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A shift in strategy or "error"? Strategy classification over multiple stochastic specifications

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  • Clintin P. Davis-Stober
  • Nicholas Brown
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    Abstract

    We present a classification methodology that jointly assigns to a decision maker a best-fitting decision strategy for a set of choice data as well as a best-fitting stochastic specification of that decision strategy. Our methodology utilizes normalized maximum likelihood as a model selection criterion to compare multiple, possibly non-nested, stochastic specifications of candidate strategies. In addition to single strategy with ``error'' stochastic specifications, we consider mixture specifications, i.e., strategies comprised of a probability distribution over multiple strategies. In this way, our approach generalizes the classification framework of Broder and Schiffer (2003a). We apply our methodology to an existing dataset and find that some decision makers are best fit by a single strategy with varying levels of error, while others are best described as using a mixture specification over multiple strategies.

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    Bibliographic Info

    Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

    Volume (Year): 6 (2011)
    Issue (Month): 8 (December)
    Pages: 800-813

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    Handle: RePEc:jdm:journl:v:6:y:2011:i:8:p:800-813

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    Related research

    Keywords: mixture models; strategy classification; comparative model fit; normalized maximum likelihood; error models; stochastic specification.;

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    1. Ballinger, T Parker & Wilcox, Nathaniel T, 1997. "Decisions, Error and Heterogeneity," Economic Journal, Royal Economic Society, vol. 107(443), pages 1090-1105, July.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-33, March.
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    9. Klaus Fiedler, 2010. "How to study cognitive decision algorithms: The case of the priority heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(1), pages 21-32, February.
    10. Loomes, Graham & Moffatt, Peter G & Sugden, Robert, 2002. " A Microeconometric Test of Alternative Stochastic Theories of Risky Choice," Journal of Risk and Uncertainty, Springer, vol. 24(2), pages 103-30, March.
    11. Ulf Böckenholt, 2006. "Thurstonian-Based Analyses: Past, Present, and Future Utilities," Psychometrika, Springer, vol. 71(4), pages 615-629, December.
    12. Carbone, Enrica & Hey, John D, 2000. " Which Error Story Is Best?," Journal of Risk and Uncertainty, Springer, vol. 20(2), pages 161-76, March.
    13. Hey, John D & Orme, Chris, 1994. "Investigating Generalizations of Expected Utility Theory Using Experimental Data," Econometrica, Econometric Society, vol. 62(6), pages 1291-1326, November.
    14. J�rg Rieskamp & Jerome R. Busemeyer & Barbara A. Mellers, 2006. "Extending the Bounds of Rationality: Evidence and Theories of Preferential Choice," Journal of Economic Literature, American Economic Association, vol. 44(3), pages 631-661, September.
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    Cited by:
    1. Benjamin E. Hilbig, 2014. "On the role of recognition in consumer choice: A model comparison," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 9(1), pages 51-57, January.

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