Publikation: A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity
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Ambiguity occurs insofar as a reasoner lacks information about the relevant physical probabilities. There are objections to the application of standard Bayesian inductive logic and decision theory in contexts of significant ambiguity. A variety of alternative frameworks for reasoning under ambiguity have been proposed. Two of the most prominent are Imprecise Bayesianism and Dempster–Shafer theory. We compare these inductive logics with respect to the Ambiguity Dilemma, which is a problem that has been raised for Imprecise Bayesianism. We develop an agent-based model comparison that isolates the difference between the two inductive logics in their updating methods. We find that Dempster–Shafer theory does not avoid the Ambiguity Dilemma. We discuss the implications of this result.
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RADZVILAS, Mantas, William PEDEN, Daniele TORTOLI, Francesco DE PRETIS, 2024. A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity. In: Journal of Logic and Computation. Oxford University Press (OUP). ISSN 0955-792X. eISSN 1465-363X. Verfügbar unter: doi: 10.1093/logcom/exae069BibTex
@article{Radzvilas2024-10-23compa-71160, year={2024}, doi={10.1093/logcom/exae069}, title={A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity}, issn={0955-792X}, journal={Journal of Logic and Computation}, author={Radzvilas, Mantas and Peden, William and Tortoli, Daniele and De Pretis, Francesco} }
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