The Beginning of Infinity: Explanations That Transform the World

by David Deutsch

as I mentioned in Chapter 3, the biosphere is much less pleasant for its inhabitants than anything that a benevolent, or even halfway decent, human designer would design.

The central flaw of creationism – that its account of how the knowledge in adaptations could possibly be created is either missing, supernatural or illogical – is also the central flaw of pre-Enlightenment, authoritative conceptions of human knowledge.

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.

Socrates was right to point out that the appearance of design in living things is something that needs to be explained. It cannot be the ‘product of chance’. And that is specifically because it signals the presence of knowledge.

For instance, the lenses in its eyes have a purpose similar to that of a telescope, of focusing light to form an image on its retina, which in turn has the purpose of recognizing food, danger and so on.

Charles Darwin distinguished the process that he discovered by calling it ‘evolution by natural selection’ – though a better name would have been ‘evolution by variation and selection’.

The central idea of neo-Darwinism is that evolution favours the genes that spread best through the population.

A common misconception about Darwinian evolution is that it maximizes ‘the good of the species’.

Thus, it is said, evolution optimizes the good of the species, not the individual. But, in reality, evolution optimizes neither.

A related misconception is that evolution is always adaptive – that it always constitutes progress, or at least some sort of improvement in useful functionality which it then acts to optimize.

Thus, although the existence of progress in the biosphere is what the theory of evolution is there to explain, not all evolution constitutes progress, and no (genetic) evolution optimizes progress.

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.

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.

Organisms are the slaves, or tools, that genes use to achieve their ‘purpose’ of spreading themselves through the population.

But what genes are adapted to – what they do better than almost any variant of themselves – has nothing to do with the species or the individuals or even their own survival in the long run. It is getting themselves replicated more than rival genes.

Neo-Darwinism does not refer, at its fundamental level, to anything biological. It is based on the idea of a replicator (anything that contributes causally to its own copying).

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.

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).

The most general way of stating the central assertion of the neo-Darwinian theory of evolution is that a population of replicators subject to variation (for instance by imperfect copying) will be taken over by those variants that are better than their rivals at causing themselves to be replicated.

So the knowledge embodied in genes is knowledge of how to get themselves replicated at the expense of their rivals.

But the core of the explanation for the presence of a gene is always that it got itself replicated more than its rival genes.

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.

But one day the genes of a rare species could survive its extinction by causing themselves to be stored on a computer and then implanted into a cell of a different species.

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.

The physicist Brandon Carter calculated in 1974 that if the strength of the interaction between charged particles were a few per cent smaller, no planets would ever have formed and the only condensed objects in the universe would be stars; and if it were a few per cent greater, then no stars would ever explode, and so no elements other than hydrogen and helium would exist outside them.

And, in any case, arguing for supernatural explanations on the grounds that a current scientific explanation is flawed or lacking is just a mistake. As we carved in stone in Chapter 3, problems are inevitable – there are always unsolved problems. But they get solved.

Therefore 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.

It is only in the universes that contain astrophysicists that anyone ever wonders why the constants seem fine-tuned.

The main similarities: genes and ideas are both replicators; knowledge and adaptations are both hard to vary. The main difference: human knowledge can be explanatory and can have great reach; adaptations are never explanatory and rarely have much reach beyond the situations in which they evolved.

Biological evolution does not optimize benefits to the species, the group, the individual or even the gene, but only the ability of the gene to spread through the population.

The guiding principle is, as always, to reject bad explanations in favour of good ones.

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 influences on them, which is itself an intractable task.

This has given rise to a widespread misconception about emergence and explanation, known as reductionism: the doctrine that science always explains and predicts things reductively, i.e. by analysing them into components.

Even in physics, some of the most fundamental explanations, and the predictions that they make, are not reductive.

All those doctrines are irrational for the same reason: they advocate accepting or rejecting theories on grounds other than whether they are good explanations.

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.

As I explained in Chapter 1, regarding the explanatory function of theories as paramount is not just an idle preference. The predictive function of science is entirely dependent on it.

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.

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.

There is no inconsistency in having multiple explanations of the same phenomenon, at different levels of emergence.

Our own brains are, likewise, computers which we can use to learn about things beyond the physical world, including pure mathematical abstractions.

Where did it come from? Plato concluded that it – and all human knowledge – must come from the supernatural.

But it is no mystery where our knowledge of abstractions comes from: it comes from conjecture, like all our knowledge, and through criticism and seeking good explanations.