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arxiv: 1903.00336 · v2 · pith:GNCWPAWInew · submitted 2019-02-28 · 💻 cs.AI

Interpreting, axiomatising and representing coherent choice functions in terms of desirability

classification 💻 cs.AI
keywords choicetermsfunctionscoherencecoherentdesirabilityinterpretationorders
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Choice functions constitute a simple, direct and very general mathematical framework for modelling choice under uncertainty. In particular, they are able to represent the set-valued choices that appear in imprecise-probabilistic decision making. We provide these choice functions with a clear interpretation in terms of desirability, use this interpretation to derive a set of basic coherence axioms, and show that this notion of coherence leads to a representation in terms of sets of strict preference orders. By imposing additional properties such as totality, the mixing property and Archimedeanity, we obtain representation in terms of sets of strict total orders, lexicographic probability systems, coherent lower previsions or linear previsions.

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    A decision-theoretic model is developed in which quantum measurements act as uncertain decisions whose utilities encode Born's rule, enabling an imprecise-probabilities treatment of quantum uncertainty.