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| Content Provider | Springer Nature Link |
|---|---|
| Author | Shaffer, Michael John |
| Copyright Year | 2008 |
| 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. |
| Starting Page | 61 |
| Ending Page | 92 |
| Page Count | 32 |
| File Format | |
| ISSN | 09246495 |
| Journal | Minds and Machines |
| Volume Number | 19 |
| Issue Number | 1 |
| e-ISSN | 15728641 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2008-11-21 |
| Publisher Place | Dordrecht |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Artificial intelligence Bayesianism Causality Conditionals Counterfactuals Decision theory Deliberation Probabilities Rationality Utility Planning Systems Theory, Control Interdisciplinary Studies Philosophy of Mind Artificial Intelligence (incl. Robotics) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Philosophy Artificial Intelligence |
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