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Can Genetic Algorithms Explain Experimental Anomalies?

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  • Marco Casari

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Abstract

In experimental data, it is common to find persistent oscillations in the aggregate outcomes and high levels of heterogeneity in individual behavior. Furthermore, it is not unusual to find significant deviations from aggregate Nash equilibrium predictions. In this paper, we employ an evolutionary model with boundedly rational agents to explain these findings. We use data from common property resource experiments (Casari and Plott, 2003). Instead of positing individual-specific utility functions, we model decision makers as selfish and identical. Agent interaction is simulated using an individual learning genetic algorithm (GA), where agents have constraints in their working memory, a limited ability to maximize, and experiment with new strategies. We show that the model replicates most of the patterns that can be found in common property resource experiments. Copyright Kluwer Academic Publishers 2004

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Bibliographic Info

Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 24 (2004)
Issue (Month): 3 (March)
Pages: 257-275

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Handle: RePEc:kap:compec:v:24:y:2004:i:3:p:257-275

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Web page: http://www.springerlink.com/link.asp?id=100248
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Related research

Keywords: bounded rationality; common-pool resources; experiments; genetic algorithms;

References

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Cited by:
  1. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
  2. Hommes, Cars & Lux, Thomas, 2013. "Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments," Macroeconomic Dynamics, Cambridge University Press, vol. 17(02), pages 373-401, March.
  3. Kirill Chernomaz, 2014. "Adaptive learning in an asymmetric auction: genetic algorithm approach," Journal of Economic Interaction and Coordination, Springer, vol. 9(1), pages 27-51, April.

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