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Genetic Algorithm Learning To Choose And Use Information

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  • Routledge, Bryan R.

Abstract

A genetic algorithm (GA) is used to model learning in a financial model similar to the Grossman Stiglitz model. Individuals need to learn how to use a signal, how to make an inference about a signal from a market-clearing price, and whether or not a signal is worth acquiring. We provide examples in which the GA does and does not converge to the rational expectations equilibrium. Similar to earlier results, the behavior depends heavily on the rate of experimentation or mutation in the GA and the size of the risky-asset supply noise in the economy.

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Bibliographic Info

Article provided by Cambridge University Press in its journal Macroeconomic Dynamics.

Volume (Year): 5 (2001)
Issue (Month): 02 (April)
Pages: 303-325

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Handle: RePEc:cup:macdyn:v:5:y:2001:i:02:p:303-325_01

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Cited by:
  1. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
  2. Thomas H. Noe & Michael J. Rebello & Jun Wang, 2006. "The Evolution of Security Designs," Journal of Finance, American Finance Association, vol. 61(5), pages 2103-2135, October.
  3. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233 Elsevier.
  4. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.
  5. Brennan, Michael J & Li, Feifei & Torous, Walt, 2005. "Dollar Cost Averaging," University of California at Los Angeles, Anderson Graduate School of Management qt53p0r65q, Anderson Graduate School of Management, UCLA.
  6. Colin Fyfe & John Paul Marney & Heather Tarbert, 2005. "Risk adjusted returns from technical trading: a genetic programming approach," Applied Financial Economics, Taylor & Francis Journals, vol. 15(15), pages 1073-1077.
  7. Noe, Thomas H. & Rebello, Michael & Wang, Jun, 2012. "Learning to bid: The design of auctions under uncertainty and adaptation," Games and Economic Behavior, Elsevier, vol. 74(2), pages 620-636.

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