34 research outputs found
Computational Aspects of Multi-Winner Approval Voting
We study computational aspects of three prominent voting rules that use
approval ballots to elect multiple winners. These rules are satisfaction
approval voting, proportional approval voting, and reweighted approval voting.
We first show that computing the winner for proportional approval voting is
NP-hard, closing a long standing open problem. As none of the rules are
strategyproof, even for dichotomous preferences, we study various strategic
aspects of the rules. In particular, we examine the computational complexity of
computing a best response for both a single agent and a group of agents. In
many settings, we show that it is NP-hard for an agent or agents to compute how
best to vote given a fixed set of approval ballots from the other agents
Algorithms for two variants of Satisfaction Approval Voting
Multi-winner voting rules based on approval ballots have received increased
attention in recent years. In particular Satisfaction Approval Voting (SAV) and
its variants have been proposed. In this note, we show that the winning set can
be determined in polynomial time for two prominent and natural variants of SAV.
We thank Arkadii Slinko for suggesting these problems in a talk at the Workshop
on Challenges in Algorithmic Social Choice, Bad Belzig, October 11, 2014
Heuristics in Multi-Winner Approval Voting
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- 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
Heuristic Strategies in Uncertain Approval Voting Environments
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 experiment, we show that people vote truthfully in some situations
and prioritize high utility candidates in others. We examine when these
behaviors maximize expected utility and show how the structure of the voting
environment affects both how well each heuristic performs and how humans employ
these heuristics.Comment: arXiv admin note: text overlap with arXiv:1905.1210
Modeling Voters in Multi-Winner Approval Voting
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-
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 vote to obtain a better outcome, but often do not identify the optimal
manipulation. There are a number of predictive models of agent behavior in the
COMSOC and psychology literature that are based on cognitively plausible
heuristic strategies. We show that the existing approaches do not adequately
model real-world data. We propose a novel model that takes into account the
size of the winning set and human cognitive constraints, and demonstrate that
this model is more effective at capturing real-world behaviors in multi-winner
approval voting scenarios.Comment: 9 pages, 4 figures. To be published in the Proceedings of the
Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 202