The Foundations of Causal Decision Theory
This 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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actions agent's preferences Allan Gibbard assign atomless belief revision beliefs and desires bettor causal decision theory chance chapter Coherence conditional probability constant acts constraints context decision maker decision problem defined definition difference disjunction efficacy value entails epistemic epistemic perspective evidential decision theory evidential relevance existence expected payoff expected utility hypothesis expected utility maximization expected utility theory fact fair price finite fortune gambler given grand-world indicative conditional Jeffrey Jeffrey's Equation Jeffrey's theory learning Lemma likelihood ranking logically matter-of-fact supposition Newcomb's Paradox nonnull obeys outcomes pair partition Pascal's Thesis Petersburg Paradox preference ranking principle probabilistic probability function proof propositions in Q prospects Ramsey Test rational agent rational choice theory real numbers reason representation theorem represents requirement Reyni-Popper measures Richard Jeffrey satisfy Savage Savage's theory sense Skyrms Stalnaker's strictly prefers structure axioms subjective probability suppose theorists things tion true unconditional unique utility function
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