Robust Evolutionary Algorithm Design for Socio-economic Simulation

Agent-based computational economics (ACE) combines elements from economics and computer science. In this paper, we focus on the relation between the evolutionary technique that is used and the economic problem that is modeled. In the field of ACE, economic simulations often derive parameter settings for the evolutionary algorithm directly from the values of the economic model parameters. In this paper, we compare two important approaches that are dominating ACE research and show that the above practice may hinder the performance of the evolutionary algorithm and thereby hinder agent learning. More specifically, we show that economic model parameters and evolutionary algorithm parameters should be treated separately by comparing the two widely used approaches to social learning with respect to their convergence properties and robustness. This leads to new considerations for the methodological aspects of evolutionary algorithm design within the field of ACE.

[1]  Murat Yildizoglu,et al.  Competing R&D Strategies in an Evolutionary Industry Model , 2002 .

[2]  F. Vega-Redondo The evolution of Walrasian behavior , 1997 .

[3]  Sibel Sirakaya,et al.  On-line computation of Stackelberg equilibria with synchronous parallel genetic algorithms , 2003 .

[4]  W. Brian Arthur,et al.  On designing economic agents that behave like human agents , 1993 .

[5]  A. Cournot Researches into the Mathematical Principles of the Theory of Wealth , 1898, Forerunners of Realizable Values Accounting in Financial Reporting.

[6]  Frank Westerhoff,et al.  Modeling Exchange Rate Behavior with a Genetic Algorithm , 2003 .

[7]  George J. Mailath,et al.  Introduction: Symposium on evolutionary game theory , 1992 .

[8]  J. A. La Poutré,et al.  Heterogeneous, boundedly rational agents in the cournot duopoly , 2003 .

[9]  Thomas Riechmann Learning in Economics , 2001 .

[10]  T. Riechmann Genetic algorithm learning and evolutionary games , 2001 .

[11]  Bruce Edmonds,et al.  Towards a Descriptive Model of Agent Strategy Search , 1999 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  Herbert Dawid,et al.  Adaptive Learning by Genetic Algorithms, Analytical Results and Applications to Economic Models, 2nd extended and revised edition , 1999 .

[14]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[15]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[16]  Hans M. Amman,et al.  On social learning and robust evolutionary algorithm design in economic games , 2005, 2005 IEEE Congress on Evolutionary Computation.

[17]  J. Duffy,et al.  Using Genetic Algorithms to Model the Evolution of Heterogeneous Beliefs , 1999 .

[18]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[19]  Herbert Dawid,et al.  Adaptive Learning by Genetic Algorithms , 1996 .

[20]  Jasmina Arifovic,et al.  STABILITY OF EQUILIBRIA UNDER GENETIC-ALGORITHM ADAPTATION: AN ANALYSIS , 1998 .

[21]  Shu-Heng Chen,et al.  Toward a computable approach to the efficient market hypothesis: An application of genetic programming , 1995 .

[22]  Jasmina Arifovic,et al.  Revisiting Individual Evolutionary Learning in the Cobweb Model – An Illustration of the Virtual Spite-Effect , 2006 .

[23]  Drew Fudenberg,et al.  Learning in Games , 1998 .

[24]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[25]  Jasmina Arifovic,et al.  Evolutionary dynamics of currency substitution , 2001 .

[26]  Jasmina Arifovic Genetic algorithm learning and the cobweb model , 1994 .

[27]  Edmund Chattoe,et al.  Just How (Un)realistic are Evolutionary Algorithms as Representations of Social Processes? , 1998, J. Artif. Soc. Soc. Simul..

[28]  D. Midgley,et al.  Breeding competitive strategies , 1997 .

[29]  Leigh Tesfatsion,et al.  Introduction to the CE Special Issue on Agent-Based Computational Economics , 2001 .

[30]  Herbert Dawid,et al.  Learning of cycles and sunspot equilibria by Genetic Algorithms , 1996 .

[31]  Nicolaas J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses , 1998 .

[32]  Tomas Klos,et al.  Decentralized Interaction and Co-Adaptation in the Repeated Prisoner&2018;s Dilemma , 1999, Comput. Math. Organ. Theory.

[33]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[34]  Eric Ringhut,et al.  Modeling Expectations with GENEFER – an Artificial Intelligence Approach , 2003 .

[35]  Ellen R. McGrattan,et al.  Money as a medium of exchange in an economy with artificially intelligent agents , 1990 .

[36]  P. McNelis,et al.  Approximating and Simulating the Stochastic Growth Model: Parameterized Expectations, Neural Networks, and the Genetic Algorithm , 2001 .

[37]  Shu-Heng Chen,et al.  Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market , 2001 .

[38]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[39]  Han La Poutré,et al.  Stabilization of tag-mediated interaction by sexual reproduction in an evolutionary agent system , 2005, Inf. Sci..

[40]  Volker Nissen,et al.  Evolutionary Algorithms in Management Applications , 1995 .

[41]  Jörgen W. Weibull,et al.  Evolutionary Game Theory , 1996 .