Meaningful Learning in Weighted Voting Games: An Experiment

Eric Guerci (1) , Nobuyuki Hanaki (1) , Naoki Watanabe (2)

1 GREDEG - Groupe de Recherche en Droit, Economie et Gestion
2 Faculty of Engineering, Information and Systems [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.

Mots clés: experiment; voting game; learning; two-armed bandit problem

Domaines: Economies et finances

Eric Guerci, Nobuyuki Hanaki, Naoki Watanabe. Meaningful Learning in Weighted Voting Games: An Experiment. Theory and Decision, 2017, 83 (10), pp.131-153. ⟨10.1007/s11238-017-9588-x⟩. ⟨halshs-01216244⟩