The Foundations of Causal Decision TheoryThis book defends the view that any adequate account of rational decision making must take a decision maker's beliefs about causal relations into account. The early chapters of the book introduce the non-specialist to the rudiments of expected utitlity theory. The major technical advance offered by the book is a "representation theorem" that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory. In providing the most complete and robust defense of causal decision theory the book will be of interest to a broad range of readers in philosophy, economics, psychology, mathematics, and artificial intelligence. |
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Common terms and phrases
actions agent's preferences Allan Gibbard Armendt assign auspiciousness axioms belief revision beliefs and desires causal decision theory chance chapter choice Coherence Conditional Excluded Middle conditional probability constant acts constraint decision maker decision problem defined difference efficacy value entails epistemic epistemic perspective evaluate evidence evidential decision theory evidential relevance evidentialist expected utility hypothesis expected utility theory fact fair price G₁ G₂ Gibbard given grand-world holds indicative conditionals Jeffrey Jeffrey's Equation learning Lemma Lewis likelihood ranking logically matter-of-fact supposition Newcomb's Paradox nonnull obeys outcomes partition Pascal's payoffs preference ranking probabilistic probability function proof propositions Q₂ Ramsey Test rational agent real numbers reason relative representation theorem requirement Réyni-Popper measures Richard Jeffrey satisfy Savage Savage's theory sense similarity gauge Skyrms small-world Stalnaker's subjective probability subjunctive conditionals subjunctive supposition suppose take the money theorists things tion true unconditional unique utility function wager X₁ X₂ Y₂