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Meaningful Learning in Weighted Voting Games: An Experiment

Listed author(s):
  • Eric Guerci

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS - UNS - Université Nice Sophia Antipolis)

  • Nobuyuki Hanaki

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - CNRS - UNS - Université Nice Sophia Antipolis)

  • Naoki Watanabe

    ()

    (Faculty of Engineering, Information and Systems, University of Tsukuba - University of Tsukuba)

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.

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Paper provided by HAL in its series Working Papers with number halshs-01216244.

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Date of creation: 15 Oct 2015
Handle: RePEc:hal:wpaper:halshs-01216244
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01216244
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