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Inferring Strategies from Observed Actions: A Nonparametric, Binary Tree Classification Approach

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Author Info
Jim Engle-Warnick () (Nuffield College, Oxford)

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Abstract

This paper introduces a non-parametric binary classification tree approach to inferring unobserved strategies from the observed actions of economic agents. The strategies are in the form of possibly nested if-then statements. We apply our approach to experimental data from the repeated ultimatum game, which was conducted in four different countries by Roth et al. (1991). We find that strategy inference is consistent with existing inference, provides new explanations for subject behavior, and provides new empirically-based hypotheses regarding ultimatum game strategies. We conclude that strategy inference is potentially useful as a complementary method of statistical inference in applied research.

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File URL: http://www.nuff.ox.ac.uk/Economics/papers/2001/w14/bintree1gen.pdf
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Publisher Info
Paper provided by Economics Group, Nuffield College, University of Oxford in its series Economics Papers with number 2001-W14.

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Length: 27 pages
Date of creation: 01 Aug 2001
Date of revision:
Handle: RePEc:nuf:econwp:0114

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Web page: http://www.nuff.ox.ac.uk/economics/

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Related research
Keywords: binary tree classifer strategy bargaining nonparametric resampling experimental economics

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  1. Jim Engle-Warnick & Bradley Ruffle, 2006. "The Strategies Behind Their Actions: A Method To Infer Repeated-Game Strategies And An Application To Buyer Behavior," Departmental Working Papers 2005-04, McGill University, Department of Economics. [Downloadable!]
  2. Daniel Houser & Michael Keane & Kevin McCabe, 2002. "Behavior in a dynamic decision problem: An analysis of experimental evidence using a bayesian type classification algorithm," Experimental 0211001, EconWPA. [Downloadable!]
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