Flexible level-1 consensus ensuring stable social choice: analysis and algorithms

Mor Nitzan, Shmuel Nitzan, Erel Segal-Halevi

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Level-1 consensus is a recently-introduced property of a preference-profile. Intuitively, it means that there exists a preference relation which induces an ordering of all other preferences such that frequent preferences are those that are more similar to it. This is a desirable property, since it enhances the stability of social choice by guaranteeing that there exists a Condorcet winner and it is elected by all scoring rules. In this paper, we present an algorithm for checking whether a given preference profile exhibits level-1 consensus. We apply this algorithm to a large number of preference profiles, both real and randomly-generated, and find that level-1 consensus is very improbable. We support these empirical findings theoretically, by showing that, under the impartial culture assumption, the probability of level-1 consensus approaches zero when the number of individuals approaches infinity. Motivated by these observations, we show that the level-1 consensus property can be weakened while retaining its stability implications. We call this weaker property Flexible Consensus. We show, both empirically and theoretically, that it is considerably more probable than the original level-1 consensus. In particular, under the impartial culture assumption, the probability for Flexible Consensus converges to a positive number when the number of individuals approaches infinity.

Original languageEnglish
Pages (from-to)457-479
Number of pages23
JournalSocial Choice and Welfare
Volume50
Issue number3
DOIs
StatePublished - 1 Mar 2018

Bibliographical note

Funding Information:
discussions. We are thankful to several users of the http://math.stackexchange.com website, in particular Hernan J. Gonzalez, Vineel Kumar Reddy Kovvuri, spaceisdarkgreen and zhw., for their kind help in coping with mathematical issues. We are thankful to I. Nitzan who made this collaboration possible. M.N. is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. E.S. is grateful to the Israeli Science Foundation for the ISF Grant 1083/13.

Publisher Copyright:
© 2017, Springer-Verlag GmbH Germany.

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