On the role of distances in defining voting rules

A voting rule is an algorithm for determining the winner in an election, and there are several approaches that have been used to justify the proposed rules. One justification is to show that a rule satisfies a set of desirable axioms that uniquely identify it. Another is to show that the calculation that it performs is actually maximum likelihood estimation relative to a certain model of noise that affects voters (MLE approach). The third approach, which has been recently actively investigated, is the so-called distance rationalizability framework. In it, a voting rule is defined via a class of consensus elections (i.e., a class of elections that have a clear winner) and a distance function. A candidate c is a winner of an election E if c wins in one of the consensus elections that are closest to E relative to the given distance. In this paper, we show that essentially any voting rule is distance-rationalizable if we do not restrict the two ingredients of the rule: the consensus class...

• Type: Conference
• Year: 2010
• Where: ATAL
• Authors: Edith Elkind, Piotr Faliszewski, Arkadii M. Slinko
• Keywords: ATAL 2010, Consensus Elections, Distance Rationalizability, Intelligent Agents, Voting Rule