Electoral Outcomes and Social Log-Likelihood Maxima

Abstract

This paper is concerned with the public policies that occur in economies with elections when political candidates estimate voting behavior with log-concave probabilistic voting estimators (e.g., normal estimators). We establish that, for a vector of policies to be the outcome of an election, it is both necessary and sufficient that these policies maximize the society's mean (or social) log-likelihood function. This implies: First, the set of possible electoral outcomes is convex. Second, there is an electoral equilibrium whenever the set of social alternatives is compact. This property which holds for all multi-dimensional policy spaces does not use any special symmetry requirements on voter preferences. Third, under 'cardinal probabilistic voting,' every electoral outcome is also a maximum of a Nash type Social Welfare function. Fourth, in a finite population of m voters with independent probabilistic voting density functions a vector of policies is an electoral outcome if any only if it has the maximum estimated likelihood of receiving unanimous support.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1979
Accession Number
ADA081524

Entities

People

  • Peter Coughlin
  • Shmuel Nitzan

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Competition
  • Elections
  • Estimators
  • Mathematics
  • Military Research
  • Point Theorem
  • Political Science
  • Probability
  • Public Policy
  • Real Numbers
  • Social Sciences
  • Social Welfare
  • United States
  • Universities

Fields of Study

  • Economics

Readers

  • Economics
  • Educational Psychology
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space