9 research outputs found
How Hard Is It to Control an Election by Breaking Ties?
We study the computational complexity of controlling the result of an
election by breaking ties strategically. This problem is equivalent to the
problem of deciding the winner of an election under parallel universes
tie-breaking. When the chair of the election is only asked to break ties to
choose between one of the co-winners, the problem is trivially easy. However,
in multi-round elections, we prove that it can be NP-hard for the chair to
compute how to break ties to ensure a given result. Additionally, we show that
the form of the tie-breaking function can increase the opportunities for
control. Indeed, we prove that it can be NP-hard to control an election by
breaking ties even with a two-stage voting rule.Comment: Revised and expanded version including longer proofs and additional
result
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
On the Hardness of Bribery Variants in Voting with CP-Nets
We continue previous work by Mattei et al. (Mattei, N., Pini, M., Rossi, F.,
Venable, K.: Bribery in voting with CP-nets. Ann. of Math. and Artif. Intell.
pp. 1--26 (2013)) in which they study the computational complexity of bribery
schemes when voters have conditional preferences that are modeled by CP-nets.
For most of the cases they considered, they could show that the bribery problem
is solvable in polynomial time. Some cases remained open---we solve two of them
and extend the previous results to the case that voters are weighted. Moreover,
we consider negative (weighted) bribery in CP-nets, when the briber is not
allowed to pay voters to vote for his preferred candidate.Comment: improved readability; identified Cheapest Subsets to be the
enumeration variant of K.th Largest Subset, so we renamed it to K-Smallest
Subsets and point to the literatur; some more typos fixe
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
Complexity of Manipulating and Controlling Approval-Based Multiwinner Voting
We investigate the complexity of several manipulation and control problems
under numerous prevalent approval-based multiwinner voting rules. Particularly,
the rules we study include approval voting (AV), satisfaction approval voting
(SAV), net-satisfaction approval voting (NSAV), proportional approval voting
(PAV), approval-based Chamberlin-Courant voting (ABCCV), minimax approval
voting (MAV), etc. We show that these rules generally resist the strategic
types scrutinized in the paper, with only a few exceptions. In addition, we
also obtain many fixed-parameter tractability results for these problems with
respect to several natural parameters, and derive polynomial-time algorithms
for certain special cases.Comment: 45pages, 1figure, full version of a paper at IJCAI 201
The Schulze Method of Voting
We propose a new single-winner election method ("Schulze method") and prove
that it satisfies many academic criteria (e.g. monotonicity, reversal symmetry,
resolvability, independence of clones, Condorcet criterion, k-consistency,
polynomial runtime). We then generalize this method to proportional
representation by the single transferable vote ("Schulze STV") and to methods
to calculate a proportional ranking ("Schulze proportional ranking").
Furthermore, we propose a generalization of the Condorcet criterion to
multi-winner elections. This paper contains a large number of examples to
illustrate the proposed methods