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- @math 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 this http URL 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.
There are many other closely related studies of strategic voting and preference reporting in cooperative and noncooperative settings. Recently, developed a large system to look at how users report preferences to an allocation mechanism and studied the strategic behavior of those users; finding that most users were myopic and that searching a small space of accounted for most strategic voting. There is also a series of papers looking at predicting and modeling user behavior in simple games, including voting games @cite_22 @cite_1 @cite_37 . In each of these papers large corpora of humans playing simple games are collected and then models are proposed to fit the observed behavior. Generally speaking, most participants are only strategic in a limited sense and do not look ahead as far into the future as one would expect based on formal game theoretic models @cite_33 .Other recent work with human decision making in cooperative games settings includes weighted voting games @cite_9 and coalition allocation games @cite_20 .
@cite_37 @cite_22 @cite_33 @cite_9 @cite_1 @cite_20
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We present a systematic study of Plurality elections with strategic voters who, in addition to having preferences over election winners, also have secondary preferences, governing their behavior when their vote cannot affect the election outcome. Specifically, we study two models that have been recently considered in the literature: lazy voters, who prefer to abstain when they are not pivotal, and truth-biased voters, who prefer to vote truthfully when they are not pivotal. For both lazy and truth-biased voters, we are interested in their behavior under different tie-breaking rules (lexicographic rule, random voter rule, random candidate rule). Two of these six combinations of secondary preferences and tie-breaking rules have been studied in prior work; for the remaining four, we characterize pure Nash equilibria (PNE) of the resulting strategic games and study the complexity of related computational problems. We then use these results to analyze the impact of different secondary preferences and tie-breaking rules on the election outcomes. Our results extend to settings where some of the voters are non-strategic. To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users. We theoretically and experimentally study voter behavior in a setting characterized by plurality rule and mandatory voting, where voters choose from three options. We are interested in the occurrence of strategic voting in an environment where Condorcet cycles may occur. In particular, we focus on how information about the distribution of preferences affects strategic behavior. We also vary the relative importance of the second preferred option to investigate how this affects the strategic vote. Quantal response equilibrium analysis is used to analyze the game and proves to be a good predictor for the experimental data. Our results indeed show that strategic voting arises, the extent of which depends on (i) the availability of information; (ii) the relative importance of the intermediate candidate; (iii) the electorate's relative support for one's preferred candidate; and (iv) the relative position of the plurality-supported candidate in a voter's preference ordering. Our results show that information serves as a coordination device where strategic voting does not harm the plurality-preferred candidate's chances of winning. Plurality voting is perhaps the most commonly used way to aggregate the preferences of multiple voters. The purpose of this paper is to provide a comprehensive study of people's voting behaviour in various online settings under the Plurality rule. Our empirical methodology consisted of a voting game in which participants vote for a single candidate out of a given set. We implemented voting games that replicate two common real-world voting scenarios: In the first, a single voter votes once after seeing a large pre-election poll. In the second game, several voters play simultaneously, and change their vote as the game progresses, as in small committees. 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 and their preferences. We show that people can be classified into at least three groups, two of which are not engaged in any strategic behavior. The third and largest group tends to select the natural "default" action when there is no clear strategic alternative. When an active strategic decision can be made that improves their immediate payoff, people usually choose that strategic alternative. 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.