Aggregating preferences in multi-issue domains by using maximum likelihood estimators

Conference Paper

In this paper, we study a maximum likelihood estimation (MLE) approach to voting when the set of alternatives has a multi-issue structure, and the voters' preferences are represented by CP-nets. We first consider general multi-issue domains, and study whether and how issue-by-issue voting rules and sequential voting rules can be represented by MLEs. We first show that issue-by-issue voting rules in which each local rule is itself an MLE (resp. a candidate scoring rule) can be represented by MLEs with a weak (resp. strong) decomposability property. Then, we prove two theorems that state that if the noise model satisfies a very weak decomposability property, then no sequential voting rule that satisfies unanimity can be represented by an MLE, unless the number of voters is bounded. We then consider multi-issue domains in which each issue is binary; for these, we propose a general family of distance-based noise models, of which give an axiomatic characterization. We then propose a more specific family of natural distance-based noise models that are parameterized by a threshold. We identify the complexity of winner determination for the corresponding MLE voting rule in the two most important subcases of this framework. Copyright © 2010, International Foundation for Autonomous Agents and Multiagent Systems ( All rights reserved.

Duke Authors

Cited Authors

  • Xia, L; Conitzer, V; Lang, J

Published Date

  • January 1, 2010

Published In

Volume / Issue

  • 1 /

Start / End Page

  • 399 - 406

Electronic International Standard Serial Number (EISSN)

  • 1558-2914

International Standard Serial Number (ISSN)

  • 1548-8403

International Standard Book Number 13 (ISBN-13)

  • 9781617387715

Citation Source

  • Scopus