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Evolutionary Computation in Economics

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Encyclopedia of Machine Learning and Data Mining

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Definition

Evolutionary computation (EC) is a field in computational intelligence that takes its inspiration from nature to develop methods that resolve continuous optimization and combinatorial optimization problems. When it comes to economics, it is the area of research that involves the use of EC techniques, also subclassified as evolutionary algorithms (EAs), cultural algorithms, and self-organization algorithms, among others, in order to approach topics in economic science. The algorithms, defined as generic population-based metaheuristic optimization algorithms, are developed on the basis of the concept of biological evolution and use iterative processes such as reproduction, mutation, recombination, and selection. Some of these methods, such as genetic algorithms (GAs), genetic programming (GP), evolutionary programming (EP), estimation of distribution algorithms (EDA), evolutionary strategies (ESs), memetic algorithms, harmony search, and artificial life, have been studied and...

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Recommended Reading

  • Agapie A, Agapie A (2001) Evolutionary computation for econometric modeling. Adv Model Optim 3(1): 1–5

    MATH  Google Scholar 

  • Alexandrova-Kabadjova B, Tsang E, Krause A (2011) Competition is bad for consumers: analysis of an artificial payment card market. J Adv Comput Intell Intell Inform 15:188–196

    Article  Google Scholar 

  • Amin M (2002) Restructuring the electric enterprise: simulating the evolution of the electric power industry with intelligent adaptive agents. In: Faruqui A, Eakin K (eds) Market analysis and resource management, chapter 3. Kluwer Academic Publishers, Boston/Dordetch/London

    Google Scholar 

  • Arifovic J (1994) Genetic algorithm learning and the cobweb model. J Econ Dyn Control 18:3–28

    Article  MATH  Google Scholar 

  • Arifovic J (1996) The behavior of the exchange rate in the genetic algorithm and experimental economics. J Political Econ 104:510–541

    Article  Google Scholar 

  • Arthur WB (1991) Learning and adaptive economic behavior. Designing economic agents that act like human agents: a behavioral approach to bounded rationality. Am Econ Rev 81:353–359

    Google Scholar 

  • Arthur WB, Holland JH, LeBaron B, Palmer RG, Talyer P (1997) Asset pricing under endogenous expectations in an artificial stock market. In: Brian Arthur W, Durlauf S, Lane D (eds) The economy as an evolving complex system II. Addison-Wesley, Reading

    Google Scholar 

  • Axelrod R (1987) The evolution of strategies in the iterated prisoner’s dilemma. Genetic algorithms and simulated annealing of research notes in AI, chapter 3. Pitman/Morgan Kaufmann, London/Los Altos, pp 32–41

    Google Scholar 

  • Axelrod R (2003) Advancing the art of simulation in the social sciences. Jpn J Manag Inf Syst, Spec Issue Agent-Based Model 12(3):16–22

    Google Scholar 

  • Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211:1390–1396

    Article  MathSciNet  MATH  Google Scholar 

  • Axelrod R, Tesfatsion L (2006) A guide for newcomers to agent-based modeling in the social sciences. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, volume 2: agent-based computational economics. Handbooks in economics, chapter Appendix A. North-Holland Amsterdam, pp 1647–1656

    Google Scholar 

  • Axtell R (2000) Why agents? On the varied motivations for agent computing in the social sciences. Working paper 17, Center on Social and Economic Dynamics

    Google Scholar 

  • Brenner T (2006) Agent learning representation advice in modelling economic learning. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, volume 2: agent-based computational economics. Handbooks in economics, chapter 18 North-Holland, pp 895–948

    Google Scholar 

  • Bullard J, Arifovic J, Duffy J (1995) Learning in a model of economic growth and development. Working paper 1995-017A, Federal Reserve Bank Of St. Louis

    Google Scholar 

  • Bullard J, Duffy J (1999) Using genetic algorithms to model the evolution of heterogeneous beliefs. Comput Econ 13:41–60

    Article  MATH  Google Scholar 

  • Chen S-H (2007) Editorial: computationally intelligent agents in economics and finance. Inf Sci 177(5):1153–1168

    Article  Google Scholar 

  • Chen S-H, Ni CC (2000) Simulating the ecology of oligopolistic competition with genetic algorithms. Knowl Inf Syst 2(2):285–309

    Article  MATH  Google Scholar 

  • Chen S-H, Yeh C-H (1996) Genetic programming learning in the cobweb model with speculators. In: International computer symposium (ICS’96). Proceedings of international conference on artificial intelligence. National Sun Yat-Sen University, Kaohsiung, R.O.C., 19–21, pp 39–46

    Google Scholar 

  • Duffy J, Engle-Warnick J (2001) Using symbolic regression to infer strategies from experimental data. In: Chen S-H (ed) Evolutionary computation in economics and finance. Physica-Verlag, New York, pp 61–82

    Google Scholar 

  • Edmonds B (1999) Modelling bounded rationality in agent-based simulations using the evolution of mental models. In: Brenner T (ed) Computational techniques for modelling learning in economics. Kluwer, Boston, pp 305–332

    Chapter  Google Scholar 

  • Greene WH (2003) Econometric analysis, 5th edn. Prentice Hall, Upper Saddle River, 07456

    Google Scholar 

  • Heppenstall A, Evans A, Birkin M (2006) Using hybrid agent-based systems to model spatially-influenced retail markets. J Artif Soc Soc Simul 9(3): 2

    Google Scholar 

  • Izumi K, Ueda K (2001) Phase transition in a foreign exchange market-analysis based on an artificial market approach. IEEE Trans Evol Comput 5(5):456–470

    Article  Google Scholar 

  • Jin N, Tsang EPK (2006) Co-adaptive strategies for sequential bargaining problems with discount factors and outside options. In: Proceedings of the IEEE congress on evolutionary computation, Vancouver. IEEE Press, pp 7913–7920

    Google Scholar 

  • Koza J (1992) A genetic approach to econometric modelling. In: Bourgine P, Walliser B (eds) Economics and cognitive science. Pergamon Press, Oxford/New York, pp 57–75

    Chapter  Google Scholar 

  • Lucas RE (1986) Adaptive behavior and economic theory. In: Hogarth RM, Reder MW (eds) Rational choice: the contrast between economics and psychology. University of Chicago Press, Chicago/London, pp 217–242

    Google Scholar 

  • Marimon R, McGrattan E, Sargent TJ (1990) Money as a medium of exchange in an economy with artificially intelligent agents. J Econ Dyn Control 14:329–373

    Article  MathSciNet  MATH  Google Scholar 

  • Martinez-Jaramillo S, Tsang EPK (2009) An heterogeneous, endogenous and coevolutionary gp-based financial market. IEEE Trans Evol Comput 13:33–55

    Article  Google Scholar 

  • Nash J (1950) The barganing problem. Econometrica 18:155–162

    Article  MathSciNet  Google Scholar 

  • Östermark R (1999) Solving irregular econometric and mathematical optimization problems with a genetic hybrid algorithm. Comput Econ 13(2):103–115

    Article  MATH  Google Scholar 

  • Papadimitriou C (1994) Computational complexity. Addison-Wesley, Reading

    MATH  Google Scholar 

  • Simon HA (1957) Models of man: social and rational. John Wiley and Sons, Inc., New York

    MATH  Google Scholar 

  • Smith JM (1972) Game theory and the evolution of fighting. Edinburgh University Press, Edinburgh, pp 8–28

    Google Scholar 

  • Tesfatsion L (2006) Agent-based computational economics: a constructive approach to economic theory. In: Judd KL, Tesfatsion L (eds) Handbook of computational economics, volume 2: agent-based computational economics. Volume 2 of handbooks in economics, chapter 16 North-Holland Amsterdam, pp 831–880

    Google Scholar 

  • Tsang EPK (2008) Computational intelligence determines effective rationality. Int J Autom Comput 5:63–66

    Article  Google Scholar 

  • von Neumann J, Morgenstern O (1944) Theory of games and economic behavior. Princeton University Press, Princeton

    MATH  Google Scholar 

  • Vriend NJ (1995) Self-organization of markets: an example of a computational approach. Comput Econ 8: 205–231

    Article  MATH  Google Scholar 

  • Witt U (2008) Evolutionary economics. In The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. Durlauf and Lawrence E. Blume. Palgrave Macmillan, London

    Google Scholar 

  • Wooldridge M (2002) An introduction to multiAgent systems. Wiley, Chichester

    Google Scholar 

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Author information

Authors and Affiliations

  1. Banco de México, Av. 5 de Mayo No. 6, 06059, Mexico City, Mexico

    Biliana Alexandrova-Kabadjova

  2. Directorate of Regulation and Supervision, Banco de México, Mexico City, Mexico

    Alma Lilia García-Almanza

  3. Directorate of Financial System Risk Analysis, Banco de México, Av. 5 de Mayo No. 6, 06059, Mexico City, Mexico

    Serafín Martínez-Jaramillo

Authors
  1. Biliana Alexandrova-Kabadjova
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  2. Alma Lilia García-Almanza
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  3. Serafín Martínez-Jaramillo
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Corresponding author

Correspondence to Biliana Alexandrova-Kabadjova .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Alexandrova-Kabadjova, B., García-Almanza, A.L., Martínez-Jaramillo, S. (2017). Evolutionary Computation in Economics. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_87

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