Bigger than Chaos


Richard Marshall interviews Michael Strevens in 3:AM Magazine:

3:AM: Are social systems understandable from this point of view using the same probability tools as the natural sciences involving the laws of large numbers? So can we use the same approach with, say, statistical physics and population genetics and areas of economics?

MS: That is a great unsolved question. In the nineteenth century, scientists and government statisticians began to find fairly stable social trends: rates of marriage, suicide, undeliverable letters and other unfortunate events tended to stay much the same from year to year (though the rates differed from place to place). Further, these patterns could be captured quite well using the mathematics of probability, which was fast maturing at the time. There was great hope for a science of society that would replicate the success of the science of inert matter—a “social physics”.

That hope turned out to be premature. Pinning down social and economic trends in the detail we’d like has turned out to be incredibly difficult. Maybe that’s in part because we want more detail from our theories of people than from our theories of molecules. Maybe because social trends change too fast or depend in too complicated a way on environmental factors. Or maybe there are, in some cases at least, no statistical trends at all. Maybe we need another kind of mathematics, different from probability mathematics, to understand these systems. It’s a wonderful topic. I don’t know if I will ever contribute substantially to it myself—time is running out!—but I hope at the very least to make it a more central topic of philosophical discussion.

3:AM: How does this approach to complex systems relate to chaos? Is it kind of what chaos is?

MS: In a sequence of coin tosses, you have short term unpredictability—you never know whether the next toss is going to be heads or tails—and long term predictability—if you toss the coin for long enough, you can be pretty sure that you will get about one half of each side. It’s the same story for all the complex systems whose behavior can be represented using probabilities. You can’t predict, for example, which rabbit will be eaten by which fox the day after tomorrow, but you can predict the approximate rate of rabbit predation (and this number plays a crucial role in ecological and evolutionary models). Here’s an interesting thought: might short-term unpredictability and long-term stability be linked? Might the source of the unpredictability also be a source of the stability? In my book, I show that the answer is yes.

More here.