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

Jim Engle-Warnick ()

Econometrics from University Library of Munich, Germany

Abstract: This paper introduces a nonparametric 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.

Keywords: binary tree; classifier; strategy; bargaining; nonparamtric; resampling; experimental economics (search for similar items in EconPapers)
JEL-codes: C14 C51 C63 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2000-07-20, Revised 2001-08-02
Note: Type of Document - pdf; prepared on pc; to print on any; pages: 27; figures: included
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Inferring strategies from observed actions: a nonparametric, binary tree classification approach (2003)
Working Paper: Inferring Strategies from Observed Actions: A Nonparametric, Binary Tree Classification Approach (2001)
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Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpem:0004002

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