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Robust Winner Determination in Positional Scoring Rules with Uncertain Weights

Paolo Viappiani ()
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Paolo Viappiani: DECISION - LIP6 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique

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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.

Date: 2020-04
New Economics Papers: this item is included in nep-spo
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Published in Theory and Decision, 2020, 88 (3), pp.323-367. ⟨10.1007/s11238-019-09734-3⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02373399

DOI: 10.1007/s11238-019-09734-3

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