Meaningful learning in weighted voting games: an experiment

Eric Guerci, Nobuyuki Hanaki, Naoki Watanabe

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)131-153
Number of pages23
JournalTheory and Decision
Volume83
Issue number1
DOIs
Publication statusPublished - 2017 Jun 1

Keywords

  • Experiment
  • Learning
  • Two-armed bandit problem
  • Voting game

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Applied Psychology
  • Social Sciences(all)
  • Economics, Econometrics and Finance(all)
  • Computer Science Applications

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