Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare

Tyler Lu, Pingzhong Tang, Ariel D. Procaccia, Craig Boutilier
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:541-551, 2012.

Abstract

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).

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-lu12a, title = {{B}ayesian Vote Manipulation: Optimal Strategies and Impact on Welfare}, author = {Lu, Tyler and Tang, Pingzhong and Procaccia, Ariel D. and Boutilier, Craig}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {541--551}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/lu12a/lu12a.pdf}, url = {https://proceedings.mlr.press/r10/lu12a.html}, abstract = {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).}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare %A Tyler Lu %A Pingzhong Tang %A Ariel D. Procaccia %A Craig Boutilier %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-lu12a %I PMLR %P 541--551 %U https://proceedings.mlr.press/r10/lu12a.html %V R10 %X 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). %Z Reissued by PMLR on 04 October 2026.
APA
Lu, T., Tang, P., Procaccia, A.D. & Boutilier, C.. (2012). Bayesian Vote Manipulation: Optimal Strategies and Impact on Welfare. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:541-551 Available from https://proceedings.mlr.press/r10/lu12a.html. Reissued by PMLR on 04 October 2026.

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