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Public Choice

, Volume 174, Issue 3–4, pp 219–238 | Cite as

The likelihood of social choice violations in rank sum scoring: algorithms and evidence from NCAA cross country running

  • James Boudreau
  • Justin Ehrlich
  • Mian Farrukh Raza
  • Shane Sanders
Article

Abstract

Recent contributions have used combinatorial algorithms to determine the likelihood of particular social choice violations in rank sum scoring. Given the broad importance of rank sum scoring (e.g., in non-parametric statistical testing, sporting competition, and mathematical competition), it is important to establish the level of ambiguity generated by this aggregation rule. Combinatorial likelihoods are naïve, however, in that they assume each possible outcome sequence for an event to be equally likely. We develop a computational algorithm to extend upon previous combinatorial results as to the likelihood of a violation of transitivity or independence in rank sum scoring. We use a similar computational scoring approach to analyze the empirically-observed likelihood of each such violation across fourteen NCAA Cross Country Championships. Within the data, rank sum scoring fails to specify a robust winning team (i.e., one that also rank sum wins against each possible subset of opponents) in 4 of 14 cases. Overall, we find that empirical likelihoods of social choice violations are consistently (significantly) overestimated by combinatorial expectations. In the NCAA data, we find correlated ability (quality) levels within team (group) and discuss this as a cause of lower empirical likelihoods. Combinatorial analysis proves reliable in predicting the order of empirical likelihoods across violation type and event setting.

Keywords

Rank sum scoring Collective decision making Borda count Social choice violations 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • James Boudreau
    • 1
  • Justin Ehrlich
    • 2
  • Mian Farrukh Raza
    • 3
  • Shane Sanders
    • 4
  1. 1.Department of Economics, Finance, and Quantitative Analysis, Coles College of BusinessKennesaw State UniversityKennesawUSA
  2. 2.Western Illinois UniversityMacombUSA
  3. 3.Kansas State UniversityManhattanUSA
  4. 4.Syracuse UniversitySyracuseUSA

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