Evaluating Resistance to False-Name Manipulations in Elections - Waggoner, Bo; Xia, Lirong; Conitzer, Vincent (Proceedings of the AAAI Conference on Artificial Intelligence, 2021)
In many mechanisms (especially online mechanisms), a strategic agent can inﬂuence the outcome by creating multiple false identities. We consider voting settings where the mechanism designer cannot completely prevent false-name manipulation, but may use false-name-limiting methods such as CAPTCHAs to inﬂuence the amount and characteristics of such manipulation. Such a designer would prefer, ﬁrst, a high probability of obtaining the “correct” outcome, and second, a statistical method for evaluating the correctness of the outcome. In this paper, we focus on settings with two alternatives. We model voters as independently drawing a number of identities from a distribution that may be inﬂuenced by the choice of the false-name-limiting method. We give a criterion for the evaluation and comparison of these distributions. Then, given the results of an election in which false-name manipulation may have occurred, we propose and justify a statistical test for evaluating the outcome.