4. The Paradigm Shift Argument.
A surprising number of otherwise hard-nosed scientists, when confronted with the argument that science might be ending, start sounding like philosophical relativists, or social constructivists, or other doubters of scientific truth. They begin to sound, in other words, like the people who write for the postmodern journal Social Text, which last June was the victim of a hoax that was perpetrated by the New York University physicist Alan Sokal and subsequently made the front page of The New York Times.
According to these skeptics, science is a process not of discovery but of invention, like art or music or literature. We just think science can't go any further because we can't see beyond our current paradigms. In the future, we will submit to new paradigms that cause the scales to fall from our eyes and open up vast new realms of inquiry. This kind of thinking can be traced back to the philosopher Thomas Kuhn, who wrote the extremely influential book Structure of Scientific Revolutions, and who died last June.
But modern science has been much less revolutionary÷much less susceptible to dramatic shifts in perspective÷than Kuhn suggested. Particle physics rests on the firm foundation of quantum mechanics, and modern genetics, far from undermining the fundamental paradigm of Darwinian evolution, has bolstered it.
If you view atoms and elements and the double helix and viruses and stars and galaxies as inventions, projections of our culture, which future cultures may replace with other convenient illusions, then you are unlikely to agree with me that science is finite. If science is as ephemeral as art, of course it can continue forever. But if you think that science is a process of discovery rather than merely of invention, if you believe that science is capable of achieving genuine truth, then you must take seriously the possibility that all the great, genuine paradigm shifts are behind us.
5. End of Science Is Just Semantic Trickery
My book had at least one serious shortcoming. In arguing that science will never achieve anything as fundamental as quantum mechanics or the theory of evolution, I should have said what I meant by "fundamental." I'll take a stab at that now. A fact or theory is fundamental in proportion to how broadly it applies both in space and in time. Both quantum mechanics and the theory of general relativity apply, as far as we know, throughout the entire universe at all times since its birth. That makes these theories truly fundamental.
Technically, all biological theories are less fundamental than the cornerstone theories of physics, because biological theories apply÷as far as we know÷only to particular arrangements of matter that have existed on our lonely little planet for the past 3.5 billion years. But biology has the potential to be more meaningful than physics because it more directly addresses a phenomenon we find especially fascinating: ourselves.
In his 1995 book Darwin's Dangerous Idea, Daniel Dennett argued persuasively that evolution by natural selection is "the single best idea anyone has ever had," because it "unifies the realm of life, meaning and purpose with the realm of space and time, cause and effect, mechanism and physical law."
I agree. Darwin's achievement÷especially when fused with Mendelian genetics into the new synthesis÷has rendered all subsequent biology oddly anticlimactic, at least from a philosophical perspective. Take developmental biology, which addresses the transformation of a single fertilized cell into a multicellular creature. In her review of my book for The New York Times, Natalie Angier expressed the hope that "unifying insights that illuminate pattern formation in the developing embryo" would disprove my end-of-science thesis. But according to the eminent British biologist Lewis Wolpert, those "unifying insights" may already be behind us. Wolpert was recently quoted in Science as saying that "the principles of development are understood and all that remains is to fill in the details." But this scientific triumph has unfolded virtually unnoticed by the public. One reason is that developmental biology is excruciatingly complicated, and most science writers avoid it. Natalie Angier is one of the few writers talented enough to make the subject fun to read about.
But another reason that developmental biology does not attract more attention may be that its findings fit so comfortably within the broader paradigm of evolutionary theory and DNA-based genetics. It is "normal science," to use the phrase favored by Kuhn. Normal science solves puzzles that are posed by the prevailing paradigm but does not challenge the paradigm's basic tenets.
In a way, all biology since Darwin has been normal science. Even Watson and Crick's discovery of the double helix, although it has had enormous practical consequences, merely revealed how heredity works on a molecular level; no significant revision of the new synthesis was required.
6. The Chaoplexity Gambit
Many modern scientists hope that advances in computers and mathematics will enable them to transcend their current knowledge and create a powerful new science. This is the faith that sustains the trendy fields of chaos and complexity. In my book I lump chaos and complexity together under a single term, chaoplexity, because after reading dozens of books about chaos and complexity and talking to scores of people in both fields, I realized that there is no significant difference between them.
Chaoplexologists have argued that with more powerful computers and mathematics they can answer age-old questions about the inevitability, or lack thereof, of life, or even of the entire universe. They can find new laws of nature analogous to gravity or the second law of thermodynamics. They can make economics and other social sciences as rigorous as physics. They can find a cure for AIDS. These are all claims that have been made by researchers at the Santa Fe Institute.
These claims stem from an overly optimistic interpretation of certain developments in computer science. Over the past few decades, researchers have found that various simple rules, when followed by a computer, can generate patterns that appear to vary randomly as a function of time or scale. Let's call this illusory randomness "pseudo-noise." A paradigmatic example of a pseudo-noisy system is the mother of all fractals, the Mandelbrot set, which is an icon of the chaoplexity movement.
The fields of both chaos and complexity have held out the hope that much of the noise that seems to pervade nature is actually pseudo-noise, the result of some underlying, deterministic algorithm. But the noise that makes it so difficult to predict earthquakes, the stock market, the weather and other phenomena is not apparent but very real. This kind of noisiness will never be reduced to any simple set of rules, in my view.
Of course, faster computers and advanced mathematical techniques will improve our ability to predict certain complicated phenomena. Popular impressions notwithstanding, weather forecasting has become more accurate over the last few decades, in part because of improvements in computer modeling. But an even more important factor is improvements in data-gathering÷notably satellite imaging. Meteorologists have a larger, more accurate database upon which to build their models and against which to test them. Forecasts improve through this dialectic between simulation and data-gathering.
At some point, we are drifting over the line from science per se toward engineering. The model either works or doesn't work according to some standard of effectiveness; "truth" is irrelevant. Moreover, chaos theory tells us that there is a fundamental limit to forecasting related to the butterfly effect. One has to know the initial conditions of a system with infinite precision to be able to predict its course. This is something that has always puzzled me about chaoplexologists: according to one of their fundamental tenets, the butterfly effect, many of their goals may be impossible to achieve.