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Most analyses of manipulation of voting schemes have adopted two assumptionsthat greatly diminish their practical import. First, it is usually assumed thatthe manipulators have full knowledge of the votes of the nonmanipulatingagents. Second, analysis tends to focus on the probability of manipulationrather than its impact on the social choice objective (e.g., social welfare).We relax both of these assumptions by analyzing optimal Bayesian manipulationstrategies when the manipulators have only partial probabilistic informationabout nonmanipulator votes, and assessing the expected loss in social welfare(in the broad sense of the term). We present a general optimization frameworkfor the derivation of optimal manipulation strategies given arbitrary votingrules and distributions over preferences. We theoretically and empiricallyanalyze the optimal manipulability of some popular voting rules usingdistributions and real data sets that go well beyond the common, butunrealistic, impartial culture assumption. We also shed light on the starkdifference between the loss in social welfare and the probability ofmanipulation by showing that even when manipulation is likely, impact to socialwelfare is slight (and often negligible).

Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare