Decision theory, intelligent planning and counterfactuals

Minds and Machines 19 (1):61-92 (2009)
Authors
Michael Shaffer
St. Cloud State University
Abstract
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.
Keywords Artificial intelligence   Bayesianism   Causality   Conditionals   Counterfactuals   Decision theory   Deliberation   Planning   Probabilities   Rationality   Utility
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DOI 10.1007/s11023-008-9126-2
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Counterfactuals.David K. Lewis - 1973 - Blackwell.
The Foundations of Statistics.Leonard J. Savage - 1954 - Wiley Publications in Statistics.

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