so on) and perform the computation 10 times faster (or even faster depending on the number of chosen universes):

The only way to explain how Shor’s algorithm works is by appealing to the multiverse, otherwise there are simply not enough atoms in our universe:

To those who still cling to a single-universe world-view, I issue this challenge: explain how Shor’s algorithm works. I do not merely mean predict that it will work, which is merely a matter of solving a few uncontroversial equations. I mean provide an explanation. When Shor’s algorithm has factorized a number, using 10^500 or so times the computational resources that can be seen to be present, where was the number factorized? There are only about 10^80 atoms in the entire visible universe, an utterly minuscule number compared with 10^500. So if the visible universe were the extent of physical reality, physical reality would not even remotely contain the resources required to factorize such a large number. Who did factorize it, then? How, and where, was the computation performed? — The Fabric of Reality, page 217

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