The Lawrence Jacobsen Library
Books Received (Primate-Science/PrimateLit)

DYNAMICS IN HUMAN AND PRIMATE SOCIETIES:
AGENT -BASED MODELING OF SOCIAL AND SPATIAL PROCESSES

Editors
Timothy A. Kohler
George J. Gumerman


Santa Fe Institute
Studies in the Sciences of Complexity



Oxford University Press 2000



CONTENTS


Preface xi


Putting Social Sciences Together Again: An Introduction to the Volume 1
Timothy A. Kohler


Nonlinear and Synthetic Models for Primate Societies 19
Irenaeus J. A. te Boekhorst and Charlotte K. Hemelrijk


The Evolution of Cooperation in an Ecological Context: An Agent Based Model 4 5
John W. Pepper and Barbara B. Smuts


Evolution of Inference 77
Brian Skyrms


Trajectories to Complexity in Artificial Societies: Rationality, Belief, and Emotions 89
Jim E. Doran


MAGICAL Computer Simulation of Mesolithic Foraging 107
Mark Winter Lake


Be There Then: A Modeling Approach to
Settlement Determinants and Spatial Efficiency Among
Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest 145
Timothy A. Kohler, James Kresl, Carla Van West, Eric
Carr, and Richard H. Wilshusen


Understanding Anasazi Culture Change Through Agent-Based Modeling 179
Jeffrey S. Dean, George J. Gumerman, Joshua M. Epstein,
Robert L. Axtell, Alan C. Swedlund, Miles T. Parker, and
Steven McCarroll


Anti-Chaos, Common Property, and the Emergence of Cooperation 207
J. Stephen Lansing


The Political Impact of Marriage in a Virtual Polynesian Society 225
Cathy A. Small


The Impact of Raiding on Settlement Patterns in
the Northern Valley of Oaxaca: An Approach Using Decision Trees 251
Robert G. Reynolds


The Fractal House of Pharaoh:
Ancient Egypt as a Complex Adaptive System, a Trial Formulation 355
Mark Lehner


Modeling Sociality: The View from Europe 355
Nigel Gilbert


Agent-Based Modeling of Small-Scale Societies: State of the Art and Future Prospects 373
Henry T. Wright


Index 387



EXCERPT FROM THE INTRODUCTION


Putting Social Sciences Together Again: An Introduction to the Volume


Timothy A. Kohler


"Whose game was empires and whose stakes were thrones,
Whose table earth--whose dice were human bones"


Lord Byron, Age of Bronze


We accept many definitions for games, most not so grandiose as those of Napoleon treated by Byron. Often when I
demonstrate the simulation of Anasazi settlement discussed in chapter 7 of this volume someone will say, "This is
just a game isn't it?" I'm happy to admit that it is, so long as our definition of games encompasses child's
play-which teaches about and prepares for reality-and not just those frivolous pastimes of adults, which release them
from it.


This volume is based on and made possible by recent developments in the field of agent-based simulation. More than some
dry computer science technology or another corporate software gambit, this technology is in fact provoking great interest
in the possibilities of simulating social, spatial, and evolutionary dynamics in human and primate societies in ways
that have not previously been possible.


What is agent-based modeling? Models of this sort are sometimes also called individual-oriented, or distributed
artificial intelligence-based. Action in such models takes place through agents, which are processes, however
simple, that collect information about their environment, make decisions about actions based on that information,
and act (Doran et al. 1994:200). Artificial societies composed of interacting collections of such agents allow
controlled experiments (of the sort impossible in traditional social research) on the effects of tuning one behavioral
or environmental parameter at a time (Epstein and Axtell 1996:1-20). Research using these models emphasizes dynamics
rather than equilibria, distributed processes rather than systems-level phenomena, and patterns of relationships
among agents rather than relationships among variables. As a result visualization is an important part of analysis,
affording these approaches a sometimes gamelike and often immediately engaging quality. OK, I admit it-they're fun.


Despite our emphasis on agent-based modeling, we do not mean to imply that it should displace, or is always
superior to, systems-level models based on, for example, differential equations. On the contrary: te Boekhorst and
Hemelrijk (chapter 2) nicely demonstrate how these approaches may be complementary. Even more strongly, we do not
argue that these activities should become, ahead of empirical research, the principal tool of social science. We do
hope to demonstrate that these approaches deserve an important place in the social science toolkit.


All social simulation (whether of the agent-based type, or of whole systems in the tradition of Forrester [1968];
see also van der Leeuw and McGlade [1997]) is viewed with suspicion by many in the research community, even
including some who accept the value of simulation for problems in the physical and biotic domains. I therefore begin
this chapter with a perspective on why such doubts have arisen-and why the researchers whose work is assembled here
think these approaches are useful nonetheless. I then continue with a discussion of some of the problems of
anthropology-the social science I know best-that might be reduced by extended and rigorous application of the sorts
of methods explored in this volume.


Interest in these models crosscuts the social sciences, humanities, and biological sciences. Recent important
and strongly related contributions in the social sciences have issued, for example, from political scientists
(Axelrod 1997) and from economists (Young 1998). In the final section of this chapter I suggest that these
methods have great promise for re-integrating social sciences long isolated by artificial disciplinary boundaries.



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