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Can Genetic Algorithms Explain Experimental Anomalies?

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  • Marco Casari

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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  • Marco Casari, 2004. "Can Genetic Algorithms Explain Experimental Anomalies?," Computational Economics, Springer;Society for Computational Economics, vol. 24(3), pages 257-275, March.
    • Handle: RePEc:kap:compec:v:24:y:2004:i:3:p:257-275
    DOI: 10.1007/s10614-004-4197-5
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    Cited by:

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    2. Hommes, Cars & Lux, Thomas, 2013. "Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments," Macroeconomic Dynamics, Cambridge University Press, vol. 17(2), pages 373-401, March.
    3. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    4. William Tracy, 2014. "Paradox Lost: The Evolution of Strategies in Selten’s Chain Store Game," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 83-103, January.
    5. Sylvie Geisendorf, 2018. "Evolutionary Climate-Change Modelling: A Multi-Agent Climate-Economic Model," Computational Economics, Springer;Society for Computational Economics, vol. 52(3), pages 921-951, October.
    6. Kirill Chernomaz, 2014. "Adaptive learning in an asymmetric auction: genetic algorithm approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 9(1), pages 27-51, April.
    7. Sylvie Geisendorf, 2011. "Internal selection and market selection in economic Genetic Algorithms," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 817-841, December.
    8. Shu-Heng Chan & Shu G. Wang, 2010. "Emergent Complexity in Agent-Based Computational Economics," ASSRU Discussion Papers 1017, ASSRU - Algorithmic Social Science Research Unit.

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