On the convergence of iterative voting: how restrictive should restricted dynamics be?

Svetlana Obraztsova, Evangelos Markakis, Maria Polukarov, Zinovi Rabinovich, Nicholas R. Jennings

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

28 Citations (Scopus)


We study convergence properties of iterative voting procedures. Such procedures are defined by a voting rule and a (restricted) iterative process, where at each step one agent can modify his vote towards a better outcome for himself. It is already known that if the iteration dynamics (the manner in which voters are allowed to modify their votes) are unrestricted, then the voting process may not converge. For most common voting rules this may be observed even under the best response dynamics limitation. It is therefore important to investigate whether and which natural restrictions on the dynamics of iterative voting procedures can guarantee convergence. To this end, we provide two general conditions on the dynamics based on iterative myopic improvements, each of which is sufficient for convergence. We then identify several classes of voting rules (including Positional Scoring Rules, Maximin, Copeland and Bucklin), along with their corresponding iterative processes, for which at least one of these conditions hold.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
Number of pages7
Publication statusPublished - 2015


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