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More About This Textbook
Overview
Agentbased computational modeling is changing the face of social science. In Generative Social Science, Joshua Epstein argues that this powerful, novel technique permits the social sciences to meet a fundamentally new standard of explanation, in which one "grows" the phenomenon of interest in an artificial society of interacting agents: heterogeneous, boundedly rational actors, represented as mathematical or software objects. After elaborating this notion of generative explanation in a pair of overarching foundational chapters, Epstein illustrates it with examples chosen from such farflung fields as archaeology, civil conflict, the evolution of norms, epidemiology, retirement economics, spatial games, and organizational adaptation. In elegant chapter preludes, he explains how these widely diverse modeling studies support his sweeping case for generative explanation.
This book represents a powerful consolidation of Epstein's interdisciplinary research activities in the decade since the publication of his and Robert Axtell's landmark volume, Growing Artificial Societies. Beautifully illustrated, Generative Social Science includes a CD that contains animated movies of core model runs, and programs allowing users to easily change assumptions and explore models, making it an invaluable text for courses in modeling at all levels.
Editorial Reviews
Science  Daniel Diermeier
It should be noted that having all these contributions in one place is not only useful but pleasing...Epstein's book is a concise and well articulated defense of agentbased modeling. Generative Social Science is essential reading for anyone seriously interested in the foundations and the practice of agentbased modeling.JASSS  Rosaria Conte
Epstein's Generative Social Science . . . is to be regarded as a success. It is a highly professional book, comestible also by nonexperts without giving up scientific rigour. Probably because the author is fond of its subject matter, and manages to transfer his enthusiasm into the reader, the book may be read all at once, as a narrative. . . . In sum, there are good reasons to expect that the community of simulators will welcome this book with enthusiasm, and that other supporters will be recruited.American Journal of Sociology  Michael Macy
Epstein's generative manifesto is essential reading for anyone seriously interested in explaining social life.From the Publisher
"It should be noted that having all these contributions in one place is not only useful but pleasing...Epstein's book is a concise and well articulated defense of agentbased modeling. Generative Social Science is essential reading for anyone seriously interested in the foundations and the practice of agentbased modeling."—Daniel Diermeier, Science"Epstein's Generative Social Science . . . is to be regarded as a success. It is a highly professional book, comestible also by nonexperts without giving up scientific rigour. Probably because the author is fond of its subject matter, and manages to transfer his enthusiasm into the reader, the book may be read all at once, as a narrative. . . . In sum, there are good reasons to expect that the community of simulators will welcome this book with enthusiasm, and that other supporters will be recruited."—Rosaria Conte, JASSS
"Epstein's generative manifesto is essential reading for anyone seriously interested in explaining social life."—Michael Macy, American Journal of Sociology
Science
It should be noted that having all these contributions in one place is not only useful but pleasing...Epstein's book is a concise and well articulated defense of agentbased modeling. Generative Social Science is essential reading for anyone seriously interested in the foundations and the practice of agentbased modeling.— Daniel Diermeier
JASSS
Epstein's Generative Social Science . . . is to be regarded as a success. It is a highly professional book, comestible also by nonexperts without giving up scientific rigour. Probably because the author is fond of its subject matter, and manages to transfer his enthusiasm into the reader, the book may be read all at once, as a narrative. . . . In sum, there are good reasons to expect that the community of simulators will welcome this book with enthusiasm, and that other supporters will be recruited.— Rosaria Conte
American Journal of Sociology
Epstein's generative manifesto is essential reading for anyone seriously interested in explaining social life.— Michael Macy
Science
It should be noted that having all these contributions in one place is not only useful but pleasing...Epstein's book is a concise and well articulated defense of agentbased modeling. Generative Social Science is essential reading for anyone seriously interested in the foundations and the practice of agentbased modeling.— Daniel Diermeier
JASSS
Epstein's Generative Social Science . . . is to be regarded as a success. It is a highly professional book, comestible also by nonexperts without giving up scientific rigour. Probably because the author is fond of its subject matter, and manages to transfer his enthusiasm into the reader, the book may be read all at once, as a narrative. . . . In sum, there are good reasons to expect that the community of simulators will welcome this book with enthusiasm, and that other supporters will be recruited.— Rosaria Conte
Product Details
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Meet the Author
Joshua M. Epstein is a Senior Fellow in Economic Studies at the Brookings Institution, a founding member of the BrookingsJohns Hopkins Center on Social and Economic Dynamics, and a member of the External Faculty of the Santa Fe Institute. He is the coauthor of "Growing Artificial Societies: Social Science from the Bottom Up" and the author of "Nonlinear Dynamics, Mathematical Biology, and Social Science".
Read an Excerpt
Generative Social Science
Studies in AgentBased Computational ModelingBy Joshua M. Epstein
Princeton University Press
Copyright © 2006 Princeton University PressAll right reserved.
ISBN: 9780691125473
Chapter One
AGENTBASED COMPUTATIONAL MODELS AND GENERATIVE SOCIAL SCIENCEJOSHUA M. EPSTEIN
This article argues that the agentbased computational model permitsa distinctive approach to social science for which the term "generative" is suitable. In defending this terminology, features distinguishing the approach from both "inductive" and "deductive" science are given. Then, the following specific contributions to social science are discussed: The agentbased computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agentbased modeling provides a powerful way to address certain enduringand especially interdisciplinaryquestions. It allows one to subject certain core theoriessuch as neoclassical microeconomicsto important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give riseor "map up"to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agentbased ("bottom up") models. The agentbased approach maywell have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agentbased modeling offers powerful new forms of hybrid theoreticalcomputational work; these are particularly relevant to the study of nonequilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social sciencesome related to intractability, and some to undecidability proper. Finally, since "emergence" figures prominently in this literature, I take up the connection between agentbased modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible.
Generative Social Science
The agentbased computational modelor artificial societyis a new scientific instrument. It can powerfully advance a distinctive approach to social science, one for which the term "generative" seems appropriate. I will discuss this term more fully below, but in a strong form, the central ideais this: To the generativist, explaining the emergence of macroscopic societal regularities, such as norms or price equilibria, requires that one answer the following question:
The Generativist's Question
The agentbased computational model is wellsuited to the study of this question since the following features are characteristic:
HETEROGENEITY
Representative agent methodscommon in macroeconomicsare not used in agentbased models (see Kirman 1992). Nor are agents aggregated into a few homogeneous pools. Rather, agent populations are heterogeneous; individuals may differ in myriad waysgenetically, culturally, by social network, by preferencesall of which may change or adapt endogenously over time.
AUTONOMY
There is no central, or "topdown," control over individual behavior in agentbased models. Of course, there will generally be feedback from macrostructures to microstructures, as where newborn agents are conditioned by social norms or institutions that have taken shape endogenously through earlier agent interactions. In this sense, micro and macro will typically coevolve. But as a matter of model specification, no central controllers or other higher authorities are posited ab initio.
EXPLICIT SPACE
Events typically transpire on an explicit space, which may be a landscape of renewable resources, as in Epstein and Axtell (1996), an ndimensional lattice, or a dynamic social network. The main desideratum is that the notion of "local" be well posed.
LOCAL INTERACTIONS
Typically, agents interact with neighbors in this space (and perhaps with environmental sites in their vicinity). Uniform mixing is generically not the rule. It is worth noting that although this next feature is logically distinct from generativity, many computational agentbased models also assume:
BOUNDED RATIONALITY
There are two components of this: bounded information and bounded computing power. Agents do not have global information, and they do not have infinite computational power. Typically, they make use of simple rules based on local information (see Simon 1982 and Rubinstein 1998).
The agentbased model, then, is especially powerful in representing spatially distributed systems of heterogeneous autonomous actors with bounded information and computing capacity who interact locally.
The Generativist's Experiment
In turn, given some macroscopic explananduma regularity to be explainedthe canonical agentbased experiment is as follows:
Concisely, is the way generative social scientists answer. In fact, this type of experiment is not new and, in principle, it does not necessarily involve computers. However, recent advances in computing, and the advent of largescale agentbased computational modeling, permit a generative research program to be pursued with unprecedented scope and vigor.
Examples
A range of important social phenomena have been generated in agentbased computational models, including: rightskewed wealth distributions (Epstein and Axtell 1996), rightskewed firm size and growth rate distributions (Axtell 1999), price distributions (Bak et al. 1993), spatial settlement patterns (Dean et al. 1999), economic classes (Axtell et al. 2001), price equilibria in decentralized markets(Albin and Foley 1990; Epstein and Axtell 1996), trade networks (Tesfatsion 1995; Epstein and Axtell 1996), spatial unemployment patterns(Topa 1997), excess volatility in returns to capital (Bullard and Duffy 1998), military tactics (Ilachinski 1997), organizational behaviors (Prietula, Carley, and Gasser 1998), epidemics (Epstein and Axtell 1996), traffic congestion patterns (Nagel and Rasmussen 1994), cultural patterns (Axelrod 1997c; Epstein and Axtell 1996), alliances (Axelrod and Bennett 1993; Cederman 1997), stock market price time series (Arthur et al. 1997), voting behaviors (Kollman, Miller, and Page 1992), cooperation in spatial games (Lindgren and Nordahl 1994; Epstein 1998; Huberman and Glance 1993; Nowak and May 1992; Miller 1996), and demographic histories (Dean et al. 1999). These examples manifest a wide range of (often implicit) objectives and levels of quantitative testing.
Before discussing specific models, it will be useful to identify certain changes in perspective that this approach may impose on the social sciences. Perhaps the most fundamental of these changes involves explanation itself.
Explanation and Generative Sufficiency
Agentbased models provide computational demonstrations that a given microspecification is in fact sufficient to generate a macrostructure of interest. Agentbased modelers may use statistics to gauge the generative sufficiency of a given microspecificationto test the agreement between realworld and generated macro structures. (On levels of agreement, see Axtell and Epstein 1994.) A good fit demonstrates that the target macrostructurethe explanandumbe it a wealth distribution, segregation pattern, price equilibrium, norm, or some other macrostructure, is effectively attainable under repeated application of agentinteraction rules: It is effectively computable by agent society. (The view of society as a distributed computational device is developed more fully below.) Indeed, this demonstration is taken as a necessary condition for explanation itself. To the generativistconcerned with formation dynamicsit does not suffice to establish that, if deposited in some macroconfiguration, the system will stay there. Rather, the generativist wants an account of the configuration's attainment by a decentralized system of heterogeneous autonomous agents. Thus, the motto of generative social science, if you will, is: If you didn't grow it, you didn't explain its emergence. Or, in the notation of firstorder logic:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)
It must be emphasized that the motto applies only to that domain of problems involving the formation or emergence of macroscopic regularities. Proving that some configuration is a Nash equilibrium, for example, arguably does explain its persistence, but does not account for its attainment. Regarding the converse of expression (1), if a microspecification, m, generates a macrostructure of interest, then m is a candidate explanation. But it may be a relatively weak candidate; merely generating a macrostructure does not necessarily explain its formation particularly well. Perhaps Barnsley's fern (Barnsley 1988) is a good mathematical example. The limit object indeed looks very much like a black spleenwort fern. Butunder iteration of a certain affine function systemit assembles itself in a completely unbiological way, with the tip first, then a few outer branches, eventually a chunk of root, back to the tip, and so forthnot connectedly from the bottom up (now speaking literally).
It may happen that there are distinct microspecifications having equivalent generative power (their generated macrostructures fit the macrodata equally well). Then, as in any other science, one must do more work, figuring out which of the microspecifications is most tenable empirically. In the context of social science, this may dictate that competing microspecifications with equal generative power be adjudicated experimentallyperhaps in the psychology lab.
In summary, if the microspecification m does not generate the macrostructure x, then m is not a candidate explanation. If m does generate x, it is a candidate. If there is more than one candidate, further work is required at the microlevel to determine which m is the most tenable explanation empirically. For most of the social sciences, it must be said, the problem of multiple competing generative accounts would be an embarrassment of riches. The immediate agenda is to produce generative accounts per se. The principal instrument in this research program is the agentbased computational model. And as the earlier examples suggest, the effort is underway.
This agenda imposes a constructivist (intuitionistic) philosophy on social science. In the air is a foundational debate on the nature of explanation reminiscent of the controversy on foundations of mathematics in the 1920s30s. Central to that debate was the intuitionists' rejection of nonconstructive existence proofs (see below): their insistence that meaningful "existence in mathematics coincides with constructibility" (FraenkelandBarHillel 1958, 207). While the specifics are of course different hereand I am not discussing intuitionism in mathematics properthis is the impulse, the spirit, of the agentbased modelers: If the distributed interactions of heterogeneous agents can't generate it, then we haven't explained its emergence.
Generative versus Inductive and Deductive
From an epistemological standpoint, generative social science, while empirical (see below), is not inductive, at least as that term is typically used in the social sciences (e.g., as where one assembles macroeconomic data and estimates aggregate relations econometrically). (For a nice introduction to general problems of induction, beginning with Hume, see Chalmers 1982. On inductive logic, see Skyrms 1986. For Bayesians and their critics, see, respectively, Howson and Urbach 1993 and Glymour 1980.)
The relation of generative social science to deduction is more subtle. The connection is of particular interest because there is an intellectual tradition in which we account an observation as explained precisely when we can deduce the proposition expressing that observation from other, more general, propositions. For example, we explain Galileo's leaning Tower of Pisa observation (that heavy and light objects dropped from the same height hit the ground simultaneously) by strictly deducing, from Newton's Second Law and the Law of Universal Gravitation, the following proposition: "The acceleration of a freely falling body near the surface of the earth is independent of its mass." In the present connection, we seek to explain macroscopic social phenomena. And we are requiring that they be generated in an agentbased computational model. Surprisingly, in that event, we can legitimately claim that they are strictly deducible. In particular, if one accepts the ChurchTuring thesis, then every computationincluding every agentbased computationcan be executed by a suitable register machine (Hodel 1995; Jeffrey 1991). It is then a theorem of logic and computability that every program can be simulated by a firstorder language. In particular, with N denoting the natural numbers:
Theorem. Let P be a program. There is a firstorder language L, and for each a N a sentence C(a) of L, such that for all a N, the Pcomputation with input a halts the sentence C(a) is logically valid.
This theorem allows one to use the recursive unsolvability of the halting problem to establish the recursive unsolvability of the validity problem in firstorder logic (see Kleene 1967). Explicit constructions of the correspondence between register machine programs and the associated logical arguments are laid out in detail by Jeffrey (1991) and Hodel (1995). The point here is that for every computation, there is a corresponding logical deduction. (And this holds even when the computation involves "stochastic" features, since, on a computer, these are produced by deterministic pseudorandom number generation (see Knuth 1969). Even if one conducts a statistical analysis over some distribution of runsusing different random seedseach run is itself a deduction. Indeed, it would be quite legitimate to speak, in that case, of a distribution of theorems.) In any case, from a technical standpoint, generative implies deductive, a point that will loom large later, when we argue that agentbased modeling and classical emergentism are incompatible.
Importantly, however, the converse does not apply: Not all deductive argument has the constructive character of agentbased modeling. Nonconstructive existence proofs are obvious examples. These work as follows: Suppose we wish to prove the existence of an x with some property (e.g., that it is an equilibrium). We take as an axiom the socalled Law of the Excluded Middle that (i) either x exists or x does not exist. Next, we (ii) assume that x does not exist, and (iii) derive a contradiction. From this we conclude that (iv) x must exist. But we have failed to exhibit x, or indicate any algorithm that would generate it, patently violating the generative motto (1). The same holds for many nonconstructive proofs in mathematical economics and game theory (e.g., deductions establishing the existence of equilibria using fixedpoint theorems). See Lewis 1985. In summary, then, generative implies deductive, but the converse is not true.
Given the differences between agentbased modeling and both inductive and deductive social science, a distinguishing term seems appropriate. The choice of "generative" was inspired by Chomsky's(1965) early usage: Syntactic theory seeks minimal rule systems that are sufficient to generate the structures of interest, grammatical constructions among them. The generated structures of interest here are, of course, social.
Now, at the outset, I claimed that the agentbased computational model was a scientific instrument. A fair question, then, is whether agentbased computational modeling offers a powerful new way to do empirical research. I will argue that it does. Interestingly, one of the early efforts involves the seemingly remote fields of archaeology and agentbased computation.
(Continues...)
Table of Contents
Introduction xi
Prelude to Chapter 1: The Generativist Manifesto 1
Chapter 1: AgentBased Computational Models and Generative Social Science by Joshua M. Epstein 4
Prelude to Chapter 2: Confession of a Wandering Bark 47
Chapter 2: Remarks on the Foundations of AgentBased Generative Social Science by Joshua M. Epstein 50
Prelude to Chapter 3: Equilibrium, Explanation, and Gauss's Tombstone 72
Chapter 3: NonExplanatory Equilibria: An Extremely Simple Game with (Mostly) Unattainable Fixed Points by Joshua M. Epstein and Ross A. Hammond 75
Appendix to Chapter 3: Large Effect of a Subtle Rule Change 86
Prelude to Chapters 46: Generating Civilizations: The 1050 Project and the Artificial Anasazi Model 88
Chapter 4: Understanding Anasazi Culture Change through AgentBased Modeling Jeffrey S. Dean, George J. Gumerman, Joshua M. Epstein, Robert L. Axtell, Alan C. Swedlund, Miles T. Parker, and Stephen McCarroll 90
Chapter 5: Population Growth and Collapse in a Multiagent Model of the Kayenta Anasazi in Long House Valley by Robert L. Axtell, Joshua M. Epstein, Jeffrey S. Dean, George J. Gumerman, Alan C. Swedlund, Jason Harburger, Shubha Chakravarty, Ross Hammond, Jon Parker, and Miles Parker 117
Chapter 6: The Evolution of Social Behavior in the Prehistoric American Southwest by George J. Gumerman, Alan C. Swedlund, Jeffrey S. Dean, and Joshua M. Epstein 130
Prelude to Chapter 7: Generating Patterns in the Timing of Retirement 144
Chapter 7: Coordination in Transient Social Networks: An AgentBased Computational Model of the Timing of Retirement by Robert L. Axtell and Joshua M. Epstein 146
Prelude to Chapter 8: Generating Classes without Conquest 175
Chapter 8: The Emergence of Classes in a MultiAgent Bargaining Model by Robert L. Axtell, Joshua M. Epstein, and H. Peyton Young 177
Prelude to Chapter 9: Generating Zones of Cooperation in the Prisoner's Dilemma Game 196
Chapter 9: Zones of Cooperation in Demographic Prisoner's Dilemma by Joshua M. Epstein 199
Appendix to Chapter 9: Generating Norm Maps in the Demographic Coordination Game 222
Prelude to Chapter 10: Generating Thoughtless Conformity to Norms 225
Chapter 10: Learning to be Thoughtless: Social Norms and Individual Computation by Joshua M. Epstein 228
Prelude to Chapter 11: Generating Patterns of Spontaneous Civil Violence 245
Chapter 11: Modeling Civil Violence: An AgentBased Computational Approach by Joshua M. Epstein 247
Prelude to Chapter 12: Generating Epidemic Dynamics 271
Chapter 12: Toward a Containment Strategy for Smallpox Bioterror: An IndividualBased Computational Approach by Joshua M. Epstein, Derek A.T. Cummings, Shubha Chakravarty, Ramesh M. Singha, and Donald S. Burke 277
Prelude to Chapter 13: Generating Optimal Organizations 307
Chapter 13: Growing Adaptive Organizations: An AgentBased Computational Approach by Joshua M. Epstein 309
Coda 345
Index 349