Swarm Intelligence

Swarm Intelligence
James Kennedy and Russell C Eberhart

This was a really cool book. I will have to get the last few chapters later on, since I focused on the first half of the book. The first half is about human society as an information processor. Swarm optimization is essentially a model of social learning that has optimization applications, but was originally conceived as a method of exploring theories of social cognition. The book starts by discussing social theory, brains, language, information, culture, etc, and eventually moves in to the more technical application of coding and implementing swarm optimization. The emphasis of this book is not on the individual but on the social. The interactions between actors is what creates social intelligence and culture and that is what they hope to model and (since one of them is an engineer) exploit.

This book reminded me of the reasons I first went into social science. I wanted to explore the evolution of culture as a thing. The book starts by discussing modeling intelligence and what intelligence really is. They integrate ideas such as emergence and evolutionary computation. Then they move into culture suggesting that 'truth evolves' specifically since it relies on fuzzy systems like the brain to interpret symbols and produce meaning. The next chapters give an introduction to evolution and optimization, we even get some brief commentary on the creation / evolution debate*. Eventually we get to a topic I tend to find very interesting: flocks, herds, and swarms, and treating social behavior as optimization.

The idea is that even random (sorry, stochastic) movement can induce learning. A snail moving about a space is essentially searching for food. I often feel social scientists (well, sociologists in particular) are hesitant to draw parallels between other biological phenomena of organization and social learning and human methods of social learning. Kennedy and Eberhart don't even hesitate, and contrary to other points in the book where they justify evolution or justify modeling, we get little justification for the use of sociobiology. Ants are such an amazing evolved system for problem solving and decision making. The chapter on herds and swarms illustrates how ants problem solve, how this system can be modeled, and what it can tell us about how humans process and act on information.

On a very local level humans appear chaotic and unpredictable, but at the right scale human groups tend to behave in a very fluid manner. I've always taken the view that organisms respond and adapt to their own environment, but what if the environment is made of other organisms like them? Maybe all that complexity in human behavior is not so much that humans individually are unpredictable, but that we can't control the multitude of influences on behavior. Herbert Simon suggests, "A man, viewed as a behaving system, is quite simple. The apparent complexity of his behavior over time is largely a reflection of the complexity of the environment in which he finds himself" (100). Especially if that environment is other humans reacting to other humans. The book then moves into models and science behind social influence (and we see Axelrod come up again, damn he has made SO many influential models of human behavior... so many).

At the end we get our math and our code and our engineering. The particle swarm optmization model uses most of the ideas from the preceding chapters. The particles are networked together to each other and seek consistency with those they are connected to. They also try to optimize their own fitness while still maintaining that consistency of those they are connected to. In this way, if you put a particle swarm in a space the particles tend towards optimal solutions, and since they are stochastic like gnats they also explore the space around optimal solutions which leads to even better solutions.

They end the book by saying, "It is almost unbelievable, even to us, that the computer program that started out as a social-psychology simulation is now used to optimize power grids in Asia; develop high-tech products in the United States; and to solve high-dimensional, nonlinear, noisy mathematical problems" (427). The suggest that study of this 'paradigm' is in its infancy and is far from exhausted. This book gives a fantastic and incredibly cool look at social science, modeling, evolution, information, culture, and computation. I definitely could not spend enough time digging into it, as I would desperately like to code my own particle swarm optimization and experiment with it. Eventually... I have to keep focused on what I have, I guess.

* - "The disagreement between religious advocates and evolutionary scientists comes down to this: the creationists know how life began on earth, and the evolutionists don't." (82)