by David Orrell
Human beings excel at two types of prediction, and these correspond to the categories above. One is based on empathy—working out what someone else is feeling, putting ourselves in the shoes of another—and the second on cause and effect. The former works best for living beings, the latter for objects. But different people prefer different approaches. Psychologists Simon Baron-Cohen and Alan Leslie have proposed that autistic children find it hard to empathize. As babies, they make less eye contact with their mothers, and later in life, they have problems communicating or maintaining social relationships. In one study, the eye movements of adult autistics were tracked while they watched emotionally charged scenes from the 1966 film Who’s Afraid of Virginia Woolf ? 4 The patients tended to focus on peripheral objects rather than on the eyes of the actors. During a scene in which Richard Burton and Elizabeth Taylor kiss passionately, one viewer kept his attention on a light switch. “The world of objects is much more central to them than the world of people,” said Fred Volkmar, one of the study’s authors.5 Risk factors for autism include being male, and having mathematicians or physicists in the family.
Autism, or its weaker form, Asperger syndrome, need not be a barrier to success, or to genius. Hans Asperger, the Austrian doctor for whom the latter condition was named, believed that “a dash of autism” was essential for high achievement in many fields, in particular the “highly specialised academic professions, with a preference for abstract content.”6 It has been speculated that some of the greatest scientists of all time, including Albert Einstein, Isaac Newton, and even Socrates, showed classic signs of having Asperger syndrome.7 Newton, for example, was famously anti-social and incommunicative. If no one showed up for his lectures, he just gave them to the empty room.8
EXTREME SCIENCE
In June 2000, following a lecture at the Sorbonne by the economist Bernard Geurrien on the disconnect between conventional economics and reality, a group of fifteen French students unleashed a kind of primal scream against their educational establishments. In a petition signed by hundreds, they exclaimed: “We wish to escape from imaginary worlds! . . . We oppose the uncontrolled use of mathematics! . . . We are for a pluralism of approaches in economics! . . . Call to teachers: wake up before it is too late! . . . We no longer want to have this autistic science imposed on us.”9 Thus was born autisme-économie, or the post-autistic economics (PAE) movement. An interview in the French newspaper Le Monde garnered the students instant attention; the movement has since spawned many research papers, books, and a journal.
The choice of name was unfortunate—the students’ intention was not to criticize people with autistic disorders. However, the comparison between mainstream theory and an extreme mental condition makes sense, and not just in economics. A similar “extreme” pattern is evident in the archetypes and structures of Pythagorean thought. It is perhaps best summarized by the Pythagorean’s list of opposites in table 1.1 (see page 29), which like a set of aesthetic principles has shaped all three areas of prediction.
ONE VS PLURALITY—As the physicist B. K. Ridley wrote, “Mathematical physicists are motivated by the vision of Oneness. They are offended by the plurality of particles on the one hand and by the plurality of forces on the other.”10 Many genome scientists, in their quest for genetic causes of complex traits, appear to be driven by what the biologist Richard Lewontin describes as the “ideology of simple unitary causes.”11 The economist Frank Knight wrote, “This is the way our minds work; we must divide to conquer. Where a complex situation can be dealt with as a whole—if that ever happens—there is no occasion for ‘thought.’”12
RIGHT VS LEFT—Pythagorean science emphasized rational, logical thought (since Hippasus, “irrational” has been the worst term of abuse in science).13 This is a speciality of the left hemisphere of the brain, which controls the right side of the body. Both hemispheres of the brain work in unison, but loosely speaking, the left tends to use a predictive approach to problem solving, based on mental models and causality, while the right uses an integrative approach, which takes into account context and the big picture. In one experiment, split-brain patients were shown a sequence of red and green dots, which is random but biased so reds appear 75 percent of the time and greens only 25 percent. It was found that the right hemisphere tends to guess the next dot will be red (the simple climatological approach) while the left hemisphere will search for a complicated but nonexistent pattern, and do less well.14
MALE VS FEMALE—As Evelyn Fox Keller noted, modern science was developed “not by humankind but by men.”15 The first secretary of the Royal Society, Henry Oldenburg, described its aim as being “to raise a Masculine philosophy.”16 Similarly, Jay Griffiths wrote that mechanistic science has stripped away “chance, caprice and unpredictability—all things which, for good and for bad, have been associated with the female.”17 The economist Julie A. Nelson said that “analytical methods associated with detachment, mathematical reasoning, formality, and abstraction have cultural associations that are positive and masculine, in contrast with methods associated with connectedness, verbal reasoning, informality, and concrete detail, which are culturally considered feminine.”18 The oracles are still male.
AT REST VS IN MOTION—the Pythagoreans resisted any idea of movement in mathematics apart from the fixed rotation of stars and planets, which spin like tops in their crystalline shells. Copernicus wrote: “We conceive immobility to be nobler and more divine than mutability and instability.”19 The aim of predictive science is to find permanent, immutable laws. It is married to progress, but in the sense that it wants to fill in that single fixed image to greater and greater detail. As Galileo pointed out, however, the difference between mutability and immutability is “exactly the difference between a living animal and a dead one.”
STRAIGHT VS CROOKED—Mechanistic science has long been based on linear cause-and-effect relationships, for example, by drawing straight lines through clouds of data to deduce correlations. The emphasis on linearity began to change in the 1960s—weather models are certainly not linear—but even now, the crooked is ignored wherever possible (as Mandelbrot found when he tried to apply his fractal geometry to economics). The reason: straight lines are predictable, while crooked lines weave and change.
ODD VS EVEN—The Pythagoreans associated the even numbers with the dyad two, which signified mutability, excess, conflict, and indeterminacy. Mechanistic science is similarly intolerant of duality and mutability.
LIMITED VS UNLIMITED—The goal of mechanistic science is always to fix and constrain nature, to make it predictable. Even in chaos theory, much emphasis has been placed on finding attractors that limit the system to a small band of motion. Since Adam Smith and Malthus, the “dismal science” of economics has been structured around the ideas of scarcity and limits.
LIGHT VS DARKNESS—Mechanistic science tries, like Kepler did, “to draw the obscure facts of nature into the bright light of knowledge.” 20 It has illuminated much, but it still often ends up fumbling in the dark for the switch.
SQUARE VS OBLONG—Squares and circles have a deep geometric symmetry, which allows them to be described by a single parameter. Oblongs and ovals spoil this symmetry, as do clouds. Kepler wanted nothing more than to “square” his oval orbits, which he called a “cart-ful of dung.”21 Newton rehabilitated them by showing that it wasn’t the shape of the orbit that mattered but the form of the underlying law—and nothing is more symmetrical or invariant than the law of gravity.
Many truly innovative scientists, including Kepler, Newton, and Pythagoras, have mixed rationality with a kind of mysticism that defies categorization. However, it is fair to say that the basic thought processes of mechanistic science lean heavily towards the first column of opposites. There is nothing bad about the first column— it even includes the word “good” (in Good versus Evil). As a practising mathematician, I have spent a lot of time there myself. I enjoy clean lines and modernist architecture as much as the next person. But one can have too much of a
Good thing. There has emerged a worldview that, while in many respects tremendously successful—we could say it got us where we are today—is also one-sided and extreme. The Pythagorean quest for the harmony of the spheres did not end with Kepler, but has been channelled into mechanistic models that reduce the natural world to a collection of simple objects that can be understood and controlled.
In fields such as complexity, systems biology, or theoretical physics, scientists have in recent decades begun to move towards a rounder, more holistic, “post-Pythagorean” perspective. By necessity, they have had to grapple with duality, with mixing the left and the right.22 Engineers have always been ready to adopt top-down approaches. Predictive scientists, however, still cling to a purely mechanistic model of the world—not because they are stuck in the past or have been programmed by an ancient Apollonic cult, but because to do otherwise is to admit that the world is not predictable. They view the world as a collection of inert things—objects rather than subjects.
SUBJECT OR OBJECT
In 1967, the physicist Erwin Schrödinger observed that science is based on two beliefs: that nature is both objectifiable and knowable. The first means that we can isolate ourselves from nature and study it as an independent object from a position of lofty detachment. As the philosopher Mary Midgley noted, the emphasis on objectivity has had a profound effect on social scientists, who “have often pursued a very powerful and confused notion of ‘objectivity’ as requiring, not just the avoidance of personal bias, but a refusal to talk or think about subjective factors at all. The word ‘subjective’ then becomes a simple term of abuse directed at any mention of thoughts or feelings, and the word ‘objective’ a potent compliment for any approach which ignores them.”23 The result is that even human beings are treated as lifeless objects. Unlike the blind Gloucester in Shakespeare’s King Lear, a scientist should not see the world feelingly.
Weather and climate modellers too have always been afraid of what the meteorologist Carl-Gustaf Rossby called the “horrible subjectivity.” The word “forecast” was invented by Robert FitzRoy, precisely to avoid the subjective connotations of “prediction” and any unwelcome comparisons with Zadkiel’s Almanac. But changing the name did not make the issue of subjectivity go away.24
The Greek Circle Model, with its swirling epicycles, maintained its hold on the human imagination for 2,000 years not just because of its prominent use of circles, which according to Ptolemy were alone in being “strangers to disparities and disorders,” but also because it worked as a predictive device (it could accurately forecast eclipses and the movements of the planets). A thirteen-year-old Tycho Brahe became fascinated by astronomy when he witnessed a partial eclipse of the sun that had been accurately foretold. It seemed “something divine that men could know the motions of the stars so accurately that they were able a long time beforehand to predict their places and relative positions.”25 Christopher Columbus wowed the natives of Jamaica into submission by accurately predicting a lunar eclipse on February 29, 1502, then promising to return their moon if they obeyed him.26 Einstein’s theory of relativity was accepted not because a committee agreed that it was a very sensible model, but because its predictions, most of which were highly counterintuitive, could be experimentally verified. Modern GCMs have no such objective claim to validity, because they cannot predict the weather over any relevant time scale. Many of their parameters are invented and adjusted to approximate past climate patterns.27 Even if this is done using mathematical procedures, the process is no less subjective because the goals and assumptions are those of the model builders. Their projections into the future —especially when combined with the output of economic models—are therefore a kind of fiction. The fact that climate change is an important and contentious issue makes it all the more important that we acknowledge this. The problem with the models is not that they are subjective or objective—there is nothing wrong with a good story, or an informed and honestly argued opinion. It is that they are couched in the language of mathematics and probabilities: subjectivity masquerading as objectivity. Like the Wizard of Oz, they are a bit of a sham.
Schrödinger’s second tenet—that nature is knowable—implies there is a kind of inbuilt correspondence between our minds and nature. Just as Pythagoras and Kepler believed the motion of the planets revealed a cosmic harmony that could be understood by mathematics, modern scientists see the climate system, and even life itself, as a challenging but tractable mathematical problem. It is indeed remarkable how fundamental forces of nature, such as gravity, seem to conform to mathematical equations; but the same is not necessarily true of complex emergent properties, such as life. As Evelyn Fox Keller noted, “Belief in the knowability of nature is implicitly a belief in a one-to-one correspondence between theory and reality.”28 This affects the kind of problems that scientists pose. “Questions asked about objects with which one feels kinship are likely to differ from questions asked about objects one sees as unalterably alien. Similarly, explanations that satisfy us about a natural world that is seen as ‘blind, simple and dumb,’ ontologically inferior, may seem less self-evidently satisfying for a natural world seen as complex and, itself, resourceful.”29
So what difference would it make to long-term predictions for the planet if the planet itself were treated as a living entity?
IT’S ALIVE
The idea that the earth behaves like a self-regulating organism is the essence of James Lovelock’s Gaia theory. In the early 1960s, before Lovelock used his electron-capture gas chromatograph to detect the buildup of ozone-eating CFCs in the atmosphere, he was invited by NASA to invent a different kind of instrument—one capable of being sent on a spacecraft to answer that famous question, posed by David Bowie, “Is there life on Mars?” The first problem was what to test for, since any life forms on Mars may be radically different from those on earth. The most general characteristic of life, it seemed, was that it takes in energy and matter and discards waste products. Such processes should leave a chemical signature on the Martian atmosphere.
To test his idea, Lovelock and Dian Hitchcock began to analyze the chemical makeup of Mars and compare it with that of the earth. The results showed a strong contrast. The atmosphere of Mars, like that of Venus, was about 95 percent carbon dioxide, with some oxygen and no methane. The earth was 77 percent nitrogen, 21 percent oxygen, and a relatively large amount of methane. Mars was chemically dead; all the reactions that were going to take place had already done so. The earth, however, was far from chemical equilibrium. Methane and oxygen, which react with each other very easily, are both present in the atmosphere. Lovelock concluded that for this to be the case, the gases had to be in constant dynamic circulation, and the pump driving this circulation was life.30
About 3 billion years ago, bacteria and photosynthetic algae started to remove carbon dioxide from the young earth’s atmosphere, producing oxygen as a waste product. Over enormous time periods, this process changed the chemical content of the atmosphere— to the point where organisms began to suffer from oxygen poisoning. The situation was relieved only with the advent of other life forms powered by aerobic consumption. It was life processes, the cumulative actions of countless organisms, that made the atmosphere we now enjoy. The blanket of greenhouse gases that controls our temperature, the ozone layer that acts as sunscreen, the entire physical condition of the planet—all have been actively shaped by life itself. The net effect of these processes was that the earth itself appeared as a living entity—a kind of super-organism. As Lovelock wrote, “An awesome thought came to me. . . . Could it be that life on Earth not only made the atmosphere, but also regulated it—keeping it at a constant composition, and at a level favourable for organisms?”31 On a stroll with his novelist neighbour, William Golding, Lovelock described his idea and asked advice for a name. Golding suggested naming it after Gaia, the Greek earth goddess (who was displaced by Apollo from the seat of prophecy along with Sybil and Python).
The Gaia hypothesis hinged on the
observation that the planet is self-regulating, or homeostatic. In an extension of Darwinism, life forms are regulated by the environment, and in turn the environment is actively regulated for life. As we saw in Chapter 5, homeostasis is not the same as static behaviour—all life forms are in a constant state of flux—but refers instead to the ability to retain a degree of internal order in a changing environment. The heat of the sun has increased by 25 percent since life began on earth, yet the temperature has remained more or less constant (on this scale, even ice ages count as relatively minor fluctuations). The human body regulates its temperature by shivering if too cold or perspiring if too hot, and other mechanisms. Lovelock, together with the American microbiologist Lynn Margulis, uncovered a number of feedback loops that could act as regulatory influences on a planetary scale. These are similar to those discussed in Chapter 7, but they operate over longer geological time scales.
An example is the long-term regulation of carbon dioxide. When volcanoes such as Mount St. Helens erupt, they throw massive quantities of carbon dioxide into the atmosphere. If the carbon were allowed to build up over millennia, the greenhouse effect would make the earth too warm to support life. One process by which carbon dioxide is removed from the atmosphere is rock weathering, where rainwater and carbon dioxide combine with rocks to form carbonates. Lovelock, Margulis, and others discovered that this process is hugely accelerated by the presence of soil bacteria. The carbonates are washed into the ocean, where algae use them to make microscopic shells. When the algae die, their shells sink to the ocean floor, forming limestone sediments. Since the soil bacteria are more active in high temperatures, the removal of carbon dioxide is accelerated when the planet is hot. This has the effect of cooling the planet. Therefore, the whole massive cycle forms a stabilizing negative feedback loop. The idea of self-regulation was illustrated with the conceptual model Daisyworld (see Appendix III).
