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Growing Artificial Societies: Social Science From the Bottom Up (Complex Adaptive Systems), by Joshua M. Epstein, Robert L. Axtell
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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.
- Sales Rank: #297127 in Books
- Published on: 1996-10-01
- Original language: English
- Number of items: 1
- Dimensions: 8.90" h x .50" w x 6.00" l, .90 pounds
- Binding: Paperback
- 228 pages
Amazon.com Review
Growing Artificial Societies is a groundbreaking book that posits a new mechanism for studying populations and their evolution. By combining the disciplines of cellular automata and "artificial life", Joshua M. Epstein and Robert Axtell have developed a mechanism for simulating all sorts of emergent behavior within a grid of cells managed by a computer. In their simulations, simple rules governing individuals' "genetics"" and their competition for foodstuffs result in highly complex societal behaviors. Epstein and Axtell explore the role of seasonal migrations, pollution, sexual reproduction, combat, and transmission of disease or even "culture" within their artificial world, using these results to draw fascinating parallels with real- world societies. In their simulation, for instance, allowing the members to "trade" increases overall well-being but also increases economic inequality. In Growing Artificial Societies, the authors provide a workable framework for studying social processes in microcosm, a thoroughly fascinating accomplishment.
Review
"Computer simulations are changing the frontiers of science. Growing Artificial Societies is an outstanding example of why; it shows how sociocultural phenomena like trade, wealth, and warfare arise naturally out of the simple actions of individuals. This illuminating, entertaining book will set the standard for the practice of social science in the 21st century."
(John L. Casti, Santa Fe Institute)"Epstein and Axtell present an exciting theoretical version of an integrated social science built on simple and explicit microfoundations."
(Sidney G. Winter, Wharton School of Business)Growing Artificial Societies is a milestone in social science research. It vividly demonstrates the potential of agent-based computer simulation to break disciplinary boundaries. It does this by analyzing, in a unified framework, the dynamic interactions of such diverse activities as trade, combat, mating, culture, and disease. It is an impressive achievement.
(Robert Axelrod, University of Michigan) From the Back Cover
Growing Artificial Societies is a milestone in social science research. It vividly demonstrates the potential of agent-based computer simulation to break disciplinary boundaries. It does this by analyzing in a unified framework the dynamic interactions of such diverse activities as trade, combat, mating, culture, and disease. It is an impressive achievement.'
Most helpful customer reviews
7 of 9 people found the following review helpful.
Good intro to agent sims.
By A Customer
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.
5 of 5 people found the following review helpful.
Good advertisement for the generative approach to social science
By Xavier Marquez
The idea of the "generative" approach to social science, as described in this book, is that we attempt to understand the workings of a society not through "discursive" models (as in qualitative social science) or through game theoretical models with extremely simplified assumptions and homogeneous agents (as in more "quantitative" approaches), but by creating a simplified version of the society and letting it "run." So we use software (or pen and paper; Schelling's pioneering models of segregation were studied without the benefit of a computer) to generate an environment and simplified agents that interact with one another, and then we see what happens. The advantages of this approach is that one can add complexity to a model in a controlled manner and study the resulting dynamics (rather than merely static equilibria); so, for example, one might start with agents who move and eat, then see what happens if you add the ability to trade, and so on (besides, it's fun to play around with such models). The disadvantages, however, come from the very flexibility of the approach, which allows for highly complex models: it is sometimes hard to tell whether or not a particularly interesting result is simply an artifact of the simulation or the consequence of some particular simplifying assumption (though it should be noted that the same is often true of traditional models, where striking results, like the pareto-optimality of free markets, are sometimes basically artifacts of the simplifying assumptions made for the sake of creating "tractable" models).
Epstein and Axtell report in this book the results of a pioneering 1994 simulation: "Sugarland". They start with a very simple environment, the "sugarscape", consisting of a two-dimensional surface (technically, a torus) with an unevenly distributed resource ("sugar") and add some very simple agents who can move around the sugarscape according to simple rules and "eat" the sugar. As they add more complex rules, they basically "generate" a number of features of societies - like migration patterns, cultural transmissions, combat, trade, credit relationships, disease transmission, etc. - and study how some simple rule changes affect these patterns. The book does not describe in detail how to do this (they give some information that might help in replicating the simulation, but no code); instead, one can read it as a kind of advertisement for the generative approach to social science. Epstein and Axtell excitedly argue that many phenomena can be understood as "emergent" effects of the interaction of agents following simple rules, and hence that the best way of understanding them is by looking at which simple rules are able to generate them. In general, this idea seems reasonable; it is unlikely that we will ever fully understand polities and economies through models composed of homogeneous, fully rational agents. We will need to study complexities that become tractable only through simulation.
But though Epstein and Axtell's results are often suggestive, it is not always clear that they have properly looked at the ways in which minor changes in the parameters of the simulation might disrupt the patterns they find, or sufficiently thought about how to interpret the results of their simulations. As a result, the book is somewhat disappointing: if you are already convinced of the utility of the generative approach, you may not learn much here, and if you are not, then you may think that the authors have not really addressed the important objections to the generative approach. A good example is in the chapter of the book on combat and cultural transmission. Here the assumptions made about how to model cultural transmission and combat between agents seem rather arbitrary (rather than based, for example, on research about human or animal combat), and though they explore a number of alternative specifications, the results seem only lightly grounded theoretically, more an artifact of the simulation than an illuminating model of actual cultural transmission or combat. (By contrast, the chapter on trade and credit is probably the most solid in the book, since they ground the results in economic theory and systematically explore some of the space of alternative rule specifications and parameter values for their agents. Nevertheless, their results there do not go beyond what most sophisticated economists already knew, and do not add much knowledge about the properties of dynamic adjustment in markets - we get no bubbles or other interesting phenomena, for example, and we would need to introduce something like production functions to make the model better reflect actual economies).
Of course, the book is 16 years old right now, and new advances have been made in agent-based simulation and "generative" social science. Besides, as I mentioned above, the book is perhaps best read as an advertisement for the virtues of the "artificial societies" approach rather than as a contribution to the study of actual societies; it opens your appetite rather than satisfies it (I wanted to do some programming as I was reading it, to try to see if I could replicate their results). But it is probably fair to say that the book does not quite succeed at showing that the artificial societies approach is actually worth taking seriously, unless you are already convinced of its merits.
17 of 17 people found the following review helpful.
This is not a "how-to" book
By Amazon Customer
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.
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