A decision theory for non-probabilistic uncertainty and its applications
A decision theory for non-probabilistic uncertainty and its applications
Statistical decisions using likelihood information without prior probabilities
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Decision making for symbolic probability
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Decision making under incomplete data using the imprecise Dirichlet model
International Journal of Approximate Reasoning
A decision theory for partially consonant belief functions
International Journal of Approximate Reasoning
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This paper studies decision making for Walley's partially consonant belief functions (pcb). In a pcb, the set of foci are partitioned. Within each partition, foci are nested. The pcb class includes probability and possibility functions as extreme cases. We adopt an axiomatic system, similar in spirit to von Neumann and Morgenstern's axioms for preferences leading to the linear utility theory, for a preference relation on pcb lotteries. We Drove a representation theorem for this preference relation. Utility for a pcb lottery is a combination of linear utility for probabilistic lottery and binary utility for possibilistic lottery.