6,763 research outputs found
Possible Winners in Noisy Elections
We consider the problem of predicting winners in elections, for the case
where we are given complete knowledge about all possible candidates, all
possible voters (together with their preferences), but where it is uncertain
either which candidates exactly register for the election or which voters cast
their votes. Under reasonable assumptions, our problems reduce to counting
variants of election control problems. We either give polynomial-time
algorithms or prove #P-completeness results for counting variants of control by
adding/deleting candidates/voters for Plurality, k-Approval, Approval,
Condorcet, and Maximin voting rules. We consider both the general case, where
voters' preferences are unrestricted, and the case where voters' preferences
are single-peaked.Comment: 34 page
Approval-Based Shortlisting
Shortlisting is the task of reducing a long list of alternatives to a
(smaller) set of best or most suitable alternatives from which a final winner
will be chosen. Shortlisting is often used in the nomination process of awards
or in recommender systems to display featured objects. In this paper, we
analyze shortlisting methods that are based on approval data, a common type of
preferences. Furthermore, we assume that the size of the shortlist, i.e., the
number of best or most suitable alternatives, is not fixed but determined by
the shortlisting method. We axiomatically analyze established and new
shortlisting methods and complement this analysis with an experimental
evaluation based on biased voters and noisy quality estimates. Our results lead
to recommendations which shortlisting methods to use, depending on the desired
properties
The Complexity of Fully Proportional Representation for Single-Crossing Electorates
We study the complexity of winner determination in single-crossing elections
under two classic fully proportional representation
rules---Chamberlin--Courant's rule and Monroe's rule. Winner determination for
these rules is known to be NP-hard for unrestricted preferences. We show that
for single-crossing preferences this problem admits a polynomial-time algorithm
for Chamberlin--Courant's rule, but remains NP-hard for Monroe's rule. Our
algorithm for Chamberlin--Courant's rule can be modified to work for elections
with bounded single-crossing width. To circumvent the hardness result for
Monroe's rule, we consider single-crossing elections that satisfy an additional
constraint, namely, ones where each candidate is ranked first by at least one
voter (such elections are called narcissistic). For single-crossing
narcissistic elections, we provide an efficient algorithm for the egalitarian
version of Monroe's rule.Comment: 23 page
Momentum and Social Learning in Presidential Primaries
This paper provides an investigation of the role of momentum and social learning in sequential voting systems. In the econometric model, voters are uncertain over candidate quality, and voters in late states attempt to infer the information held by those in early states from voting returns. Candidates experience momentum effects when their performance in early states exceeds expectations. The empirical application focuses on the responses of daily polling data to the release of voting returns in the 2004 presidential primary. We find that Kerry benefited from surprising wins in early states and took votes away from Dean, who held a strong lead prior to the beginning of the primary season. The voting weights implied by the estimated model demonstrate that early voters have up to 20 times the influence of late voters in the selection of candidates, demonstrating a significant departure from the ideal of "one person, one vote." We then address several alternative, non-learning explanations for our results. Finally, we run simulations under different electoral structures and find that a simultaneous election would have been more competitive due to the absence of herding and that alternative sequential structures would have yielded different outcomes.
Who Can Win a Single-Elimination Tournament?
A single-elimination (SE) tournament is a popular way to select a winner in
both sports competitions and in elections. A natural and well-studied question
is the tournament fixing problem (TFP): given the set of all pairwise match
outcomes, can a tournament organizer rig an SE tournament by adjusting the
initial seeding so that their favorite player wins? We prove new sufficient
conditions on the pairwise match outcome information and the favorite player,
under which there is guaranteed to be a seeding where the player wins the
tournament. Our results greatly generalize previous results. We also
investigate the relationship between the set of players that can win an SE
tournament under some seeding (so called SE winners) and other traditional
tournament solutions. In addition, we generalize and strengthen prior work on
probabilistic models for generating tournaments. For instance, we show that
\emph{every} player in an player tournament generated by the Condorcet
Random Model will be an SE winner even when the noise is as small as possible,
; prior work only had such results for . We also establish new results for significantly more
general generative models.Comment: A preliminary version appeared in Proceedings of the 30th AAAI
Conference on Artificial Intelligence (AAAI), 201
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Reform and Representation: A New Method Applied to Recent Electoral Changes
Can electoral reforms such as an independent redistricting commission and the top-two primary create conditions that lead to better legislative representation? We explore this question by presenting a new method for measuring a key indicator of representation - the congruence between a legislator's ideological position and the average position of her district's voters. Our novel approach combines two methods: the joint classification of voters and political candidates on the same ideological scale, along with multilevel regression and post-stratification to estimate the position of the average voter across many districts in multiple elections. After validating our approach, we use it to study the recent impact of reforms in California, showing that they did not bring their hoped-for effects
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