Micromotives and Macrobehavior

Micromotives and Macrobehavior
Thomas Schelling

How can we theorize the origins of segregation? How sensitive is segregation to the whims of actors? Thomas Schelling conceived a model of segregation that helps illuminate a number of tricky questions and produces some stunning insights. The model was first run on a checker board. The idea is that agents may desire at least some degree of similarity of people they move in next door to. If they do not find a desired level of similarity they move randomly to an open spot. The process continues.

Checker boards are slow though, so if you want to play with this idea with the NetLogo model and thousands of actors, please do so. You will find that even a desire for 30% similarity among the agents will globally produce 70% similarity. I played with the model and found a nonlinear relationship - at about 76% desired similarity you get about 99.9% global similarity. Over 76% desired (with 2000 agents) you get chaos, the system never crystallizes.

What the heck is the invisible hand of the economy? I think that's what this book seeks to explain. And it's a fantastic book. Really, I can't play it up enough. It's going to be assigned in classes I teach in the future, I spent half the time reading it thinking 'ooohhhh, right!'

Schelling is really looking for true principles of organization. For instance the Inescapable Mathematics of Musical chairs - someone is always left without a chair when a stable state is achieved. There are some inescapable facts of some social systems. For instance one person's raise is everyone else's inflation.

One thing this book really counters, like Sawyer did (but more concisely and effectively, I think), is methodological individualism. Individual experience only marginally explains global effects. I think case-studies and interviews are useful for understanding, but they tend to ignore how individual behavior interacts with the rest of the system and creates structure that can in turn affect behavior. Like how Schelling opens his book about a lecture he gave where everyone sat in the back of the auditorium and the first 15 rows were completely empty. Agent decision was important in this, but not as important as path-dependency. When the first few people sat down it set a norm that newcomers conformed to. Assuming humans are predictable behavior systems, initial seating conditions are more responsible for the seating arrangement than the individuals who sat down.

The book is rife with examples. Schelling often starts listing off cases where his principles are displayed. "I walk across the lawn if that seems to be what others are doing; I sometimes double-park if it looks as though everybody is double-parked. I stay in line if everybody is standing politely yin line, but if people begin to surge toward the ticket window I am alert to be - though never among the first - not among the last" (93).

Schelling comments on a number of phenomena common to the social science and complexity science. He mentions 'tipping points' for instance. This is a result of feedback. If we invite a certain number of people to a party there is an expectation among those invited that not everyone will attend. Since no one wants to be the few awkward people at a party people will hesitate. If there is knowledge that plenty of people are invited, so much so that the liklihood of being an awkward few is small, then there is a critical change and a higher proportion of people invited will actually attend. That's the idea at least, it's because of positive feedback, but the term isn't mentioned here.

The emphasis of the book is on modeling phenomena as a way of understanding it. Schelling is particularly interested in modeling sorting and aggregation of income, race, and cultural preference. His modeling assumes that the read has the mathematical prowess of an average economist (which, I think he is an economist, actually, which would explain the chapter on intersecting curves). Like Miller and Page's book, Schelling emphasizes the science over the technical bits of modeling. Good science always comes first and should provide insights and understanding to complex social situations. This book predates much of the vernacular of complexity science, but it belongs right smack in the middle of it. *recommended*