Heuristics in Multi-Winner Approval Voting

TL;DR

This paper investigates how voters use heuristics rather than optimal strategies in multi-winner approval voting, showing that voters often prioritize high-utility candidates and that heuristics can be effective with partial information.

Contribution

It introduces an analysis of heuristic voting behaviors in multi-winner approval voting and demonstrates their effectiveness through simulations with partial information.

Findings

Voters tend to manipulate votes to improve outcomes.

Most voters do not identify the optimal manipulation.

Heuristics based on candidate utility are effective in partial information settings.

Abstract

In many real world situations, collective decisions are made using voting. Moreover, scenarios such as committee or board elections require voting rules that return multiple winners. In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish. Winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most votes. An agent may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics to strategize, instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in multi-winner approval voting scenarios with complete information. We show that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. Instead, voters tend to prioritize the candidates with the highest utilities. Using simulations, we demonstrate the effectiveness of these heuristics in situations where agents only have access to partial information.