Advertisement

Journal of Evolutionary Economics

, Volume 21, Issue 5, pp 817–841 | Cite as

Internal selection and market selection in economic Genetic Algorithms

  • Sylvie GeisendorfEmail author
Regular Article

Abstract

Genetic algorithms have been established as an alternative to neoclassical optimization for the illustration of economic agents’ behavior. Critics however, doubt they depict the particularities of social evolution, because they fail to describe intentional behavior. The current paper argues that advocates as well as critics of the procedure have overlooked the crucial necessity to distinguish between internal and external selection in the economy and to include both in economic Genetic Algorithms. The paper claims that such a differentiation will allow the model to depict intentional decisions as well as market selection and help to understand the effects of bounded rationality. It illustrates this point with a brief example modeled after the new-versus-new competition between lean-burn engines and catalysts in the 70th.

Keywords

Genetic Algorithms Internal selection Intentionality External selection Bounded rationality Economic evolution 

Notes

Acknowledgements

The paper has much benefited from the comments of two anonymous referees. Particularly one of them detected several mistakes and gave useful advice. I also thank Fank Thesing and Jan Nill for information on the lean-burn modeling example.

References

  1. Andreoni J, Miller J (1995) Auctions with artificial adaptive agents. Games Econ Behav 10:39–64CrossRefGoogle Scholar
  2. Aldrich HE, Hodgson GM, Hull DL, Knudsen T, Mokyr J, Vanberg V (2008) In defence of generalized Darwinism. J Evol Econ 18:577–596CrossRefGoogle Scholar
  3. Arifovic J (1991) Learning by genetic algorithms in economic environments. Doctoral dissertation, University of ChicagoGoogle Scholar
  4. Arifovic J (1994) Genetic algorithm learning and the cobweb model. J Econ Control 18:3–28CrossRefGoogle Scholar
  5. Arifovic J (1996) The behavior of the exchange rate in the genetic algorithm and experimental economies. J Pol Econ 104:510–541CrossRefGoogle Scholar
  6. Arifovic J, Ledyard J (2002) Computer testbeds: the dynamics of groves-ledyard mechanisms. Society for Computational Economics, Computing in Economics and Finance no. 244Google Scholar
  7. Arifovic J, Maschek MK (2006) Revisiting individual evolutionary learning in the cobweb model–an illustration. Comput Econ 28:333–354CrossRefGoogle Scholar
  8. Axelrod R (1987) The evolution of strategies in the iterated prisoner’s dilemma. In: Davis L (ed) Genetic algorithms and simulated annealing. Kaufman, Los Altos, pp 32–41Google Scholar
  9. Beckenbach F (1999) Learning by genetic algorithms in economics? In: Brenner T (ed) Computational techniques for modelling learning in economics. Advances in computational economics 11. Kluwer, DordrechtGoogle Scholar
  10. Birchenhall C (1995) Modular technical change and genetic algorithms. Comput Econ 8:233–253CrossRefGoogle Scholar
  11. Birchenhall C, Kastrinos N, Metcalfe S (1997) Genetic algorithms in evolutionary modelling. J Evol Econ 7:375–393CrossRefGoogle Scholar
  12. Brenner T (1998) Can evolutionary algorithms describe learning processes? J Evol Econ 8:271–283CrossRefGoogle Scholar
  13. Burke DS, De Jong KA, Grefenstette JJ, Ramsey CL, Wu AS (1998) Putting more genetics into genetic algorithms. Evol Comput 6(4):387–410CrossRefGoogle Scholar
  14. Casari M (2004) Can genetic algorithms explain experimental anomalies. Comput Econ 24:257–275CrossRefGoogle Scholar
  15. Casari M (2006) Markets in equilibrium with firms out of equilibrium: a simulation study. J Econ Behav Organ 65:261–276CrossRefGoogle Scholar
  16. Chattoe E (1998) Just how (Un)realistic are evolutionary algorithms as representations of social processes? J Artif Soc Soc Simul 1. http://www.soc.surrey.ac.uk/JASSS/1/3/2.html
  17. Dawid H (1999) Adaptive learning by genetic algorithms: analytical results and applications to economic models, 2nd revised and extended edition. Springer, HeidelbergGoogle Scholar
  18. Dawid H, Kopel M (1998) On economic applications of the genetic algorithm: a model of the cobweb type. J Evol Econ 8:297–315CrossRefGoogle Scholar
  19. Dopfer K, Potts J (2004) Evolutionary realism: a new ontology for economics. J Econ Methodol 11:195–212CrossRefGoogle Scholar
  20. Duffy P (2000) Learning to speculate: experiments with artificial and real agents. J Econ Dyn Control 25:295–319CrossRefGoogle Scholar
  21. Duffy P, Ünver MU (2008) Internet auctions with artificial adaptive agents: a study on maket design. J Econ Behav Organ 67:394–417CrossRefGoogle Scholar
  22. Fan Y, Liang Q, Wei Y-M (2008) A generalized pattern matching approach for multi-step prediction of crude oil price. Energy Econ 30:889–904CrossRefGoogle Scholar
  23. Farahani RZ, Elahipanah M (2008) A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. Int J Prod Econ 111:229–243CrossRefGoogle Scholar
  24. Foster J (2000) Competitive selection, self-organisation and Joseph A. Schumpeter. J Evol Econ 10:311–328CrossRefGoogle Scholar
  25. Geisendorf S (1999) Genetic algorithms in resource economic models—a way to model bounded rationality in resource exploitation? Santa Fe Institute working paper 99–08–058Google Scholar
  26. Geisendorf S (2001) Evolutorische Ökologische Ökonomie. Metropolis, MarburgGoogle Scholar
  27. Geisendorf S (2009a) The influence of innovation and imitation on economic performance. Econ Issues 14(1):65–94Google Scholar
  28. Geisendorf S (2009b) The economic concept of evolution: self-organization or Universal Darwinism? J Econ Methodol 16:377–391CrossRefGoogle Scholar
  29. Geisendorf S, Weise P (2001) Kreativität und Interaktion. Gestalt Theory 23:293–311Google Scholar
  30. Georges C (2006) Learning with misspecification in an artificial currency market. J Econ Behav Organ 60:70–84CrossRefGoogle Scholar
  31. Haruvy E, Roth AE, Ünver MU (2006) The dynamics of law clerk matching: an experimental and computational investigation of proposals for reform of the market. J Econ Dyn Control 30:457–486CrossRefGoogle Scholar
  32. Hodgson GM, Knudsen T (2006) Why we need a generalized Darwinism, and why generalized Darwinism is not enough. J Econ Behav Organ 61:1–19CrossRefGoogle Scholar
  33. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  34. Holland JH, Miller JH (1991) Artificial adaptive agents in economic theory. Am Econ Rev 81:365–370Google Scholar
  35. Janssen M, de Vries B (1998) The battle of perspectives: a multi-agent model with adaptive responses to climate change. Ecol Econ 26:43–65CrossRefGoogle Scholar
  36. Jawahar N, Balaji AN (2009) A genetic algorithm for the two-stage supply chain distribution problem associated with a fixed charge. Eur J Oper Res 194:496–537CrossRefGoogle Scholar
  37. Kulak O, Yilmaz I, Gunther H-O (2008) A GA-based solution approach for balancing printed circuit board assembly lines. OR Spectrum 30:469–491CrossRefGoogle Scholar
  38. Lawrenz C (2002) Can evolutionary processes result in rational expectations? In: Lehmann-Waffenschmidt M (ed) Perspektiven des Wandels: Evolutorische Ökonomi in der Anwendung. Metropolis, MarburgGoogle Scholar
  39. Lo ST (2008) Crossover inventions and knowledge diffusion of general purpose technologies: evidence from the electrical technology. NBER Working paper series 14043Google Scholar
  40. Mathews JA (2003) Competitive dynamics and economic learning: an extended resource-based view. Ind Corp Change 12:115–145CrossRefGoogle Scholar
  41. Matilla-Garcia M (2006) Are trading rules based on genetic algorithms profitable? Appl Econ Lett 13:123–126CrossRefGoogle Scholar
  42. Miller JH (1986) A genetic model of adaptive economic behavior. Working paper, University of MichiganGoogle Scholar
  43. Miller JH, Carter TB, Rode D (2002) Communication and cooperation. J Econ Behav Organ 47:179–195CrossRefGoogle Scholar
  44. Nill J, Tiessen J (2005) Policies, time, and technological competition: lean burn engine versus catalytic converter in Japan and Europe. In: Sartorius C, Zundel S (eds) Time strategies, innovation and environmental policy. Edward Elgar, Cheltenham, pp 102–132Google Scholar
  45. Pyka A, Fagiolo G (2005) Agent-based modelling: a methodology for neo-schumpetarian economics. Discussion Paper Series 272, Institute for Economics, University of AugsburgGoogle Scholar
  46. Rechenberg H (1973) Evolutionsstrategie. Fromann-holzboog Verlag, StuttgartGoogle Scholar
  47. Riechmann T (1999) Learning and behavioural stability—an economic interpretation of genetic algorithms. J Evol Econ 9:225–242CrossRefGoogle Scholar
  48. Schnaars SP (1994) Managing imitation strategies—how later entrants seize markets form pioneers. The Free Press, New YorkGoogle Scholar
  49. Som O, Kirner E (2009) Innovation patterns of non-R&D intensive manufacturing firms: an evolutionary approach to heterogeneity in innovation behaviour. In: 6th Europ meeting on Appl Evol Econ Jena, 21–23 May 2009Google Scholar
  50. Stoelhorst JW (2008) The explanatory logic and ontological commitments of generalized Darwinism. J Econ Methodol 15:343–363CrossRefGoogle Scholar
  51. Vriend N (2000) An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. J Econ Dyn Control 24:1–19CrossRefGoogle Scholar
  52. Ward MAV (2008) Thirty year quest for lean burn. Design News, 28 April 2008: 16Google Scholar
  53. Witt U (1993) Emergence and dissemination of innovations: some principles of evolutionary economics. In: Daly RH, Chen P (eds) Nonlinear dynamics and evolutionary economics. Oxford University Press, Oxford, pp 91–100Google Scholar
  54. Witt U (1997) Self-organization and economics—what is new? Struct Chang Econ Dyn 8:489–507CrossRefGoogle Scholar
  55. Witt U (1999) Bioeconomics as economics from a Darwinian perspective. J Bio-Econ 1:19–34Google Scholar
  56. Witt U (2004) On the proper interpretation of ‘evolution’ in economics and its implications for production theory. J Econ Methodol 11:125–146CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Section Environmental and Behavioural EconomicsUniversity of KasselKasselGermany

Personalised recommendations