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Evaluating generalizability and parameter consistency in learning models

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  • Yechiam, Eldad
  • Busemeyer, Jerome R.
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    Abstract

    A new evaluation method is proposed for comparing learning models used for predicting decisions based on experience. The method is based on the generalization of models' predictions at the individual level. First, it evaluates the ability to make a priori predictions for decisions in new tasks using parameters from different tasks performed by an individual decision-maker. Second, it evaluates the consistency of parameters estimated in different tasks performed by the same person. We use this method to examine two rules for updating past experience with payoff feedback: The Delta rule, where only the chosen option is updated; and a Decay-Reinforcement rule, where additionally, non-chosen options are discounted. The results reveal that although the Decay-Reinforcement rule fits the data better, it has poor generality and parameter consistency at the individual level. The current method thus improves the ability to select models based on their correspondence to consistent characteristics within individual decision-makers.

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    File URL: http://www.sciencedirect.com/science/article/B6WFW-4R2RMX2-1/1/8772a6f5776329d5ad5527f399b139ce
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    Bibliographic Info

    Article provided by Elsevier in its journal Games and Economic Behavior.

    Volume (Year): 63 (2008)
    Issue (Month): 1 (May)
    Pages: 370-394

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    Handle: RePEc:eee:gamebe:v:63:y:2008:i:1:p:370-394

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    Web page: http://www.elsevier.com/locate/inca/622836

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    1. Sarin, R. & Vahid, F., 1999. "Predicting how People Play Games: a Simple Dynamic Model of Choice," Monash Econometrics and Business Statistics Working Papers 12/99, Monash University, Department of Econometrics and Business Statistics.
    2. Erev, Ido & Roth, Alvin E, 1998. "Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria," American Economic Review, American Economic Association, vol. 88(4), pages 848-81, September.
    3. Amos Tversky & Daniel Kahneman, 1979. "Prospect Theory: An Analysis of Decision under Risk," Levine's Working Paper Archive 7656, David K. Levine.
    4. Fudenberg, Drew & Levine, David K., 1995. "Consistency and cautious fictitious play," Journal of Economic Dynamics and Control, Elsevier, vol. 19(5-7), pages 1065-1089.
    5. Thaler, Richard H, et al, 1997. "The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test," The Quarterly Journal of Economics, MIT Press, vol. 112(2), pages 647-61, May.
    6. Cheung, Yin-Wong & Friedman, Daniel, 1997. "Individual Learning in Normal Form Games: Some Laboratory Results," Games and Economic Behavior, Elsevier, vol. 19(1), pages 46-76, April.
    7. Erev, Ido & Bereby-Meyer, Yoella & Roth, Alvin E., 1999. "The effect of adding a constant to all payoffs: experimental investigation, and implications for reinforcement learning models," Journal of Economic Behavior & Organization, Elsevier, vol. 39(1), pages 111-128, May.
    8. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    9. Stahl, Dale O., 1996. "Boundedly Rational Rule Learning in a Guessing Game," Games and Economic Behavior, Elsevier, vol. 16(2), pages 303-330, October.
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    Cited by:
    1. Larson, Nathan & Elmaghraby, Wedad, 2008. "Procurement auctions with avoidable fixed costs: an experimental approach," MPRA Paper 32163, University Library of Munich, Germany, revised 2011.
    2. Luke Lindsay, 2011. "Correlated Individual Differences and Choice Prediction," Games, MDPI, Open Access Journal, vol. 2(1), pages 16-20, February.
    3. Andrey Kudryavtsev & Julia Pavlodsky, 2012. "Description-based and experience-based decisions: individual analysis," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(3), pages 316-331, May.

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