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The Blind Watchmaker

The Blind Watchmaker

The Blind Watchmaker 7


  It is not impossible at all. That is exactly what I firmly believe, and I have some experience of spiders and their webs.

  The Bishop goes on to the human eye, asking rhetorically, and with the implication that there is no answer, ‘How could an organ so complex evolve?’ This is not an argument, it is simply an affirmation of incredulity. The underlying basis for the intuitive incredulity that we all are tempted to feel about what Darwin called organs of extreme perfection and complication is, I think, twofold. First we have no intuitive grasp of the immensities of time available for evolutionary change. Most sceptics about natural selection are prepared to accept that it can bring about minor changes like the dark coloration that has evolved in various species of moth since the industrial revolution. But, having accepted this, they then point out how small a change this is. As the Bishop underlines, the dark moth is not a new species. I agree that this is a small change, no match for the evolution of the eye, or of echolocation. But equally, the moths only took a hundred years to make their change. One hundred years seems like a long time to us, because it is longer than our lifetime. But to a geologist it is about a thousand times shorter than he can ordinarily measure!

  Eyes don’t fossilize, so we don’t know how long our type of eye took to evolve its present complexity and perfection from nothing, but the time available is several hundred million years. Think, by way of comparison, of the change that man has wrought in a much shorter time by genetic selection of dogs. In a few hundreds, or at most thousands, of years we have gone from wolf to Pekinese, Bulldog, Chihuahua and Saint Bernard. Ah, but they are still dogs aren’t they? They haven’t turned into a different ‘kind’ of animal? Yes, if it comforts you to play with words like that, you can call them all dogs. But just think about the time involved. Let’s represent the total time it took to evolve all these breeds of dog from a wolf, by one ordinary walking pace. Then, on the same scale, how far would you have to walk, in order to get back to Lucy and her kind, the earliest human fossils that unequivocally walked upright? The answer is about 2 miles. And how far would you have to walk, in order to get back to the start of evolution on Earth? The answer is that you would have to slog it out all the way from London to Baghdad. Think of the total quantity of change involved in going from wolf to Chihuahua, and then multiply it up by the number of walking paces between London and Baghdad. This will give some intuitive idea of the amount of change that we can expect in real natural evolution.

  The second basis for our natural incredulity about the evolution of very complex organs like human eyes and bat ears is an intuitive application of probability theory. Bishop Montefiore quotes C. E. Raven on cuckoos. These lay their eggs in the nests of other birds, which then act as unwitting foster parents. Like so many biological adaptations, that of the cuckoo is not single but multiple. Several different facts about cuckoos fit them to their parasitic way of life. For instance, the mother has the habit of laying in other birds’ nests, and the baby has the habit of throwing the host’s own chicks out of the nest. Both habits help the cuckoo succeed in its parasitic life. Raven goes on:

  It will be seen that each one of this sequence of conditions is essential for the success of the whole. Yet each by itself is useless. The whole opus perfectum must have been achieved simultaneously. The odds against the random occurrence of such a series of coincidences are, as we have already stated, astronomical.

  Arguments such as this are in principle more respectable than the argument based on sheer, naked incredulity. Measuring the statistical improbability of a suggestion is the right way to go about assessing its believability. Indeed, it is a method that we shall use in this book several times. But you have to do it right! There are two things wrong with the argument put by Raven. First, there is the familiar, and I have to say rather irritating, confusion of natural selection with ‘randomness’. Mutation is random; natural selection is the very opposite of random. Second, it just isn’t true that ‘each by itself is useless’. It isn’t true that the whole perfect work must have been achieved simultaneously. It isn’t true that each part is essential for the success of the whole. A simple, rudimentary, half-cocked eye/ear/echolocation system/cuckoo parasitism system, etc., is better than none at all. Without an eye you are totally blind. With half an eye you may at least be able to detect the general direction of a predator’s movement, even if you can’t focus a clear image. And this may make all the difference between life and death. These matters will be taken up again in more detail in the next two chapters.

  CHAPTER 3

  Accumulating small change

  We have seen that living things are too improbable and too beautifully ‘designed’ to have come into existence by chance. How, then, did they come into existence? The answer, Darwin’s answer, is by gradual, step-by-step transformations from simple beginnings, from primordial entities sufficiently simple to have come into existence by chance. Each successive change in the gradual evolutionary process was simple enough, relative to its predecessor, to have arisen by chance. But the whole sequence of cumulative steps constitutes anything but a chance process, when you consider the complexity of the final end-product relative to the original starting point. The cumulative process is directed by nonrandom survival. The purpose of this chapter is to demonstrate the power of this cumulative selection as a fundamentally nonrandom process.

  If you walk up and down a pebbly beach, you will notice that the pebbles are not arranged at random. The smaller pebbles typically tend to be found in segregated zones running along the length of the beach, the larger ones in different zones or stripes. The pebbles have been sorted, arranged, selected. A tribe living near the shore might wonder at this evidence of sorting or arrangement in the world, and might develop a myth to account for it, perhaps attributing it to a Great Spirit in the sky with a tidy mind and a sense of order. We might give a superior smile at such a superstitious notion, and explain that the arranging was really done by the blind forces of physics, in this case the action of waves. The waves have no purposes and no intentions, no tidy mind, no mind at all. They just energetically throw the pebbles around, and big pebbles and small pebbles respond differently to this treatment so they end up at different levels of the beach. A small amount of order has come out of disorder, and no mind planned it.

  The waves and the pebbles together constitute a simple example of a system that automatically generates non-randomness. The world is full of such systems. The simplest example I can think of is a hole. Only objects smaller than the hole can pass through it. This means that if you start with a random collection of objects above the hole, and some force shakes and jostles them about at random, after a while the objects above and below the hole will come to be nonrandomly sorted. The space below the hole will tend to contain objects smaller than the hole, and the space above will tend to contain objects larger than the hole. Mankind has, of course, long exploited this simple principle for generating non-randomness, in the useful device known as the sieve.

  The Solar System is a stable arrangement of planets, comets and debris orbiting the sun, and it is presumably one of many such orbiting systems in the universe. The nearer a satellite is to its sun, the faster it has to travel if it is to counter the sun’s gravity and remain in stable orbit. For any given orbit, there is only one speed at which a satellite can travel and remain in that orbit. If it were travelling at any other velocity, it would either move out into deep space, or crash into the Sun, or move into another orbit. And if we look at the planets of our solar system, lo and behold, every single one of them is travelling at exactly the right velocity to keep it in its stable orbit around the Sun. A blessed miracle of provident design? No, just another natural ‘sieve’. Obviously all the planets that we see orbiting the sun must be travelling at exactly the right speed to keep them in their orbits, or we wouldn’t see them there because they wouldn’t be there! But equally obviously this is not evidence for conscious design. It is just another kind of sieve.

  Sieving o
f this order of simplicity is not, on its own, enough to account for the massive amounts of nonrandom order that we see in living things. Nowhere near enough. Remember the analogy of the combination lock. The kind of non-randomness that can be generated by simple sieving is roughly equivalent to opening a combination lock with only one dial: it is easy to open it by sheer luck. The kind of non-randomness that we see in living systems, on the other hand, is equivalent to a gigantic combination lock with an almost uncountable number of dials. To generate a biological molecule like haemoglobin, the red pigment in blood, by simple sieving would be equivalent to taking all the amino-acid building blocks of haemoglobin, jumbling them up at random, and hoping that the haemoglobin molecule would reconstitute itself by sheer luck. The amount of luck that would be required for this feat is unthinkable, and has been used as a telling mind-boggler by Isaac Asimov and others.

  A haemoglobin molecule consists of four chains of amino acids twisted together. Let us think about just one of these four chains. It consists of 146 amino acids. There are 20 different kinds of amino acids commonly found in living things. The number of possible ways of arranging 20 kinds of thing in chains 146 links long is an inconceivably large number, which Asimov calls the ‘haemoglobin number’. It is easy to calculate, but impossible to visualize the answer. The first link in the 146-long chain could be any one of the 20 possible amino acids. The second link could also be any one of the 20, so the number of possible 2-link chains is 20 20, or 400. The number of possible 3-link chains is 20 × 20 × 20, or 8,000. The number of possible 146-link chains is 20 times itself 146 times. This is a staggeringly large number. A million is a 1 with 6 noughts after it. A billion (1,000 million) is a 1 with 9 noughts after it. The number we seek, the ‘haemoglobin number’, is (near enough) a 1 with 190 noughts after it! This is the chance against happening to hit upon haemoglobin by luck. And a haemoglobin molecule has only a minute fraction of the complexity of a living body. Simple sieving, on its own, is obviously nowhere near capable of generating the amount of order in a living thing. Sieving is an essential ingredient in the generation of living order, but it is very far from being the whole story. Something else is needed. To explain the point, I shall need to make a distinction between ‘single-step’ selection and ‘cumulative’ selection. The simple sieves we have been considering so far in this chapter are all examples of single-step selection. Living organization is the product of cumulative selection.

  The essential difference between single-step selection and cumulative selection is this. In single-step selection the entities selected or sorted, pebbles or whatever they are, are sorted once and for all. In cumulative selection, on the other hand, they ‘reproduce’; or in some other way the results of one sieving process are fed into a subsequent sieving, which is fed into …, and so on. The entities are subjected to selection or sorting over many ‘generations’ in succession. The end-product of one generation of selection is the starting point for the next generation of selection, and so on for many generations. It is natural to borrow such words as ‘reproduce’ and ‘generation’, which have associations with living things, because living things are the main examples we know of things that participate in cumulative selection. They may in practice be the only things that do. But for the moment I don’t want to beg that question by saying so outright.

  Sometimes clouds, through the random kneading and carving of the winds, come to look like familiar objects. There is a much published photograph, taken by the pilot of a small aeroplane, of what looks a bit like the face of Jesus, staring out of the sky. We have all seen clouds that reminded us of something — a sea horse, say, or a smiling face. These resemblances come about by single-step selection, that is to say by a single coincidence. They are, consequently, not very impressive. The resemblance of the signs of the zodiac to the animals after which they are named, Scorpio, Leo, and so on, is as unimpressive as the predictions of astrologers. We don’t feel overwhelmed by the resemblance, as we are by biological adaptations — the products of cumulative selection. We describe as weird, uncanny or spectacular, the resemblance of, say, a leaf insect to a leaf or a praying mantis to a cluster of pink flowers. The resemblance of a cloud to a weasel is only mildly diverting, barely worth calling to the attention of our companion. Moreover, we are quite likely to change our mind about exactly what the cloud most resembles.

  Hamlet.

  Do you see yonder cloud that’s almost in shape of a camel?

  Polonius.

  By the mass, and ’tis like a camel, indeed.

  Hamlet.

  Methinks it is like a weasel.

  Polonius.

  It is backed like a weasel.

  Hamlet.

  Or like a whale?

  Polonius.

  Very like a whale.

  I don’t know who it was first pointed out that, given enough time, a monkey bashing away at random on a typewriter could produce all the works of Shakespeare. The operative phrase is, of course, given enough time. Let us limit the task facing our monkey somewhat. Suppose that he has to produce, not the complete works of Shakespeare but just the short sentence ‘Methinks it is like a weasel’, and we shall make it relatively easy by giving him a typewriter with a restricted keyboard, one with just the 26 (capital) letters, and a space bar. How long will he take to write this one little sentence?

  The sentence has 28 characters in it, so let us assume that the monkey has a series of discrete ‘tries’, each consisting of 28 bashes at the keyboard. If he types the phrase correctly, that is the end of the experiment. If not, we allow him another ‘try’ of 28 characters. I don’t know any monkeys, but fortunately my 11-month-old daughter is an experienced randomizing device, and she proved only too eager to step into the role of monkey typist. Here is what she typed on the computer:

  UMMK JK CDZZ F ZD DSDSKSM

  S SS FMCV PU I DDRGLKDXRRDO

  RDTE QDWFDVIOY UDSKZWDCCVYT

  H CHVY NMGNBAYTDFCCVD D

  RCDFYYYRM N DFSKD LD K WDWK

  JJKAUIZMZI UXDKIDISFUMDKUDXI

  She has other important calls on her time, so I was obliged to program the computer to simulate a randomly typing baby or monkey:

  WDLDMNLT DTJBKWIRZREZLMQCO P

  Y YVMQKZPGJXWVHGLAWFVCHQYOPY

  MWR SWTNUXMLCDLEUBXTQHNZVJQF

  FU OVAODVYKDGXDEKYVMOGGS VT

  HZQZDSFZIHIVPHZPETPWVOVPMZGF

  GEWRGZRPBCTPGQMCKHFDBGW ZCCF

  And so on and on. It isn’t difficult to calculate how long we should reasonably expect to wait for the random computer (or baby or monkey) to type METHINKS IT IS LIKE A WEASEL. Think about the total number of possible phrases of the right length that the monkey or baby or random computer could type. It is the same kind of calculation as we did for haemoglobin, and it produces a similarly large result. There are 27 possible letters (counting ‘space’ as one letter) in the first position. The chance of the monkey happening to get the first letter — M — right is therefore 1 in 27. The chance of it getting the first two letters — ME — right is the chance of it getting the second letter — E — right (1 in 27) given that it has also got the first letter — M — right, therefore 1/27 × 1/27, which equals 1/729. The chance of it getting the first word — METHINKS — right is 1/27 for each of the 8 letters, therefore (1/27) × (1/27) × (1/27) × (1/27) …, etc. 8 times, or (1/27) to the power 8. The chance of it getting the entire phrase of 28 characters right is (1/27) to the power 28, i.e. (1/27) multiplied by itself 28 times. These are very small odds, about 1 in 10,000 million million million million million million. To put it mildly, the phrase we seek would be a long time coming, to say nothing of the complete works of Shakespeare.

  So much for single-step selection of random variation. What about cumulative selection; how much more effective should this be? Very very much more effective, perhaps more so than we at first realize, although it is almost obvious when we reflect further. We again use our computer monkey, but with a crucial difference in its program. It again b
egins by choosing a random sequence of 28 letters, just as before:

  WDLMNLT DTJBKWIRZREZLMQCO P

  It now ‘breeds from’ this random phrase. It duplicates it repeatedly, but with a certain chance of random error — ‘mutation’ — in the copying. The computer examines the mutant nonsense phrases, the ‘progeny’ of the original phrase, and chooses the one which, however slightly, most resembles the target phrase, METHINKS IT IS LIKE A WEASEL. In this instance the winning phrase of the next ‘generation’ happened to be:

  WDLTMNLT DTJBSWIRZREZLMQCO P

  Not an obvious improvement! But the procedure is repeated, again mutant ‘progeny’ are ‘bred from’ the phrase, and a new ‘winner’ is chosen. This goes on, generation after generation. After 10 generations, the phrase chosen for ‘breeding’ was:

  MDLDMNLS ITJISWHRZREZ MECS P

  After 20 generations it was:

  MELDINLS IT ISWPRKE Z WECSEL

  By now, the eye of faith fancies that it can see a resemblance to the target phrase. By 30 generations there can be no doubt:

  METHINGS IT ISWLIKE B WECSEL

  Generation 40 takes us to within one letter of the target:

  METHINKS IT IS LIKE I WEASEL

  And the target was finally reached in generation 43. A second run of the computer began with the phrase:

  Y YVMQKZPFJXWVHGLAWFVCHQXYOPY,

  passed through (again reporting only every tenth generation):

  Y YVMQKSPFTXWSHLIKEFV HQYSPY

  YETHINKSPITXISHLIKEFA WQYSEY

  METHINKS IT ISSLIKE A WEFSEY

  METHINKS IT ISBLIKE A