| Rational Choice and Classical Decision Theory | p. 3 |
| Rational Cognition | p. 3 |
| Ideal Rationality and Real Rationality | p. 5 |
| Human Rationality and Generic Rationality | p. 8 |
| Decision Making | p. 12 |
| Classical Decision Theory and the Optimality Prescription | p. 14 |
| Values | |
| Evaluative Cognition and the Evaluative Database | p. 23 |
| The Doxastic/Conative Loop | p. 23 |
| Preference Rankings | p. 24 |
| Analog Representations of Values | p. 30 |
| Conclusions | p. 35 |
| Evaluative Induction | p. 37 |
| The Need for Evaluative Induction | p. 37 |
| Human Conative States | p. 38 |
| Evaluative Induction | p. 43 |
| Evaluative Induction as a Q&I Module | p. 50 |
| Conclusions | p. 54 |
| Some Observations about Evaluative Cognition | p. 55 |
| Liking Activities | p. 55 |
| Evaluating the Human Cognitive Architecture | p. 56 |
| State Liking | p. 59 |
| Conclusions | p. 66 |
| The Database Calculation | p. 67 |
| The Database Calculation | p. 68 |
| Justifying the Database Calculation | p. 72 |
| Feature-Based Evaluative Cognition | p. 77 |
| Probabilities | |
| Subjective Probabilities | p. 81 |
| Two Kinds of Probabilities | p. 81 |
| Subjective Probabilities and Degrees of Belief | p. 82 |
| Belief Simpliciter | p. 86 |
| Subjective Expected Utility Theory | p. 87 |
| Rational Decision Making | p. 88 |
| Do Subjective Probabilities Exist | p. 90 |
| Deriving the Optimality Prescription from Rationality Constraints | p. 92 |
| Subjective Probabilities from Epistemology | p. 93 |
| A Return to Objective Probabilities | p. 98 |
| Objective Probabilities | p. 101 |
| Physical Probabilities and Relative Frequencies | p. 101 |
| Empirical Theories | p. 104 |
| Nomic Probability | p. 106 |
| Mixed Physical/Epistemic Probabilities | p. 111 |
| Conclusions | p. 116 |
| Causal Probabilities | p. 117 |
| Causal Decision Theory | p. 117 |
| Probabilistic Causation | p. 118 |
| Skyrms and Lewis | p. 122 |
| Defining Causal Probability | p. 125 |
| Conditional Causal Probability | p. 128 |
| C-PROB[subscript A] and K-PROB[subscript A] | p. 130 |
| Computing Causal Probabilities | p. 135 |
| Computing Conditional Causal Probabilities | p. 138 |
| Simplifying the Computation Defeasibly | p. 140 |
| Conclusions | p. 142 |
| Decisions | |
| Rational Choice and Action Omnipotence | p. 145 |
| Actions and the Optimality Prescription | p. 145 |
| Action Omnipotence | p. 146 |
| Restricting the Scope of the Optimality Prescription | p. 147 |
| Expected Utility | p. 155 |
| Conditional Policies and Expected Utilities | p. 160 |
| Two Problems | p. 163 |
| Computing Expected-Utilities | p. 165 |
| Conclusions | p. 166 |
| Plans and Decisions | p. 167 |
| Against Optimality | p. 167 |
| The Logical Structure of Practical Deliberation | p. 168 |
| Groups of Actions | p. 175 |
| Actions and Plans | p. 178 |
| Choosing between Plans | p. 180 |
| AI Planning Theory: The Real World versus Toy Problems | p. 183 |
| When Is a Plan a Good One? | p. 184 |
| Locally Global Planning | p. 187 |
| Conclusions | p. 189 |
| Plans and Their Expected Utilities | p. 193 |
| Linear Plans | p. 193 |
| Linear Policies | p. 195 |
| Nonlinear Plans | p. 199 |
| Conditional Plans | p. 201 |
| An Example | p. 203 |
| Conclusions | p. 211 |
| Locally Global Planning | p. 213 |
| The Theory | p. 213 |
| Incremental Decision-Theoretic Planning | p. 213 |
| Goal-Directed Planning | p. 215 |
| Presumptively Additive Expected Utilities | p. 220 |
| Finding and Repairing Decision-Theoretic Interference | p. 221 |
| Conclusions | p. 223 |
| The Theory of Nomic Probability | p. 225 |
| Introduction | p. 225 |
| Computational Principles | p. 227 |
| The Statistical Syllogism | p. 232 |
| Direct Inference and Definite Probabilities | p. 236 |
| Indefinite Probabilities and Probability Distributions | p. 241 |
| Induction | p. 242 |
| Conclusions | p. 251 |
| Bibliography | p. 253 |
| Index | p. 263 |
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