Non-Classical Probabilities for Decision Making in Situations of Uncertainty

Keywords: reasoning under uncertinty, non-classcal probability, Dempster-Shafer theory, Belnap-Dunn logic


Analyzing situations where information is partial, incomplete or contradictory has created a demand for quantitative belief measures that are weaker than classic probability theory. In this paper, we compare two frameworks that have been proposed for this task, Dempster-Shafer theory and non-standard probability theory based on Belnap-Dunn logic. We show the two frameworks to assume orthogonal perspectives on informational shortcomings, but also provide a partial correspondence result. Lastly, we also compare various dynamical rules of the two frameworks, all seen as generalizations of classic Bayes’ conditiong.


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