David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Minds and Machines 19 (1):61-92 (2009)
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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References found in this work BETA
David K. Lewis (1973). Counterfactuals. Blackwell Publishers.
Gerd Gigerenzer (1999). Simple Heuristics That Make Us Smart. Oxford University Press.
Richard Jeffrey (1983). The Logic of Decision. University of Chicago Press.
Leonard J. Savage (1954). The Foundations of Statistics. Wiley Publications in Statistics.
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