Choices: an introduction to decision theory
Choices: an introduction to decision theory
Decision theory in expert systems and artificial intelligence
International Journal of Approximate Reasoning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Probability and conditionals
For the sake of the argument: Ramsey test conditionals, inductive inference, and nonmonotonic reasoning
Conditionals
How different is partial logic?
Partiality, modality, and nonmonotonicity
Partiality, Modality, and Nonmomotonicity
Partiality, Modality, and Nonmomotonicity
Action as a Fast and Frugal Heuristic
Minds and Machines
Simple Inference Heuristics versus Complex Decision Machines
Minds and Machines
Made to Measure: Ecological Rationality in Structured Environments
Minds and Machines
Epistemic entrenchment with incomparabilities and relational belief revision
Proceedings of the Workshop on The Logic of Theory Change
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
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The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are incomplete. Second, it will be argues that when we attempt to incorporate the knowledge of such causal connections into Bayesian decision theory, a substantial technical problem arises for which there is no currently available solution that does not suffer from some damning objection or other. From a broader perspective, this then throws into question the use of decision theory as a model of human or machine planning.