Growing Artificial Societies: Social Science from the Bottom Up

By Joshua M. Epstein and Robert L. Axtell

Book Overview

A Brookings Institution Press and MIT Press publication

How do social structures and group behaviors arise from the interaction of individuals? In this groundbreaking study, Joshua M. Epstein and Robert L. Axtell approach this age-old question with cutting-edge computer simulation techniques. Such fundamental collective behaviors as group formation, cultural transmission, combat, and trade are seen to emerge from the interaction of individual agents following simple local rules.

In their computer model, Epstein and Axtell begin the development of a bottom up social science. Their program, named Sugarscape, simulates the behavior of artificial people (agents) located on a landscape of a generalized resource (sugar). Agents are born onto the Sugarscape with a vision, a metabolism, a speed, and other genetic attributes. Their movement is governed by a simple local rule: look around as far as you can; find the spot with the most sugar; go there and eat the sugar. Every time an agent moves, it burns sugar at an amount equal to its metabolic rate. Agents die if and when they burn up all their sugar. A remarkable range of social phenomena emerge. For example, when seasons are introduced, migration and hibernation can be observed. Agents are accumulating sugar at all times, so there is always a distribution of wealth.

Next, Epstein and Axtell attempt to grow a proto-history of civilization. It starts with agents scattered about a twin-peaked landscape; over time, there is self-organization into spatially segregated and culturally distinct tribes centered on the peaks of the Sugarscape. Population growth forces each tribe to disperse into the sugar lowlands between the mountains. There, the two tribes interact, engaging in combat and competing for cultural dominance, to produce complex social histories with violent expansionist phases, peaceful periods, and so on. The proto-history combines a number of ingredients, each of which generates insights of its own. One of these ingredients is sexual reproduction. In some runs, the population becomes thin, birth rates fall, and the population can crash. Alternatively, the agents may over-populate their environment, driving it into ecological collapse.

When Epstein and Axtell introduce a second resource (spice) to the Sugarscape and allow the agents to trade, an economic market emerges. The introduction of pollution resulting from resource-mining permits the study of economic markets in the presence of environmental factors.

This study is part of the 2050 Project, a joint venture of the Santa Fe Institute, the World Resources Institute, and the Brookings Institution. The project is an international effort to identify conditions for a sustainable global system in the middle of the next century and to design policy actions to help achieve such a system.

• Format: Paperback
• Language: English
• ISBN: 0262550253
• ISBN13: 9780262550253
• Release Date: October 1996
• Publisher: Brookings Institution Press
• Length: 226 Pages
• Weight: 0.97 lbs.
• Dimensions: 0.5" x 6.1" x 9.0"
• Age Range: 18 years and up
• Grade Range: Postsecondary and higher

Customer Reviews

Pioneers of agent modeling

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 is not a "how-to" book

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 Future of Modeling Social Systems

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.

Good intro to agent sims.

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.