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Weighted Regret-Based Likelihood: A New Approach to Describing Uncertainty
Author(s) -
Joseph Y. Halpern
Publication year - 2015
Publication title -
journal of artificial intelligence research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.79
H-Index - 123
eISSN - 1943-5037
pISSN - 1076-9757
DOI - 10.1613/jair.4859
Subject(s) - regret , generalization , axiom , event (particle physics) , representation (politics) , mathematics , set (abstract data type) , imprecise probability , mathematical economics , computer science , probability distribution , statistics , mathematical analysis , physics , geometry , quantum mechanics , politics , political science , law , programming language
Recently, Halpern and Leung suggested representing uncertainty by a set of weighted probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event E′. In this paper, a notion of comparative likelihood when uncertainty is represented by a set of weighted probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given.

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