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Evaluating Case-based Decision Theory: Predicting Empirical Patterns of Human Classification Learning (Extensions)

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  • Pape, Andreas
  • Kurtz, Kenneth
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    Abstract

    We introduce a computer program which calculates an agent’s optimal behavior according to Case-based Decision Theory (Gilboa and Schmeidler, 1995) and use it to test CBDT against a benchmark set of problems from the psychological literature on human classification learning (Shepard et al., 1961). This allows us to evaluate the efficacy of CBDT as an account of human decision-making on this set of problems. We find: (1) The choice behavior of this program (and therefore Case-based Decision Theory) correctly predicts the empirically observed relative difficulty of problems and speed of learning in human data. (2) ‘Similarity’ (how CBDT decision makers extrapolate from memory) is decreasing in vector distance, consistent with evidence in psychology (Shepard, 1987). (3) The best-fitting parameters suggest humans aspire to an 80 − 85% success rate, and humans may increase their aspiration level during the experiment. (4) Average similarity is rejected in favor of additive similarity.

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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 45206.

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    Date of creation: 18 Mar 2013
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    Handle: RePEc:pra:mprapa:45206

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    Keywords: Case-based Decision Theory; Human Cognition; Learning; Agent-based Computational Economics; Psychology; Cognitive Science;

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    1. Gilboa, Itzhak & Schmeidler, David, 1995. "Case-Based Decision Theory," The Quarterly Journal of Economics, MIT Press, MIT Press, vol. 110(3), pages 605-39, August.
    2. Laibson, David I., 1997. "Golden Eggs and Hyperbolic Discounting," Scholarly Articles 4481499, Harvard University Department of Economics.
    3. Gilboa, Itzhak & Schmeidler, David, 1996. "Case-Based Optimization," Games and Economic Behavior, Elsevier, Elsevier, vol. 15(1), pages 1-26, July.
    4. Fudenberg, Drew & Levine, David, 2006. "A Dual-Self Model of Impulse Control," Scholarly Articles 3196335, Harvard University Department of Economics.
    5. Billot, Antoine & Gilboa, Itzhak & Schmeidler, David, 2008. "Axiomatization of an exponential similarity function," Mathematical Social Sciences, Elsevier, Elsevier, vol. 55(2), pages 107-115, March.
    6. Golosnoy, Vasyl & Okhrin, Yarema, 2008. "General uncertainty in portfolio selection: A case-based decision approach," Journal of Economic Behavior & Organization, Elsevier, Elsevier, vol. 67(3-4), pages 718-734, September.
    7. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, Elsevier, vol. 8(1), pages 164-212.
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