Weighted Sets of Probabilities and MinimaxWeighted Expected Regret: New Approaches for Representing Uncertainty and Making Decisions

Joseph Halpern () and Samantha Leung

Papers from arXiv.org

Abstract: We consider a setting where an agent's uncertainty is represented by a set of probability measures, rather than a single measure. Measure-bymeasure updating of such a set of measures upon acquiring new information is well-known to suffer from problems; agents are not always able to learn appropriately. To deal with these problems, we propose using weighted sets of probabilities: a representation where each measure is associated with a weight, which denotes its significance. We describe a natural approach to updating in such a situation and a natural approach to determining the weights. We then show how this representation can be used in decision-making, by modifying a standard approach to decision making-minimizing expected regret-to obtain minimax weighted expected regret (MWER).We provide an axiomatization that characterizes preferences induced by MWER both in the static and dynamic case.

Date: 2012-10
New Economics Papers: this item is included in nep-rmg and nep-upt
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Citations: View citations in EconPapers (2)

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Journal Article: Weighted sets of probabilities and minimax weighted expected regret: a new approach for representing uncertainty and making decisions (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1210.4853

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