Ranking Candidates Through Convex Sequences of Variable Weights

Llamazares, Bonifacio

doi:10.1007/s10726-015-9452-8

Cited by 10 publications

(6 citation statements)

References 29 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We now introduce the concept of possible and necessary winners of a positional scoring rule with uncertain scoring vector. Note that this differs from the more established notions of possible and necessary winners in voting (Konczak and Lang 2005;Xia and Conitzer 2011), where uncertainty is on the preferences and not on the voting rule.…”

Section: Possible and Necessary Winnersmentioning

confidence: 88%

Robust winner determination in positional scoring rules with uncertain weights

Viappiani¹

2019

Theory Decis

View full text Add to dashboard Cite

Scoring rules constitute a particularly popular technique for aggregating a set of rankings. However, setting the weights associated to rank positions is a crucial task, as different instantiations of the weights can often lead to different winners. In this work we adopt minimax regret as a robust criterion for determining the winner in the presence of uncertainty over the weights. Focusing on two general settings (non-increasing weights and convex sequences of non-increasing weights) we provide a characterization of the minimax regret rule in terms of cumulative ranks, allowing a quick computation of the winner. We then analyze the properties of using minimax regret as a social choice function. Finally we provide some test cases of rank aggregation using the proposed method.

show abstract

Section: Possible and Necessary Winnersmentioning

confidence: 88%

Robust winner determination in positional scoring rules with uncertain weights

Viappiani¹

2019

Theory Decis

View full text Add to dashboard Cite

show abstract

“…Evidently, a crucial issue in this framework is the choice of the scoring vector, since, as it is well known, the ordering of alternatives may depend on the scoring vector used. It is worth mentioning that in addition to the numerous academic examples found in the literature (see, for instance, Fishburn, 1981), Llamazares and Peña (2013) and Llamazares (2016) have also shown this fact through scoring vectors used in some sports competitions (concretely, in the Formula One World Championship and in the Motorcycle World Championship).…”

Section: Introductionmentioning

confidence: 90%

“…An analysis of some of them can be found in Llamazares and Peña (2009). Besides the above models, which are based on DEA methodology, there exist other similar models where variable weights are also used (see, for instance, Hashimoto and Wu (2004); Contreras et al (2005); Wang et al (2007a,b); Contreras (2011); Ebrahimnejad (2012); Foroughi and Aouni (2012); Hosseinzadeh Lotfi et al (2013); Llamazares and Peña (2013); Hadi-Vencheh (2014); Khodabakhshi and Aryavash (2015); Llamazares (2016)). Among this great variety of models, in this paper we focus on the approach proposed by Khodabakhshi and Aryavash (2015).…”

Section: Introductionmentioning

confidence: 99%

Aggregating preference rankings using an optimistic-pessimistic approach: Closed-form expressions

Llamazares

2017

Computers & Industrial Engineering

Self Cite

View full text Add to dashboard Cite

There exist in the literature several models to tackle the problem of aggregating preferences rankings where each alternative is evaluated with the most favorable scoring vector for it (which can be considered as an optimistic approach). Recently, Khodabakhshi and Aryavash [M. Khodabakhshi, K. Aryavash, Aggregating preference rankings using an optimistic-pessimistic approach, Computers & Industrial Engineering 85 (2015) 13-16] have suggested a new model where both the optimistic and the pessimistic approaches are taken into account. In this paper we provide closed-form expressions for the scores of alternatives when the model proposed by these authors is used. The expressions obtained allow us to analyze the model and suggest some small modifications.

show abstract

“…The winners f w (v) are the alternatives with highest score. An important class of PSRs is the one using convex weights [30,19], meaning that the difference between the weight of the first position and the weight of the second position is at least as large as the difference between the weights of the second and third positions, etc. ∀r ∈ {1, .…”

Section: Social Choice With Partial Informationmentioning

confidence: 99%

Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination

Napolitano

Cailloux

Viappiani³

2021

Algorithmic Decision Theory

View full text Add to dashboard Cite

Social choice deals with the problem of determining a consensus choice from the preferences of different agents. In the classical setting, the voting rule is fixed beforehand and full information concerning the preferences of the agents is provided. This assumption of full preference information has recently been questioned by a number of researchers and several methods for eliciting the preferences of the agents have been proposed. In this paper we argue that in many situations one should consider as well the voting rule to be partially specified. Focusing on positional scoring rules, we assume that the chair, while not able to give a precise definition of the rule, is capable of answering simple questions requiring to pick a winner from a concrete profile. In addition, we assume that the agent preferences also have to be elicited. We propose a method for robust approximate winner determination and interactive elicitation based on minimax regret; we develop several strategies for choosing the questions to ask to the chair and the agents in order to converge quickly to a near-optimal alternative. Finally, we analyze these strategies in experiments where the rule and the preferences are simultaneously elicited.

show abstract

Ranking Candidates Through Convex Sequences of Variable Weights

Cited by 10 publications

References 29 publications

Robust winner determination in positional scoring rules with uncertain weights

Robust winner determination in positional scoring rules with uncertain weights

Aggregating preference rankings using an optimistic-pessimistic approach: Closed-form expressions

Simultaneous Elicitation of Scoring Rule and Agent Preferences for Robust Winner Determination

Contact Info

Product

Resources

About