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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

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.

Suggested Citation

  • Clintin P. Davis-Stober & Nicholas Brown, 2011. "A shift in strategy or "error"? Strategy classification over multiple stochastic specifications," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 800-813, December.
  • Handle: RePEc:jdm:journl:v:6:y:2011:i:8:p:800-813
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    References listed on IDEAS

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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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