Strategic voting in the lab: compromise and leader bias behavior

Abstract

Plurality voting is perhaps the most commonly used way to aggregate the preferences of multiple voters. Yet, there is no consensus on how people vote strategically, even in very simple settings. The purpose of this paper is to provide a comprehensive study of people's voting behavior in various online settings under the plurality rule. We implemented voting games that replicate two common real-world voting scenarios in controlled experiments. In the first, a single voter votes once after seeing a pre-election poll. In the second game, a group of voters play an iterative game, and change their vote as the game progresses (as in online voting). The winning candidate in each game (and hence the subject's payment) is determined using the plurality rule. For each of these settings we generated hundreds of game instances, varying conditions such as the number of voters, subjects' preferences over candidates and the poll information that was made available to the subjects prior to voting. We show that people can be classified into several groups, one of which is not engaged in any strategic behavior, while the largest group demonstrates both a tendency for strategic compromise, and a bias toward voting for the leader in the poll. We provide a detailed analysis of this group behavior for both settings, and how it depends on the poll information. Our study has insight for multi-agent system designers in uncovering patterns that provide reasonable predictions of voters' behaviors, which may facilitate the design of agents that support people or act autonomously in voting systems.

Voting behavior in one-shot and iterative multiple referenda

“…In Profiles 5 to 7, the static groups started with an election outcome with a higher social welfare than the one of the dynamic groups, who eventually improve it to a comparable level in the last stage of iteration. 16 Interestingly, these results contrast with the findings of Meir et al (2020) in online iterated elections, who do not find any statistically significant improvement in social welfare through iteration. This suggests that multi-issue elections are more likely to profit from an iterative voting scheme than classical plurality elections over a set of candidates.…”

“…Second, iterative voting methods can be designed as voting rules per se (Grandi et al, 2013;Obraztsova et al, 2015;Airiau et al, 2017). Third, iterative voting has been used as a tool to investigate strategic voting under uncertainty, with partial information coming either from an exogenous poll or by making the votes of the other voters visible in iterated elections (Meir et al, 2020). With very few exceptions, most results in iterative voting assume that only one individual at a time is allowed to change their vote.…”

“…We next look at two measures to evaluate the quality of the election outcomes, in line with previous work in iterative voting, namely, the average social welfare and the frequency of the election of Condorcet winners (Reijngoud and Endriss, 2012;Grandi et al, 2013;Wilczynski, 2019;Meir et al, 2020).…”

“…Most work on iterative voting consists of theoretical analyses of singlewinner iterative voting, such as convergence and efficiency guarantees. A noticeable exception is the analysis of behavioural online experiments (still for single-winner voting) by Meir et al (2020), who investigate the distribution of types among voters based on a number of myopic heuristics, and compare one-shot voting with access to an information poll to iterative voting where voters can also see each others' votes.…”

Convergence of Multi-Issue Iterative Voting under Uncertainty

“…2 We find in Figure 4 that IV improves average welfare, but at a rate decreasing in r. This finding agrees with experiments by Bowman et al [2014], , suggesting that IV may reduce multiple-election paradoxes by helping agents choose better outcomes. However, further work will be needed to generalize this conclusion, as it contrasts experiments of single-issue IV by Meir et al [2020], Koolyk et al [2017].…”