Meaningful learning in weighted voting games: an experiment
Eric Guerci (),
Nobuyuki Hanaki () and
Naoki Watanabe ()
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Naoki Watanabe: Keio University
Theory and Decision, 2017, vol. 83, issue 1, 131-153
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; (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; (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: Learning; Voting game; Experiment; Two-armed bandit problem (search for similar items in EconPapers)
JEL-codes: C79 C92 D72 D83 (search for similar items in EconPapers)
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Working Paper: Meaningful Learning in Weighted Voting Games: An Experiment (2017)
Working Paper: Meaningful learning in weighted voting games: an experiment (2017)
Working Paper: Meaningful Learning in Weighted Voting Games: An Experiment (2015)
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