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In many real world situations, collective decisions are made using votingand, in scenarios such as committee or board elections, employing voting rulesthat return multiple winners. In multi-winner approval voting (AV), an agentsubmits a ballot consisting of approvals for as many candidates as they wish,and winners are chosen by tallying up the votes and choosing the top-$k$candidates receiving the most approvals. In many scenarios, an agent maymanipulate the ballot they submit in order to achieve a better outcome byvoting in a way that does not reflect their true preferences. In complex anduncertain situations, agents may use heuristics instead of incurring theadditional effort required to compute the manipulation which most favors them.In this paper, we examine voting behavior in single-winner and multi-winnerapproval voting scenarios with varying degrees of uncertainty using behavioraldata obtained from Mechanical Turk. We find that people generally manipulatetheir vote to obtain a better outcome, but often do not identify the optimalmanipulation. There are a number of predictive models of agent behavior in theCOMSOC and psychology literature that are based on cognitively plausibleheuristic strategies. We show that the existing approaches do not adequatelymodel real-world data. We propose a novel model that takes into account thesize of the winning set and human cognitive constraints, and demonstrate thatthis model is more effective at capturing real-world behaviors in multi-winnerapproval voting scenarios.

Modeling Voters in Multi-Winner Approval Voting