Optimal social choice functions: A utilitarian view

Craig Boutilier, Ioannis Caragiannis, Simi Haber, Tyler Lu, Ariel D. Procaccia, Or Sheffet

Research output: Contribution to journalArticlepeer-review

111 Scopus citations

Abstract

Abstract We adopt a utilitarian perspective on social choice, assuming that agents have (possibly latent) utility functions over some space of alternatives. For many reasons one might consider mechanisms, or social choice functions, that only have access to the ordinal rankings of alternatives by the individual agents rather than their utility functions. In this context, one possible objective for a social choice function is the maximization of (expected) social welfare relative to the information contained in these rankings. We study such optimal social choice functions under three different models, and underscore the important role played by scoring functions. In our worst-case model, no assumptions are made about the underlying distribution and we analyze the worst-case distortion - or degree to which the selected alternative does not maximize social welfare - of optimal (randomized) social choice functions. In our average-case model, we derive optimal functions under neutral (or impartial culture) probabilistic models. Finally, a very general learning-theoretic model allows for the computation of optimal social choice functions (i.e., ones that maximize expected social welfare) under arbitrary, sampleable distributions. In the latter case, we provide both algorithms and sample complexity results for the class of scoring functions, and further validate the approach empirically.

Original languageEnglish
Article number2858
Pages (from-to)190-213
Number of pages24
JournalArtificial Intelligence
Volume227
DOIs
StatePublished - 11 Jul 2015

Bibliographical note

Publisher Copyright:
© 2015 Elsevier B.V.

Keywords

  • Computational social choice

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