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Journal Articles Theory and Decision Year : 2020

Robust Winner Determination in Positional Scoring Rules with Uncertain Weights

Paolo Viappiani

Abstract

Scoring rules constitute a particularly popular technique for aggregating a set of rank-ings. 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.
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Dates and versions

hal-02373399 , version 1 (20-11-2019)

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Paolo Viappiani. Robust Winner Determination in Positional Scoring Rules with Uncertain Weights. Theory and Decision, 2020, 88 (3), pp.323-367. ⟨10.1007/s11238-019-09734-3⟩. ⟨hal-02373399⟩
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