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The computational complexity of rationalizing boundedly rational choice behavior

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  • Demuynck, Thomas

Abstract

We determine the computational complexity of various choice models that use multiple rationales to explain observed choice behavior. First, we demonstrate that the notion of rationalizability by K rationales, introduced by Kalai et al. (2002), is NP-complete for K greater than or equal to two. Then, we show that the question of sequential rationalizability by K rationales, introduced by Manzini and Mariotti (2007), is NP-complete for K greater than or equal to three. Finally, we focus on the computational complexity of two models that refine this model of sequential choice behavior. We establish that the model of choice by game trees, from Xu and Zhou (2007), is NP-complete while the status-quo bias model, from Masatlioglu and Ok (2005), can be verified in polynomial time.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Mathematical Economics.

Volume (Year): 47 (2011)
Issue (Month): 4-5 ()
Pages: 425-433

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Handle: RePEc:eee:mateco:v:47:y:2011:i:4:p:425-433

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Web page: http://www.elsevier.com/locate/jmateco

Related research

Keywords: Boundedly rational choice; Rationalization by multiple rationales; Sequential rationalization; Rationalization by game trees; Status-quo bias; Computational complexity; NP-completeness;

References

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  1. Eike B. Kroll & Bodo Vogt, 2008. "The Relevance of Irrelevant Alternatives: An experimental investigation of risky choices," FEMM Working Papers 08028, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
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  24. Xu, Yongsheng & Zhou, Lin, 2007. "Rationalizability of choice functions by game trees," Journal of Economic Theory, Elsevier, vol. 134(1), pages 548-556, May.
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Cited by:
  1. Thomas Demuynck, 2014. "The computational complexity of rationalizing Pareto optimal choice behavior," Social Choice and Welfare, Springer, vol. 42(3), pages 529-549, March.

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