Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs

Lu, Tyler; Boutilier, Craig

doi:10.1007/978-3-642-24873-3_11

Cited by 20 publications

(10 citation statements)

References 11 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This line of research starts with Kalech et al [14] who start by top-1 ballots, then top-2, etc., until there is sufficient information for knowing the winner. Lu and Boutilier [17,16] propose an incremental elicitation process using minimax regret to predict the correct winner given partial information. A more general incremental elicitation framework, with more types of elicitation questions, is cost-effective elicitation [25].…”

Section: (I) Interactive Elicitationmentioning

confidence: 99%

Approximating Voting Rules from Truncated Ballots

Ayadi¹,

Amor²,

Lang³

2020

Preprint

View full text Add to dashboard Cite

Classical voting rules assume that ballots are complete preference orders over candidates. However, when the number of candidates is large enough, it is too costly to ask the voters to rank all candidates. We suggest to fix a rank k, to ask all voters to specify their best k candidates, and then to consider "top-k approximations" of rules, which take only into account the top-k candidates of each ballot. We consider two measures of the quality of the approximation: the probability of selecting the same winner as the original rule, and the score ratio. We do a worst-case study (for the latter measure only), and for both measures, an average-case study and a study from real data sets.

show abstract

Section: (I) Interactive Elicitationmentioning

confidence: 99%

Approximating Voting Rules from Truncated Ballots

Ayadi¹,

Amor²,

Lang³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The so-called impartial model assumption, is a special case of the Mallows distribution, in which the preferences are assumed to be drawn uniformly at random from the complete set of rankings. Lu and Boutilier [2011a;2011b] adopted this approach, in settings where they took a regret-minimization towards optimizing the score of the selected candidate.…”

Section: Introductionmentioning

confidence: 99%

Efficient voting via the top-k elicitation scheme

Filmus

Oren

2014

Proceedings of the Fifteenth ACM Conference on Economics and Computation

View full text Add to dashboard Cite

Top-k voting is a common form of preference elicitation due to its conceptual simplicity both on the voters' side and on the decision maker's side. In a typical setting, given a set of candidates, the voters are required to submit only the k-length prefixes of their intrinsic rankings of the candidates. The decision maker then tries to correctly predict the winning candidate with respect to the complete preference profile according to a prescribed voting rule. This raises a tradeoff between the communication cost (given the specified value of k), and the ability to correctly predict the winner.We focus on arbitrary positional scoring rules in which the voters' scores for the candidates is given by a vector that assigns the ranks real values. We study the performance of top-k elicitation under three probabilistic models of preference distribution: a neutral distribution (impartial culture); a biased distribution, such as the Mallows distribution; and a worst-case (but fully known) distribution.For an impartial culture, we provide a technique for analyzing the performance of top-k voting. For the case of arbitrary positional scoring rules, we provide a succinct set of criteria that is sufficient for obtaining both lower and upper bounds on the minimal k necessary to determine the true winner with high probability. Our lower bounds pertain to any implementation of a top-k voting scheme, whereas for our upper bound, we provide a concrete top-k elicitation algorithm. We further demonstrate the use of this technique on Copeland's voting rule.For the case of biased distributions, we show that for any non-constant scoring rule, the winner can be predicted with high probability without ever looking at the votes. For worst-case distributions, we show that for exponentially decaying scoring rules, k = O(log m) is sufficient for all distributions.

show abstract

“…Many works have been devoted to modeling imperfect preferences. For example, fuzzy preference [11], possibilistic model [1], probabilistic model [8] and Plackett-Luce model [9] have been proposed to deal with preference with uncertainty and have gained success in various scenarios of applications. However, these methods are usually limited to uncertainty information with uncertainty by proportional or probabilistic values by imposing distribution assumptions.…”

Section: Introductionmentioning

confidence: 99%

Unequal Singleton Pair Distance for Evidential Preference Clustering

Zhang

Atkinson

2021

Belief Functions: Theory and Applications

View full text Add to dashboard Cite

Evidential preference based on belief function theory has been proposed recently, simultaneously characterizing preference information with uncertainty and imprecision. However, traditional distances on belief functions do not adapt to some intrinsic properties of preference relations, especially when indifference relation is taken into comparison, therefore may cause inconsistent results in preference-based applications. In order to solve this issue, Unequal Singleton Pair (USP) distance has been proposed previously, with applications limited in preference aggregation. This paper explores forward the effectiveness of USP distance in preference clustering, especially confronting multiple conflicting sources. Moreover, a combination strategy for multiple conflicting sources of preference is proposed. The experiments on synthetic data show that USP distance can effectively improve the clustering results in Adjusted Rand Index (ARI).

show abstract

Vote Elicitation with Probabilistic Preference Models: Empirical Estimation and Cost Tradeoffs

Cited by 20 publications

References 11 publications

Approximating Voting Rules from Truncated Ballots

Approximating Voting Rules from Truncated Ballots

Efficient voting via the top-k elicitation scheme

Unequal Singleton Pair Distance for Evidential Preference Clustering

Contact Info

Product

Resources

About