Aggregating Belief Models
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Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 103:3848, 2019.
Abstract
This paper has two goals. The first goal is to say something about how one might combine different agents’ imprecise probabilities to generate an aggregate imprecise probability. The second goal is to champion the very general theory of “belief models” (de Cooman “Belief models: an order theoretic investigation” Annals of Mathematics and AI 2005) which, I think, deserves more attention. The belief models framework is interesting partly because many other formal models of reasoning appear as special cases of belief models (for example, propositional logic, ranking functions, imprecise probability).
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