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

In this paper, we study a maximum likelihood estimation (MLE) approach to preference aggregation and voting when the set of alternatives has a multi-issue structure, and the voters' preferences are represented by CP-nets. We first 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 show that computing the winner for the corresponding MLE voting rule is NP-hard when the threshold is 1, but can be done in polynomial time when the threshold is equal to the number of issues. Next, we 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-byissue voting rules in which each local rule is itself an MLE (resp. a ranking scoring rule...

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Lirong Xia, Vincent Conitzer, Jérôme Lang
• Keywords: ATAL 2010, Distance-based Noise Models, Intelligent Agents, Multi-issue Domains, Voting Rule