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Meaningful Learning in Weighted Voting Games: An Experiment

Eric Guerci (), Nobuyuki Hanaki () and Naoki Watanabe ()
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Naoki Watanabe: Faculty of Engineering, Information and Systems [Tsukuba] - University of Tsukuba

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Abstract: By employing binary committee choice problems, this paper investigates how varying or eliminating feedback about payoffs affects: (1) subjects' learning about the underlying relationship between their nominal voting weights and their expected payoffs in weighted voting games; and (2) the transfer of acquired learning from one committee choice problem to a similar but different problem. In the experiment, subjects choose to join one of two committees (weighted voting games) and obtain a payoff stochastically determined by a voting theory. We found that: (i) subjects learned to choose the committee that generates a higher expected payoff even without feedback about the payoffs they received; and (ii) there was statistically significant evidence of ``meaningful learning'' (transfer of learning) only for the treatment with no payoff-related feedback. This finding calls for re-thinking existing models of learning to incorporate some type of introspection.

Keywords: experiment; voting game; learning; two-armed bandit problem (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cdm, nep-exp, nep-gth and nep-pol
Date: 2017-02-02
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Published in Theory and Decision, Springer Verlag, 2017, 83 (10), pp.131-153. ⟨10.1007/s11238-017-9588-x⟩

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Journal Article: Meaningful learning in weighted voting games: an experiment (2017) Downloads
Working Paper: Meaningful learning in weighted voting games: an experiment (2017)
Working Paper: Meaningful Learning in Weighted Voting Games: An Experiment (2015) Downloads
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