Heuristics in Multi-Winner Approval Voting (original) (raw)

Heuristic Strategies in Uncertain Approval Voting Environments

2020

In many collective decision making situations, agents vote to choose an alternative that best represents the preferences of the group. Agents may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In real world voting scenarios, people often do not have complete information about other voter preferences and it can be computationally complex to identify a strategy that will maximize their expected utility. In such situations, it is often assumed that voters will vote truthfully rather than expending the effort to strategize. However, being truthful is just one possible heuristic that may be used. In this paper, we examine the effectiveness of heuristics in single winner and multi-winner approval voting scenarios with missing votes. In particular, we look at heuristics where a voter ignores information about other voting profiles and makes their decisions based solely on how much they like each candidate. In a behavioral ex...

Modeling Voters in Multi-Winner Approval Voting

2021

In many real world situations, collective decisions are made using voting and, in scenarios such as committee or board elections, employing voting rules that return multiple winners. In multi-winner approval voting (AV), an agent submits 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 may manipulate the ballot they submit in order 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 instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty using behavioral data obtained from Mechanical Turk. We find that people generally manipulate their vo...

The Wisdom of Strategic Voting

Proceedings of the 24th ACM Conference on Economics and Computation

We study the voting game where agents' preferences are endogenously decided by the information they receive, and they can collaborate in a group. We show that strategic voting behaviors have a positive impact on leading to the "correct" decision, outperforming the common non-strategic behavior of informative voting and sincere voting. Our results give merit to strategic voting for making good decisions. To this end, we investigate a natural model, where voters' preferences between two alternatives depend on a discrete state variable that is not directly observable. Each voter receives a private signal that is correlated with the state variable. We reveal a surprising equilibrium between a strategy profile being a strong equilibrium and leading to the decision favored by the majority of agents conditioned on them knowing the ground truth (referred to as the informed majority decision): as the size of the vote goes to infinity, every-strong Bayes Nash Equilibrium with converging to 0 formed by strategic agents leads to the informed majority decision with probability converging to 1. On the other hand, we show that informative voting leads to the informed majority decision only under unbiased instances, and sincere voting leads to the informed majority decision only when it also forms an equilibrium. CCS Concepts: • Theory of computation → Algorithmic game theory; • Computing methodologies → Distributed artificial intelligence.

Strategic Voting Under Uncertainty About the Voting Method

Proceedings of TARK 2019, 2019

Much of the theoretical work on strategic voting makes strong assumptions about what voters know about the voting situation. A strategizing voter is typically assumed to know how other voters will vote and to know the rules of the voting method. A growing body of literature explores strategic voting when there is uncertainty about how others will vote. In this paper, we study strategic voting when there is uncertainty about the voting method. We introduce three notions of manipulability for a set of voting methods: sure, safe, and expected manipulability. With the help of a computer program, we identify voting scenarios in which uncertainty about the voting method may reduce or even eliminate a voter's incentive to misrepresent her preferences. Thus, it may be in the interest of an election designer who wishes to reduce strategic voting to leave voters uncertain about which of several reasonable voting methods will be used to determine the winners of an election.

The Basic Approval Voting Game

Studies in Choice and Welfare, 2010

We survey results about Approval Voting obtained within the standard framework of game theory. Restricting the set of strategies to undominated and sincere ballots does not help to predict Approval Voting outcomes, which is also the case under strategic equilibrium concepts such as Nash equilibrium and its usual re…nements. Strong Nash equilibrium in general does not exist but predicts the election of a Condorcet winner when one exists.

Evaluating approval-based multiwinner voting in terms of robustness to noise

2020

Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting is always robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.

The complexity of strategic behavior in multi-winner elections

2008

Although recent years have seen a surge of interest in the computational aspects of social choice, no specific attention has previously been devoted to elections with multiple winners, e.g., elections of an assembly or committee. In this paper, we characterize the worst-case complexity of manipulation and control in the context of four prominent multiwinner voting systems, under different formulations of the strategic agent's goal.

Sincere, strategic, and heuristic voting under four election rules: An experimental study

2008

Nous rendons compte d'une série d'expériences de laboratoire à propos des comportements de vote. Dans une situation où les sujets ont des préférences unimodales nous observons que le vote à un tour et le vote à deux tours génèrent des effets significatifs de dépendance du chemin, alors que le vote par approbation élit toujours le vainqueur de Condorcet et que le vote unique transférable (système de Hare) ne l'élit jamais. A partir de l'analyse des données individuelles nous concluons que les électeurs se comportent de manière stratégique tant que les calculs stratégiques ne sont pas trop complexes, auquel cas ils se repose sur des heuristiques simples.

Dynamics of manipulation in voting, veto and plurality

Cluster Computing, 2018

Multi-agent decision problems, in which independent agents have to agree on a joint plan of action or allocation of resources, are central to artificial intelligence. The main focus of paper is the analysis of dynamics of manipulation in voting rules like plurality and veto. An important technical issue that arises is manipulation of voting schemes: a voter may be able to improve the outcome (with respect to his own preferences) by reporting his preferences incorrectly. We consider scenarios where voters cannot coordinate their actions, but are allowed to change their vote after observing the current outcome, as is often the case both in offline committees and in online voting. Voters are allowed to change their votes if they can get their desirable results, we have worked on veto and plurality rule with the small number of candidates and voters. We also used different moves for analysing the dynamics of voting system and concluded different results based on different types of moves (both manipulative and non-manipulative). We also defined a new tie breaking rule ''Typicographical rule'' and according to our observation it works better than the lexicographical rule.

Lie on the Fly: Strategic Voting in an Iterative Preference Elicitation Process

Group Decision and Negotiation

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winning and which do not. Consequently, strategic voters might attempt to manipulate by deviating from their true preferences and instead submit a different response in order to attempt to maximize their profit. We provide a practical algorithm for strategic voters which computes the best manipulative vote and maximizes the voter's selfish outcome when such a vote exists. We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible. In an empirical study on four real world domains, we show that in practice manipulation occurs in a low percentage of settings and has a low impact on the final outcome. The careful voting center reduces manipulation even further, thus allowing for a non-distorted group decision process to take place. We thus provide a core technology study of a voting process that can be adopted in opinion or information aggregation systems and in crowdsourcing applications, e.g., peer grading in Massive Open Online Courses (MOOCs).