The explanatory potential of artificial societies

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Despite that, many feel that such simulations somehow contribute to our understanding. This paper tries to clarify this intuition by investigating whether artificial societies provide potential explanations. It is shown that these potential explanations, if they contribute to our understanding, considerably differ from potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum. The paper discusses how these two kinds explanatory strategies differ, and how potential functional explanations can be appraised.

[1]  Peter Lipton,et al.  Inference to the best explanation , 1993 .

[2]  L. Tesfatsion.  Agent-based computational economics : A constructive approach to economic theory , 2006 .

[3]  Robert L. Axtell,et al.  Population growth and collapse in a multiagent model of the Kayenta Anasazi in Long House Valley , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Robert Axelrod.  Advancing the art of simulation in the social sciences , 1997 .

[5]  Colin Camerer,et al.  Foundations of Human Sociality - Economic Experiments and Ethnographic: Evidence From Fifteen Small-Scale Societies , 2004 .

[6]  Harold Kincaid,et al.  Philosophical Foundations of the Social Sciences , 1995 .

[7]  Stephan Hartmann,et al.  The World as a Process: Simulations in the Natural and Social Sciences , 1996 .

[8]  J. Woodward.  Making Things Happen: A Theory of Causal Explanation , 2003 .

[9]  Timothy A. Kohler,et al.  Dynamics in human and primate societies: agent-based modeling of social and spatial processes , 2000 .

[10]  Joshua M. Epstein,et al.  Understanding Anasazi culture change through agent-based modeling , 2000 .

[11]  Wesley C. Salmon,et al.  Causality and Explanation , 1998 .

[12]  R. Keith Sawyer,et al.  Social explanation and computational simulation , 2004 .

[13]  Joshua M. Epstein,et al.  Agent-based computational models and generative social science , 1999, Complex..

[14]  William A. Brock,et al.  Discrete Choice with Social Interactions , 2001 .

[15]  M. Resnik,et al.  Aspects of Scientific Explanation. , 1966 .

[16]  P. Machamer,et al.  Thinking about Mechanisms , 2000, Philosophy of Science.

[17]  C. Bicchieri.  The grammar of society: the nature and dynamics of social norms , 2005 .

[18]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[19]  Paul Humphreys,et al.  Extending Ourselves: Computational Science, Empiricism, and Scientific Method , 2004 .

[20]  R. Hegselmann,et al.  Simulating Social Phenomena , 1997 .

[21]  Lars-Erik Cederman,et al.  Computational Models of Social Forms: Advancing Generative Process Theory1 , 2005, American Journal of Sociology.

[22]  F. Guala,et al.  Experiments in economics: External validity and the robustness of phenomena , 2005 .

[23]  Klaus G. Troitzsch,et al.  Modelling and simulation in the social sciences from the philosophy of science point of view , 1996 .

[24]  F. Guala,et al.  Experiments in Economics : Testing Theories vs. the Robustness of Phenomena , 2002 .

[25]  Daniel Little.  Varieties of social explanation , 1990 .

[26]  Johannes Lenhard,et al.  Validation of Simulation: Patterns in the Social and Natural Sciences , 2005, J. Artif. Soc. Soc. Simul..

[27]  Nancy Cartwright,et al.  Against Modularity, the Causal Markov Condition, and Any Link Between the Two: Comments on Hausman and Woodward , 2002, The British Journal for the Philosophy of Science.

[28]  J. Woodward,et al.  Scientific Explanation and the Causal Structure of the World , 1988 .

[29]  Stephan Hartmann,et al.  The World as a Process , 1996 .