12,491 research outputs found
Approximation and Parameterized Complexity of Minimax Approval Voting
We present three results on the complexity of Minimax Approval Voting. First,
we study Minimax Approval Voting parameterized by the Hamming distance from
the solution to the votes. We show Minimax Approval Voting admits no algorithm
running in time , unless the Exponential
Time Hypothesis (ETH) fails. This means that the
algorithm of Misra et al. [AAMAS 2015] is essentially optimal. Motivated by
this, we then show a parameterized approximation scheme, running in time
, which is essentially
tight assuming ETH. Finally, we get a new polynomial-time randomized
approximation scheme for Minimax Approval Voting, which runs in time
,
almost matching the running time of the fastest known PTAS for Closest String
due to Ma and Sun [SIAM J. Comp. 2009].Comment: 14 pages, 3 figures, 2 pseudocode
Comparing Election Methods Where Each Voter Ranks Only Few Candidates
Election rules are formal processes that aggregate voters preferences,
typically to select a single candidate, called the winner. Most of the election
rules studied in the literature require the voters to rank the candidates from
the most to the least preferred one. This method of eliciting preferences is
impractical when the number of candidates to be ranked is large. We ask how
well certain election rules (focusing on positional scoring rules and the
Minimax rule) can be approximated from partial preferences collected through
one of the following procedures: (i) randomized-we ask each voter to rank a
random subset of candidates, and (ii) deterministic-we ask each voter to
provide a ranking of her most preferred candidates (the -truncated
ballot). We establish theoretical bounds on the approximation ratios and we
complement our theoretical analysis with computer simulations. We find that
mostly (apart from the cases when the preferences have no or very little
structure) it is better to use the randomized approach. While we obtain fairly
good approximation guarantees for the Borda rule already for , for
approximating the Minimax rule one needs to ask each voter to compare a larger
set of candidates in order to obtain good guarantees
On the Approximability of Dodgson and Young Elections
The voting rules proposed by Dodgson and Young are both designed to nd the alternative closest to being a Condorcet winner, according to two di erent notions of proximity; the score of a given alternative is known to be hard to compute under either rule. In this paper, we put forward two algorithms for ap- proximating the Dodgson score: an LP-based randomized rounding algorithm and a deterministic greedy algorithm, both of which yield an O(logm) approximation ratio, where m is the number of alternatives; we observe that this result is asymptotically optimal, and further prove that our greedy algorithm is optimal up to a factor of 2, unless problems in NP have quasi-polynomial time algorithms. Although the greedy algorithm is computationally superior, we argue that the randomized rounding algorithm has an advantage from a social choice point of view. Further, we demonstrate that computing any reasonable approximation of the ranking produced by Dodgson\u27s rule is NP-hard. This result provides a complexity-theoretic explanation of sharp discrepancies that have been observed in the Social Choice Theory literature when comparing Dodgson elections with simpler voting rules. Finally, we show that the problem of calculating the Young score is NP-hard to approximate by any factor. This leads to an inapproximability result for the Young ranking
Three Puzzles on Mathematics, Computation, and Games
In this lecture I will talk about three mathematical puzzles involving
mathematics and computation that have preoccupied me over the years. The first
puzzle is to understand the amazing success of the simplex algorithm for linear
programming. The second puzzle is about errors made when votes are counted
during elections. The third puzzle is: are quantum computers possible?Comment: ICM 2018 plenary lecture, Rio de Janeiro, 36 pages, 7 Figure
Multiwinner Voting with Fairness Constraints
Multiwinner voting rules are used to select a small representative subset of
candidates or items from a larger set given the preferences of voters. However,
if candidates have sensitive attributes such as gender or ethnicity (when
selecting a committee), or specified types such as political leaning (when
selecting a subset of news items), an algorithm that chooses a subset by
optimizing a multiwinner voting rule may be unbalanced in its selection -- it
may under or over represent a particular gender or political orientation in the
examples above. We introduce an algorithmic framework for multiwinner voting
problems when there is an additional requirement that the selected subset
should be "fair" with respect to a given set of attributes. Our framework
provides the flexibility to (1) specify fairness with respect to multiple,
non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score
function. We study the computational complexity of this constrained multiwinner
voting problem for monotone and submodular score functions and present several
approximation algorithms and matching hardness of approximation results for
various attribute group structure and types of score functions. We also present
simulations that suggest that adding fairness constraints may not affect the
scores significantly when compared to the unconstrained case.Comment: The conference version of this paper appears in IJCAI-ECAI 201
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