‘The course of evolution is uncertain, but its patterns are not,’ observes Roger Thomas, a palaeontologist from Franklin and Marshall College, Pennsylvania. ‘The most significant pattern in the history of life is the progressive net increase in complexity of structure and dynamics that has occurred in organisms and the ecosystems in which they participate.’ With this simple, straightforward statement, Thomas plunges headlong into one of the more contentious issues in biology: the shape of the history of life on Earth. Does evolution inevitably generate ever more complex organismsand ecosystems as time passes? And if so, what is the nature of that increase?
Even to the casual observer, the answers to those questions seem obvious. Life started with single-celled organisms, moved on to primitive multicellular organisms such as bizarre jawless fish and lumbering reptiles, and has culminated (so far) in hot-blooded, sleek and fleet-of-foot mammals, some of which are also endowed with large brains and even consciousness. Intuition tells us that mammals are somehow more complex than reptiles and that reptiles are more complex than invertebrates. Evolution seems to move in the direction of ever increasing complexity. But is it possible to devise experiments to test this assumption?
So far, few researchers have tried. The difficulty is not in recognising complexity, but in measuring it. Comparing a cat with a clam, many people will feel that there is ‘something more’ going on in the cat, says Dan McShea, a palaeontologist at the University of Michigan, Ann Arbor. ‘But is that ‘something more’ greater complexity or is it greater intelligence, greater mobility, or greater similarity to us? Hard to say.’ George Gaylord Simpson, one of the founders of evolutionary biology, expressed the same sentiment forty years ago: ‘It would be a brave anatomist who would attempt to prove that Recent man is more complicated than a Devonian ostracoderm.’
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But bravery or no, the quest to pin down the nature of biological complexity is now gathering momentum. On one front, palaeontologists like McShea are trying to measure real-life complexity by focusing on evolutionary trends, in particular anatomical characteristics of vertebrates or invertebrates. On another, researchers interested in computer models of evolution are striving to establish the capacity to process information as the most meaningful index of complexity in nature. Both approaches are proving to be highly controversial: the palaeontology, because it appears to show no evidence of an increase in complexity through time; and the computer-based research, because it seems to revive outmoded notions of evolution as a progressive process, a march toward ever better life forms, culminating in Homo sapiens with its large brain.
These days, most biologists reject such anthropocentric sentiments. ‘It’s acceptable to talk about complexity, but not progress,’ observes McShea. Michael Ruse, a philosopher of science at the University of Guelph in Ontario, Canada, makes the point more graphically: ‘Saying the word ‘progress’ in the company of serious evolutionary biologists is like saying ‘fuck’ at a vicar’s tea party – it doesn’t help the climb up the ecclesiastical pole.’
Uncertain territory
Complexity is a slippery term that means different things to different people. So it is perhaps not surprising that biologists have been reluctant to stray experimentally into such uncertain territory. At an abstract level, mathematicians have tried to quantify degrees of complexity embodied in a system, often with frustratingly paradoxical results . A few biologists have tried – so far with only modest success – to pin down criteria for measuring complexity in living organisms, including counting the number of different anatomical parts. Is a cat more complex than a clam? It would be judged so by this criterion, but is it true in an absolute sense? Vervet monkeys seem more complex biologically than the trees they live in, but this view may be overlooking a subtle complexity of ‘treeness’.
One of the most respected attempts to measure complexity was made ten years ago by John Tyler Bonner, a biologist at Princeton University in New Jersey. Count the number of different types of cell in the organism, he suggested. In principle, this gives a sense of the number of specialised functions an organism can perform, and that is a clue to complexity. Although it leaves out behaviour, the approach also has the virtue of considering the whole organism, not just one part.
Predictably perhaps, larger species turn out to have more types of cell and hence, by this measure, are more complex than smaller ones. Bonner did not look to see if this type of complexity increases as species evolve, but that would be a reasonable assumption. The idea of basing measurements of complexity on anatomical or cellular ‘parts’ may be extended by looking at the irregularity of those parts. For instance, the spine of a fish is built from a chain of vertebrae, each of which is very similar to the next. In mammals, by contrast, the spine is more complex, with considerable anatomical themes being played out along the cervical, through the thoracic, and on to the lumbar vertebrae. McShea wondered if this approach might provide insight into the evolution of complexity.
A bone of contention
Based on the availability of fossil specimens, he examined five groups of species – squirrels, ruminants, camels, whales and pangolins (scaly anteaters) – to see whether the structure of the spinal column had become more complex over the past 30 million years. McShea used a bank of six measures (see diagram) – such as thickness and length of spines – in his search for evidence of change in complexity through time. In some cases he did see indications of increased complexity, but he found decreases too, and stasis. ‘The overall impression is of no trend toward increased complexity,’ he concluded. ‘You see increased complexity no more frequently than decrease.’
George Boyajian of the University of Pennsylvania and Tim Lutz of West Chester University, Pennsylvania, came to a similar conclusion studying ammonoids, nautilus-like, shelled creatures which existed for 330 million years before becoming extinct along with the dinosaurs, 65 million years ago. The spiral-shaped shells of these creatures are constructed from multiple chambers, separated by walls, or septa. The structure of the septa are sometimes simple, sometimes complex.
Although the most complex structures are to be found among the later species, and the simplest among the earliest, there was no steady progression toward increased complexity within any particular lineage. The pattern of change in the structure through time was more or less random. ‘We don’t see any direction to the change of complexity,’ says Boyajian.
Boyajian and Lutz also asked whether greater complexity conferred any measurable survival advantage. Producing evermore complexity is costly, so it seems reasonable to suppose that, where it occurs, it is beneficial. But again, this ‘common-sense’ correlation failed to materialise. The average longevity for ammonoid genera was 15 million years, with the anatomically more complex species faring no better or worse than the simple ones. The obvious question, then, is: why bother to become more complex at all?
To Harvard biologist Stephen Jay Gould, these results are entirely consistent. Evolution is driven by natural selection, which is a local phenomenon, not a global trend. Natural selection adapts organisms to prevailing conditions that are just as likely to demand a decrease in complexity as an increase. To Thomas, however, McShea’s results have another explanation. When a new group of organisms evolves, there is an initial burst of biological innovation as the number of species within the group rises steeply, followed by a plateau, where little further change occurs. ‘All the taxa included in his study lived long after the early radiation in which the main increase in mammalian complexity presumably occurred,’ says Thomas.
McShea himself is cautious about the global significance of the findings. Everyone ‘knows’, he says, that the world of nature is more complex now than it was 550 million years ago, when the first multicellular creatures evolved, and certainly more so than when only single-celled organisms existed. What his results on vertebrae complexity show, he says, is that this global increase in complexity is not expressed consistently in all lineages of animals.
So however we seek to define complexity, we are still left with the puzzle of explaining this net global increase. The British evolutionary biologist John Maynard Smith has a simple answer: ‘When you start simple, there’s no way to go but up.’ In other words, even with random change, there is an inevitable drift toward more complexity, and each new level of complexity provides new heights on which chance changes can operate. The overall effect is like an evolutionary ratchet: the direction of change is constrained in one direction – towards the simple – and open in another – towards the complex.
But drift is not the only thing that could act to produce complexity. A second, crucial force might come from competition between predators and prey, driving species to evolve ever more sophisticated weapons, behaviours and defence structures. As one example of such an ‘arms race’, Geraat Vermeij of the University of California at Davis has documented a trend of thicker and stronger crab claws matched by increasingly effective defences (such as thicker shells, the development of spines and so on) in the snails on which the crabs feed.
According to this line of reasoning, complexity is best seen as a by-product of drift and predator-prey competition – not as a fundamental evolutionary force. Or as Thomas puts it, increasing complexity is ‘a long-term effect, not a law of evolution’. But not everyone agrees with this ‘passive’ explanation.
‘Biological complexity has to do with the ability to process information,’ argues Norman Packard, a physicist with the Prediction Company, a firm in Santa Fe, New Mexico, which specialises in commercial applications of chaos and complexity theories. Packard, a pioneer in the development of these theories, has not yet developed a specific measure of what he calls ‘computational capability’. Nevertheless, he says, this capacity is seen in complex dynamical systems, such as certain computer models, and in living systems. ‘Increased computational ability is what drives the evolution of computer algorithms and living organisms.’
The computer algorithms, or programs, Packard refers to are allowed to compete with each other in some kind of ‘game’, such as prisoners’ dilemma, in which two individuals must pit their wits to minimise their chances of being incarcerated. The programs may ‘mutate’ and accumulate changes through time. In all such programs, there emerges an ever greater complexity. ‘You start with the simplest possible strategy and you finish up with complex individual strategies and a complex interactive system,’ explains Packard. ‘It’s simply the dynamics of the system that produces it, given the goal of playing the game.’ Sometimes some of the algorithms become simpler, not more complex, during the model’s life, just as organisms do in biological evolution. Nevertheless, ‘the system as a whole undoubtedly becomes more complex’.
Packard’s argument is based on computer programs, but it resonates with a view expressed in a classic 1977 textbook on evolution, by Theodosius Dobzhansky, Francisco Ayala, George Ledyard Stebbins and James Valentine. They stated that the ‘ability to gather and process information’ increased through evolutionary history and, indeed, is a mark of progress. Ayala, speaking at a conference on evolution and progress some years ago in Chicago, said: ‘The ability to obtain and process information about the environment, and to react accordingly, is an important adaptation because it allows that organism to seek out suitable environments and resources and to avoid unsuitable ones.’
Survival has to do with gathering information about the environment and responding appropriately. There is no doubt that brains have become ever more sophisticated in their ability to process this information. All complex dynamical systems in biology, from bacteria to people, have a degree of computational ability. ‘You don’t have to have a brain to process information in the way I’m talking about it,’ says Packard. ‘But a brain puts you higher on the scale of computational ability.’
But it must be remembered that the phrase ‘higher on the scale’ is instantly provocative to biologists: following Darwin, they are taught that ‘higher’ and ‘lower’ are value-laden terms, not meaningful biological labels. Biologists are also taught that higher and lower implies a progressive element in evolution, ascending the scale of nature from the simple to the complex – and that’s anathema. As McShea says, biologists are willing to tackle the notion of complexity and accept that it has increased in the history of life in some ill-defined way, but to speak of ‘progress’ is considered unwise.
If evolution is held to be progressive, then it is all too easy to see it as being directed, following an arrow of improvement through time. And that is all too redolent of the notion of ‘divine’ design of pre-Darwinian days. As a physicist, Packard is not afraid to stray into this territory. ‘Intuitively, it seems reasonable that the task of survival requires computation,’ he explains. If this is true, then selection among organisms will lead to an inexorable increase in computational abilities, generating an arrow of change, not just a drift upwards.
Twice as brainy
And that’s exactly what you see in the biological record, asserts Packard. With the evolution of mammals from reptiles some 230 million years ago came a dramatic increase in average brain size. There was a similar increase when ‘modern’ mammals evolved 50 million years ago. And today’s primates are on average twice as ‘brainy’ as other mammals.
‘Progress is a noxious, culturally embedded, untestable, nonoperational idea,’ asserts Stephen Jay Gould. Gould concedes that some trends toward bigger brains can be discerned among the mammals, but argues that the overall pattern is nothing more than the inevitable drift from simplicity to complexity. ‘You cannot suggest that what happens in some groups of six thousand species of mammals represents the thrust of evolution,’ he says. ‘Certainly, brains have had more effect (on nature) than any other structure . . . (but) next in effect are the bacteria. Effect has to be divorced from complexity.’
Gould suggests that biologists are preoccupied with brain size because of a concern with human consciousness. ‘I view consciousness as a quirky accident,’ he says. ‘But if you believe there is an inexorable increase in brain size through evolutionary history, then human consciousness becomes predictable, not a quirky accident.’
Many biologists are uncomfortable talking about increase in brain size as a measure of increased complexity. ‘You’d like to think that being able to solve problems contributes to Darwinian fitness,’ says Maynard Smith. ‘But it’s hard to relate increased brain size to fitness. After all, bacteria are fit.’
The eminent Harvard ecologist EO Wilson, however, has no doubts, and is dismissive of the suggestion that a fascination with brain size is the result of a ‘brain-centric’ attitude: ‘Isn’t that the ultimate politically correct mode of reasoning?’ Packard is equally forthright about the reality of increase in brain size and the rejection of it for social rather than scientific reasons. ‘I don’t impute a value judgement to computational superiority,’ he says. The debate over brain size increase as an inevitable part of the evolutionary process echoes Darwin’s ambivalence over progress in evolution.
Darwin grew up in Victorian Britain, at the height of the nation’s industrial and political prowess. Progress was the proper reward for effort in society, and this ethic was transferred to science. Darwin was not immune to this intellectual environment, and yet he saw that natural selection was a local, not a global, process. As a result, his writings are scattered with contradictory statements about the reality or otherwise of progress. Proponents of both sides of the debate can use selective quotations from Darwin to support their position.
When McShea embarked upon his study of the literature and the experimental detection of progress, he expected – but failed – to find evidence in its favour. Now he is left pondering the following question: ‘Why do we want to find progress in evolution?’ He wonders whether it is a device ‘to justify our position on the top of the biological heap’. Gould takes a similar view. ‘There is a profound unwillingness to abandon a view of life as predictable progress,’ he says, ‘because to do so would be to admit that human existence is nothing but a historical accident. That is difficult for many to accept.’ In other words, the reality of evolutionary progress gives meaning to life.
Roger Lewin’s latest book, Complexity, is now out in paperback (Phoenix, 1993).
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Taking the measure of intricacy
Forty years ago, Claude Shannon, a mathematician with Bell Telephone Laboratories, New York, was among the first to suggest a way of measuring complexity. His approach was based on the very reasonable assumption that the amount of information processed by a given system reflects its complexity.
Unfortunately, Shannon’s method produced answers that, to biologists at least, were plain wrong. In comparing two strings of letters, bbbbbbbb with lofigwq, for instance, the first would be judged less complex than the second. The orderliness of bbbbbbbb seems to reflect a low information content, whereas the randomness of lofigwq embodies a large information content. As Seth Lloyd of the California Institute of Technology points out: ‘By this measure, a chimpanzee typing 650 pages of random alphanumeric characters would in short order produce a work not only as long as but more complex than James Joyce’s Finnegans Wake.’
The same problem befell the notion of algorithmic complexity, devised three decades ago by Raymond Solomonoff of the Zator Company in Cambridge, Massachusetts, Gregory Chaitin of City College in New York and Andrei Kolmogorov of the Mathematics Institute in Moscow. Applied to numbers, the measure says that the longer the algorithm, or computer program, that is necessary to generate those numbers, the more complex is the set of numbers.
If natural systems can also be transformed into a number set, a similar assessment can be made. The structure of a crystal lattice, for instance, might be represented by a series of ones and zeros, such as 101010101010101. Measured by algorithmic complexity, such a structure would be viewed as simple, because it can be described by the brief algorithm ‘print 10 n times’. Again, disorder, not order, equates with complexity with this measure. ‘Both Shannon’s information content and algorithmic information content fail to capture our intuitive understanding of the concept of complexity,’ says Lloyd. What is required is something that values intricacy over randomness.
Several scholars have worked toward this. For instance, Charles Bennett of the IBM Thomas J Watson Research Center in New York developed something he called logical depth. The approach assesses the difficulty of constructing a system, without being diverted by the unpredictability of randomness.
With Heinz Pagels, who was at the Rockefeller Institute in New York before he died in 1988, Lloyd extended this approach and developed a measure of complexity based on the amount of information processed during the evolution of a system. The measure involves assessing the amount of informational and thermodynamic effort involved in assembling the system. Although more logically satisfying to biologists, the Lloyd-Pagels’ measure is impossibly hard to apply to organisms more complex than the simplest of bacteria.