Is It Like Cooking?

Is It Like Cooking?

Cooking seems to be the perfect business that depends on optionality. You add an ingredient and have the option of keeping the result if it is in agreement with Fat Tony’s taste buds, or fuhgetaboudit if it’s not. We also have wiki-style collaborative experimentation leading to a certain body of recipes. These recipes are derived entirely without conjectures about the chemistry of taste buds, with no role for any “epistemic base” to generate theories out of theories. Nobody is fooled so far by the process. As Dan Ariely once observed, we cannot reverse engineer the taste of food from looking at the nutritional label. And we can observe ancestral heuristics at work: generations of collective tinkering resulting in the evolution of recipes. These food recipes are embedded in cultures. Cooking schools are entirely apprenticeship based.

On the other side, we have pure physics, with theories used to generate theories with some empirical validation. There the “epistemic base” can play a role. The discovery of the Higgs Boson is a modern case of a particle entirely expected from theoretical derivations. So was Einstein’s relativity. (Prior to the Higgs Boson, one spectacular case of a discovery with a small number of existing external data is that of the French astronomer Le Verrier’s derivation of the existence of the planet Neptune. He did that on the basis of solitary computation, from the behavior of the surrounding planets. When the planet was actually sighted he refused to look at it, so comfortable was he with his result. These are exceptions, and tend to take place in physics and other places I call “linear,” where errors are from Mediocristan, not from Extremistan.)

Now use this idea of cooking as a platform to grasp other pursuits: do other activities resemble it? If we put technologies through scrutiny, we would see that most do in fact resemble cooking a lot more than physics, particularly those in the complex domain.

Even medicine today remains an apprenticeship model with some theoretical science in the background, but made to look entirely like science. And if it leaves the apprenticeship model, it would be for the “evidence-based” method that relies less on biological theories and more on the cataloging of empirical regularities, the phenomenology I explained in Chapter 7. Why is it that science comes and goes and technologies remain stable?

Now, one can see a possible role for basic science, but not in the way it is intended to be.3 For an example of a chain of unintended uses, let us start with Phase One, the computer. The mathematical discipline of combinatorics, here basic science, derived from propositional knowledge, led to the building of computers, or so the story goes. (And, of course, to remind the reader of cherry-picking, we need to take into account the body of theoretical knowledge that went nowhere.) But at first, nobody had an idea what to do with these enormous boxes full of circuits as they were cumbersome, expensive, and their applications were not too widespread, outside of database management, only good to process quantities of data. It is as if one needed to invent an application for the thrill of technology. Baby boomers will remember those mysterious punch cards. Then someone introduced the console to input with the aid of a screen monitor, using a keyboard. This led, of course, to word processing, and the computer took off because of its fitness to word processing, particularly with the microcomputer in the early 1980s. It was convenient, but not much more than that until some other unintended consequence came to be mixed into it. Now Phase Two, the Internet. It had been set up as a resilient military communication network device, developed by a research unit of the Department of Defense called DARPA and got a boost the days when Ronald Reagan was obsessed with the Soviets. It was meant to allow the United States to survive a generalized military attack. Great idea, but add the personal computer plus Internet and we get social networks, broken marriages, a rise in nerdiness, the ability for a post-Soviet person with social difficulties to find a matching spouse. All that thanks to initial U.S. tax dollars (or rather budget deficit) during Reagan’s anti-Soviet crusade.

So far we have been following the forward arrow. Science was of some use along the way, since computer technology relies on science in most of its aspects. But academic science did not set the direction; it served chance discoveries in an opaque environment, with almost no one but college dropouts and overgrown high school students along the way. The process remained self-directed and unpredictable at every step. And the great fallacy is to make it sound irrational—the irrational resides in not seeing a free option when it is handed to us.

China might be a quite convincing story, through the works of a genius observer, Joseph Needham, who debunked quite a bit of Western beliefs and figured out the powers of Chinese science. As China became a top-down mandarinate (that is, a state managed by Soviet-Harvard centralized scribes, as Egypt had been before), the players somehow lost the zest for bricolage, the hunger for trial and error. Needham’s biographer Simon Winchester cites the sinologist Mark Elvin’s description of the problem, as the Chinese did not have, or, rather, no longer had, what he called the “European mania for tinkering and improving.” They had all the means to develop a spinning machine, but “nobody tried”—another example of knowledge hampering optionality. They probably needed someone like Steve Jobs—blessed with an absence of college education and the right aggressiveness of temperament—to take the elements to their natural conclusion.