Growing Artificial Societies: Social Science from the Bottom Up

This short book is written by the pioneers of the agent-based modeling technique. It describes their pioneering model, starting with the most basic iteration. Each chapter describes the process of adding a layer of complexity to the model, and the results therein. It contains no "errors" or "artifacts." It is a "how to book," for those with at least a passing interest in social science and the ability to download shareware from the web (i.e. NetLogo). Any person with an interest in this field must be able to discuss this book, as well as the author's Anasazi model, among others.

This book is not a "how-to" book. They do not provide all of the code for thier sugarscape model. Yes, they provide some snap-shots of code for the reader, but those are instructive as to how to organize one's own code for your own ideas and models. If you want the entire code go to Swarm or RePast web pages and look for it in objective C or Java. I was introduced to this book in a graduate archaeology course. Now, 3 years later I've returned to it for my dissertation. What this book does it explain how simple rules and ideas can create rather complex outcomes. What are the affects of having agents vision be only 5 cells compared to infinite sight? Can simple biological questions such as resolution of vision have a profound affect on our social structure? There are a bunch of, respectively, simple questions that this book address or introduce to explain the power of this method for the social sciences. If one is looking for a "How To Book" you should go to Ascape, RePast, Swarm, or any of the other agent based modeling software research groups. What this book does is provide the reader with the conceptual issues and the foundation for what this method can do, that's it.

The authors do an impressive job of demonstrating how agent based simulations can be applied to social systems. In the past, modeling of this sort was limited to traditional analysis techniques such as applied differential equations. While some are critical of this work because they point out the number of assumptions inherent in this model, they also neglect to consider the greater degree of assumptions and over-simplifications implicit in pure mathematical models (eg, linearity, continuous functions, etc.) An advantage of agent based modeling is that one can consider all sorts of rules which do not lend themselves to purely mathematical models. Consider queuing theory as an example. While there exist basic mathematical models for queue analysis, once a certain threshold of complexity is reached, these models fail, and one must look to computer simulation as the alternative. While their results are speculative, the authors have successfully demonstrated emergence of complex behavior from simple rules. One such example is an unexpected diagonal migration path emerging from an orthogonal movement rule. In the future, this type of social modeling will be the accepted norm and practitioners will look back at this work as a foundational reference.

Granted, this is not a cookbook for creating the simulations described. However, it gives a good picture of the power of agent simulations, and shows the basics of behavior modeling. In this respect, it is an excellent text. I would suggest it for an advanced undergrad course, rather than graduate level.