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Predicting majority rule: Evaluating the uncovered set and the strong point

Author

Listed:
  • Jacob Bower-Bir

    (Department of Political Science, Indiana University, USA; Workshop in Political Theory and Policy Analysis, Indiana University, USA)

  • William Bianco

    (Department of Political Science, Indiana University, USA)

  • Nicholas D’Amico

    (Department of Political Science, Indiana University, USA)

  • Christopher Kam

    (Department of Political Science, University of British Columbia, Canada)

  • Itai Sened

    (Department of Political Science, Washington University – St. Louis, USA)

  • Regina Smyth

    (Department of Political Science, Indiana University, USA)

Abstract

This paper compares two solution concepts for majority rule decision-making in multi-dimensional settings: the uncovered set and the strong point. Our goal is to determine which of these solution concepts is the appropriate generalization of the median voter theorem to more complex (and more realistic) multi-dimensional majority-rule settings. By making this comparison, we also contribute to the debate about the degree of sophisticated decision-making exhibited by experimental subjects and their real-world counterparts. Using data from eleven previously-published majority rule experiments and analytic techniques drawn from geography, our analysis confirms expectations that the uncovered set provides accurate predictions of majority-rule decision-making; and, moreover, that the strong point provides little added insight, either as a solution concept on its own, or as a predictor of where outcomes lie inside the uncovered set.

Suggested Citation

  • Jacob Bower-Bir & William Bianco & Nicholas D’Amico & Christopher Kam & Itai Sened & Regina Smyth, 2015. "Predicting majority rule: Evaluating the uncovered set and the strong point," Journal of Theoretical Politics, , vol. 27(4), pages 650-672, October.
    • Handle: RePEc:sae:jothpo:v:27:y:2015:i:4:p:650-672
    DOI: 10.1177/0951629814562289
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    References listed on IDEAS

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