Uncertainty in Artificial Intelligence
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Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare
Tyler Lu, Pingzhong Tang, Ariel Procaccia, Craig Boutilier
Most analyses of manipulation of voting schemes have adopted two assumptions that greatly diminish their practical import. First, it is usually assumed that the manipulators have full knowledge of the votes of the nonmanipulating agents. Second, analysis tends to focus on the probability of manipulation rather than its impact on the social choice objective (e.g., social welfare). We relax both of these assumptions by analyzing optimal Bayesian manipulation strategies when the manipulators have only partial probabilistic information about nonmanipulator votes, and assessing the expected loss in social welfare (in the broad sense of the term). We present a general optimization framework for the derivation of optimal manipulation strategies given arbitrary voting rules and distributions over preferences. We theoretically and empirically analyze the optimal manipulability of some popular voting rules using distributions and real data sets that go well beyond the common, but unrealistic, impartial culture assumption. We also shed light on the stark difference between the loss in social welfare and the probability of manipulation by showing that even when manipulation is likely, impact to social welfare is slight (and often negligible).
Pages: 543-553
PS Link:
PDF Link: /papers/12/p543-lu.pdf
AUTHOR = "Tyler Lu and Pingzhong Tang and Ariel Procaccia and Craig Boutilier",
TITLE = "Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "543--553"

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