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Collaborating Authors

 Kavner, Joshua


Average-Case Analysis of Iterative Voting

arXiv.org Artificial Intelligence

It is well-known in social choice that people may misreport their preferences to improve group decisions in their favor. Consider, for example, Alice, Bob, and Charlie deciding on which ice cream flavor to order for a party, and Charlie prefers strawberry to chocolate to vanilla. Given that Alice wants chocolate and Bob wants vanilla, Charlie would be better off voting for chocolate than truthfully (i.e., strawberry), by which vanilla may win as the tie-breaker. This form of strategic behavior is prolific in political science in narrowing the number of political parties (see e.g., Duvuger's law [Riker, 1982]). Still, it is unclear what effect strategic behavior has on the social welfare of chosen outcomes. Iterative voting (IV) is one model which naturally describes agents' strategic behavior - in misreporting their truthful preferences - over time. After agents reveal their preferences initially, they have the opportunity to repeatedly update their votes given information about other agents' votes, before the final decision is reached. Meir et al. [2010] first proposed iterative plurality voting and identified many sufficient conditions for IV to converge. This was followed up by a series of work examining various social choice rules, information and behavioral assumptions, and settings to determine when, to what outcomes, and how fast IV converges (see e.g.


Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare

arXiv.org Artificial Intelligence

Recent work in iterative voting has defined the additive dynamic price of anarchy (ADPoA) as the difference in social welfare between the truthful and worst-case equilibrium profiles resulting from repeated strategic manipulations. While iterative plurality has been shown to only return alternatives with at most one less initial votes than the truthful winner, it is less understood how agents' welfare changes in equilibrium. To this end, we differentiate agents' utility from their manipulation mechanism and determine iterative plurality's ADPoA in the worst- and average-cases. We first prove that the worst-case ADPoA is linear in the number of agents. To overcome this negative result, we study the average-case ADPoA and prove that equilibrium winners have a constant order welfare advantage over the truthful winner in expectation. Our positive results illustrate the prospect for social welfare to increase due to strategic manipulation.