Thinking and Deciding

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DISCOVERING VALUES 345 Part 1E2. Grads Means 1E2a. Attract grad students Means 1E2a1. Have areas that are uniquely good Means 1E2a2. Have people doing uniquely good work Means 1E2b. Educate them Means 1F. Work in a promising field Means 1G. Be creative and industrious 2. Provide undergrads with beneficial understanding and knowledge Part 2A. Knowledge of psychology and biological basis of behavior Part 2A1. For application (clinical psych., medicine) Part 2A2. For research preparation (for the students) Part 2A3. For enriching knowledge of related fields Means 2A4. Cover field broadly Means 2A5. Provide research experience Part 2B. Knowledge of methodology and data analysis Part 2B1. For research in other fields Part 2B2. For citizenship Means 2B3. Provide research experience (same as 2A2) Part 2C. General wisdom and enlightenment Part 2C1. As individuals Part 2C2. As citizens Means 2C3. Present interesting point of view Means 2C4. Be wise and enlightened Means 2A. Attract educable, interested undergrads to courses Part 2A1. Be good teacher Part 2A2. Appeal to current interests Means 2A3. Increase prestige of Penn (media, etc.) 3. Make our lives better Part 3A. Good departmental citizen Part 3B. Good colleague 4. Help the university Part 4A. Attract good students Part 4B. Get money Part 4B1. Get grants (overhead) Means 4B1a. Do research on fundable topics Part 4B2. Attract donations Means 4B2a. Do research on popular topics 5. Improve future decision making Part 5A. Avoid lopsided majorities in future votes I wrote this as a guide to my own thinking about a decision my department was making about which fields to search in over the next five years. I showed it to a few colleagues, and they added the last point. I actually cared about this too. I was worried that, regardless of the other factors, hiring someone in the more scientific parts
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Thinking and Deciding Fourth Editio
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Contents Preface to the fourth edit
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CONTENTS vii Coincidences .........
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CONTENTS ix Disappointment and elat
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CONTENTS xi Rule utilitarianism . .
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Preface to the fourth edition The f
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Part I THINKING IN GENERAL
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Chapter 1 What is thinking? Beginni
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THE SEARCH-INFERENCE FRAMEWORK 7 Le
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THE SEARCH-INFERENCE FRAMEWORK 9 Go
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THE SEARCH-INFERENCE FRAMEWORK 11 I
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THINKING ABOUT BELIEFS 13 Science d
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HOW DO SEARCH PROCESSES WORK? 15 of
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KNOWLEDGE, THINKING, AND UNDERSTAND
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CONCLUSION 29 on the highest standa
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32 THE STUDY OF THINKING Descriptiv
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34 THE STUDY OF THINKING The second
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36 THE STUDY OF THINKING 5. Maybe t
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38 THE STUDY OF THINKING that you w
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40 THE STUDY OF THINKING Tetlock fo
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42 THE STUDY OF THINKING Training a
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44 THE STUDY OF THINKING that appro
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46 THE STUDY OF THINKING must use a
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48 THE STUDY OF THINKING The purpos
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50 THE STUDY OF THINKING Incentives
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52 THE STUDY OF THINKING ably are n
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54 THE STUDY OF THINKING Developmen
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56 THE STUDY OF THINKING Table 2.1:
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58 THE STUDY OF THINKING our goals
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Chapter 3 Rationality In the case o
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GOOD THINKING AND GOAL ACHIEVEMENT
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GOOD THINKING AND GOAL ACHIEVEMENT
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RATIONALITY AND EMOTION 67 of how t
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RATIONALITY AND EMOTION 69 Because
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RATIONALITY AND BELIEF 71 Self-dece
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RATIONALITY AND BELIEF 73 From a no
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CONCLUSION 75 propose do not help u
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78 LOGIC answer. He reasons that al
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80 LOGIC Again, this sounds good, b
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82 LOGIC For each system of logic,
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84 LOGIC S: If you know a person, i
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86 LOGIC No A are B. All B are C. T
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88 LOGIC answer is, “None of the
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90 LOGIC before they were forced to
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92 LOGIC The same subject did not c
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94 LOGIC be made with such absolute
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96 LOGIC Formal syllogisms illustra
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Part II PROBABILITY AND BELIEF
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Chapter 5 Normative theory of proba
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NORMATIVE THEORY OF PROBABILITY 105
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WHAT IS PROBABILITY? 107 theory. Th
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WHAT IS PROBABILITY? 109 have it. L
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WHAT IS PROBABILITY? 111 The person
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WELL-JUSTIFIED PROBABILITY JUDGMENT
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EVALUATING PROBABILITY JUDGMENTS 11
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BAYES’S THEOREM 121 Certain scori
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BAYES’S THEOREM 123 Formulas for
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BAYES’S THEOREM 125 Figure 5.2: G
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BAYES’S THEOREM 127 other, that i
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BAYES’S THEOREM 129 males and fem
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BAYES’S THEOREM 131 Although the
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BAYES’S THEOREM 133 The difficult
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CONCLUSION 135 2. p(aids) =.0001, p
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138 DESCRIPTIVE THEORY OF PROBABILI
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162 HYPOTHESIS TESTING willing to m
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164 HYPOTHESIS TESTING not found, s
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166 HYPOTHESIS TESTING they are nev
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168 HYPOTHESIS TESTING given any ot
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170 HYPOTHESIS TESTING Bruner and h
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172 HYPOTHESIS TESTING hypothesis.
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174 HYPOTHESIS TESTING You open the
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176 HYPOTHESIS TESTING Both of thes
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178 HYPOTHESIS TESTING would certai
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180 HYPOTHESIS TESTING Table 7.2: Z
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182 HYPOTHESIS TESTING Table 7.5: T
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184 JUDGMENT OF CORRELATION AND CON
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200 ACTIVELY OPEN-MINDED THINKING t
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202 ACTIVELY OPEN-MINDED THINKING 5
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204 ACTIVELY OPEN-MINDED THINKING D
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206 ACTIVELY OPEN-MINDED THINKING b
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208 ACTIVELY OPEN-MINDED THINKING I
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210 ACTIVELY OPEN-MINDED THINKING s
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212 ACTIVELY OPEN-MINDED THINKING t
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214 ACTIVELY OPEN-MINDED THINKING o
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216 ACTIVELY OPEN-MINDED THINKING T
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218 ACTIVELY OPEN-MINDED THINKING (
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224 ACTIVELY OPEN-MINDED THINKING s
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226 ACTIVELY OPEN-MINDED THINKING B
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Part III DECISIONS AND PLANS
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Chapter 10 Normative theory of choi
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NORMATIVE THEORY OF CHOICE 235 We s
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EXPECTED-UTILITY THEORY 237 that pr
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EXPECTED-UTILITY THEORY 239 Table 1
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EXPECTED-UTILITY THEORY 241 are sti
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WHY EXPECTED-UTILITY THEORY IS NORM
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THE UTILITY OF MONEY 251 we evaluat
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THE UTILITY OF MONEY 253 Figure 10.
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CONCLUSION 255 7. On the basis of y
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Chapter 11 Descriptive theory of ch
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BIAS IN DECISIONS UNDER UNCERTAINTY
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BIAS IN DECISIONS UNDER UNCERTAINTY
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PROSPECT THEORY 263 Probability: Th
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PROSPECT THEORY 265 asserts that on
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PROSPECT THEORY 267 A similar “th
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PROSPECT THEORY 269 care proportion
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PROSPECT THEORY 271 if X ≥ 0 and
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RANK-DEPENDENT UTILITY THEORIES 273
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EMOTIONAL EFFECTS OF OUTCOMES 275 E
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EMOTIONAL EFFECTS OF OUTCOMES 277 T
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EMOTIONAL EFFECTS OF OUTCOMES 279 a
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THE AMBIGUITY EFFECT 281 Table 11.7
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THE AMBIGUITY EFFECT 283 not the wo
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UNCERTAINTY AND REASONS FOR CHOICE
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CONCLUSION 287 imperfect correlatio
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290 CHOICE UNDER CERTAINTY if his c
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292 CHOICE UNDER CERTAINTY their be
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294 CHOICE UNDER CERTAINTY means. T
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296 CHOICE UNDER CERTAINTY for eval
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298 CHOICE UNDER CERTAINTY for the
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300 CHOICE UNDER CERTAINTY Behavior
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302 CHOICE UNDER CERTAINTY At other
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304 CHOICE UNDER CERTAINTY We sell
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306 CHOICE UNDER CERTAINTY either t
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308 CHOICE UNDER CERTAINTY You are
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Chapter 13 Utility measurement The
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DECISION ANALYSIS AND RELATED METHO
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DECISION ANALYSIS AND RELATED METHO
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THE MEASUREMENT OF UTILITY 317 Many
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Page 708: CONCLUSION 339 win the $100, if all
Page 712: Chapter 14 Decision analysis and va
Page 716: DISCOVERING VALUES 343 why people c
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Page 756: Chapter 15 Quantitative judgment Qu
Page 760: THE LENS MODEL 365 Table 15.1: Data
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THE LENS MODEL 371 because it belie
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THE MECHANISM OF JUDGMENT 373 proba
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THE MECHANISM OF JUDGMENT 375 The t
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THE MECHANISM OF JUDGMENT 377 pende
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THE MECHANISM OF JUDGMENT 379 1963.
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THE MECHANISM OF JUDGMENT 381 Elimi
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CLASSIFICATION 383 more eggs. ‘Ho
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FUNCTIONAL MEASUREMENT AND CONJOINT
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CONCLUSION 387 quality. The numbers
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390 MORAL JUDGMENT AND CHOICE We th
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392 MORAL JUDGMENT AND CHOICE Relat
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394 MORAL JUDGMENT AND CHOICE appea
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396 MORAL JUDGMENT AND CHOICE Moral
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398 MORAL JUDGMENT AND CHOICE conse
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400 MORAL JUDGMENT AND CHOICE must
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402 MORAL JUDGMENT AND CHOICE Suppo
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404 MORAL JUDGMENT AND CHOICE is na
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406 MORAL JUDGMENT AND CHOICE freed
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408 MORAL JUDGMENT AND CHOICE again
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410 MORAL JUDGMENT AND CHOICE optio
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412 MORAL JUDGMENT AND CHOICE Natur
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414 MORAL JUDGMENT AND CHOICE If th
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416 MORAL JUDGMENT AND CHOICE error
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418 FAIRNESS AND JUSTICE the idea o
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420 FAIRNESS AND JUSTICE subject ha
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422 FAIRNESS AND JUSTICE to maximiz
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424 FAIRNESS AND JUSTICE people for
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426 FAIRNESS AND JUSTICE We can thu
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428 FAIRNESS AND JUSTICE subjects a
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430 FAIRNESS AND JUSTICE great deal
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432 FAIRNESS AND JUSTICE but not ex
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434 FAIRNESS AND JUSTICE that the c
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436 FAIRNESS AND JUSTICE being, any
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438 FAIRNESS AND JUSTICE to take ou
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440 FAIRNESS AND JUSTICE Conclusion
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442 SOCIAL DILEMMAS: COOPERATION VE
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Chapter 19 Decisions about the futu
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THE CHOICE OF PERSONAL GOALS 473 Mo
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GOOD REASONS FOR STICKING TO PLANS
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BAD REASONS FOR STICKING TO PLANS:
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DISCOUNTING 479 Figure 19.1: Subjec
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DISCOUNTING 481 noted. If you have
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DISCOUNTING 483 The high subjective
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DISCOUNTING 485 in nine months, or
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DISCOUNTING 487 bly, in order to ge
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SELF-CONTROL 489 Why we need self-c
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SELF-CONTROL 491 offered the fourth
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ADAPTATION, CONTRAST, AND HEURISTIC
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MORALITY AND PRUDENCE 495 Morality
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Chapter 20 Risk Decisions under unc
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NORMATIVE THEORY 499 each individua
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NORMATIVE THEORY 501 example, to re
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RISK REGULATION AND THE INTUITIONS
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RISK REGULATION AND THE INTUITIONS
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OTHER BIASES IN RISK JUDGMENTS 507
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INSURANCE AND PROTECTIVE BEHAVIOR 5
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INVESTORS AND ENTREPRENEURS 519 abi
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INVESTORS AND ENTREPRENEURS 521 Con
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INDIVIDUAL AND SEX DIFFERENCES 523
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CONCLUSION 525 ficials thought that
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528 REFERENCES Alpert, W., & Raiffa
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530 REFERENCES Baron, J. (1997b). C
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532 REFERENCES Behn, R. D., & Vaupe
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534 REFERENCES Social Psychology, 6
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536 REFERENCES Davis, W. A. (1981).
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538 REFERENCES Elster, J. (1983). S
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540 REFERENCES for osteoporosis: Wh
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542 REFERENCES Harris, R. J., & Joy
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544 REFERENCES Oregon did. Journal
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546 REFERENCES Kahneman, D., & Tver
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548 REFERENCES Kuhn, D., Phelps, E.
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550 REFERENCES Lopes, L. L. (1987a)
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552 REFERENCES Mischel, W., Shoda,
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554 REFERENCES Parks, C. D., & Vu,
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556 REFERENCES Reuben, D. B. (1984)
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558 REFERENCES Schwartz, S. M., Bar
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560 REFERENCES Solnick, J. V., Kann
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562 REFERENCES 51, 176-197. Toulmin
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564 REFERENCES Walster, E., Walster
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Author Index Abelson, R. P., 148 Ab
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AUTHOR INDEX 569 Dawes, R. M., 156,
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AUTHOR INDEX 571 Huber, P. W., 519
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AUTHOR INDEX 573 Miyamoto, J. M., 3
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AUTHOR INDEX 575 Simon, H. A., 37,
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Subject Index 2 4 6 problem, 172 ab
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SUBJECT INDEX 579 culture, 428, 451
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SUBJECT INDEX 581 invariance, 116,
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SUBJECT INDEX 583 quantitative thin
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