Thinking and Deciding

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EXPECTED-UTILITY THEORY 241 are still sufficiently great that .01·u(being saved)+.99·u(the small inconvenience of living a Christian life if God does not exist) is greater than .01 · u(being damned)+ .99 · u(a normal life). Another way of looking at this is to say that the utility difference between being saved and being damned is so great that it is worthwhile to live the Christian life, even if the probability of God’s existence is very small. We can use the difference between outcomes to determine expected utility. When we compare two options, we can compare them within each column in a utility table. We then multiply the difference between the two utilities by the probability of the state. The following table shows this in a general form, with two options, A and B. Here, the expected utility of option A is and the utility of option B is State of the world Option 1 2 3 A uA (1) uA (2) uA (3) B uB (1) uB (2) uB (3) EUA = p(1)uA (1) + p(2)uA (2) + p(3)uA (3) EUB = p(1)uB (1) + p(2)uB (2) + p(3)uB (3) so the difference between the two utilities is EUA −EUB = p(1)[uA (1)−uB (1)]+p(2)[uA (2)−uB (2)]+p(3)[uA (3)−uB (3)] (10.3) It is the difference that determines which option is better. If the difference is positive, option A is better, and, if it is negative, B is better. You can see here why it does not matter which outcome is selected as the zero point. If we add a constant to everything in the same column, that constant gets multiplied by the probability and then added to the utilities of both options, and the difference between them is unchanged. For example, if we add C to both uA (3) and uB (3), then the rightmost term in equation 10.3 becomes p(3)[(uA (3) + C) − (uB (3) + C)], and the Cs cancel out to give back the same term that was there before. Naively, we tend to think of some outcomes as being favorable and others as unfavorable in themselves; however, outcomes are all relative. Thus, a well-known decision theorist, asked, “How’s your wife?” answered, “Compared to what?” The units do not matter either, provided that the same units are used throughout the whole table. Multiplying everything in the table by the same constant (and thus
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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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Page 488: Part III DECISIONS AND PLANS
Page 496: Chapter 10 Normative theory of choi
Page 500: NORMATIVE THEORY OF CHOICE 235 We s
Page 504: EXPECTED-UTILITY THEORY 237 that pr
Page 508: EXPECTED-UTILITY THEORY 239 Table 1
Page 514: 242 NORMATIVE THEORY OF CHOICE UNDE
Page 546: 258 DESCRIPTIVE THEORY OF CHOICE UN
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266 DESCRIPTIVE THEORY OF CHOICE UN
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Chapter 12 Choice under certainty T
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PROMINENCE AND NONCOMPENSATORY STRA
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OTHER REVERSALS: COMPATIBILITY AND
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EFFECTS OF THE OPTIONS AVAILABLE ON
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MENTAL ACCOUNTING 297 think about t
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MENTAL ACCOUNTING 299 jects may be
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MENTAL ACCOUNTING 301 When we evalu
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MENTAL ACCOUNTING 303 a promotion f
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MENTAL ACCOUNTING 305 The extra-cos
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MENTAL ACCOUNTING 307 Mr. Munn and
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CONCLUSION 309 Most of these heuris
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312 UTILITY MEASUREMENT Most of the
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314 UTILITY MEASUREMENT dollar. Hig
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316 UTILITY MEASUREMENT My answer i
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318 UTILITY MEASUREMENT we can mean
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320 UTILITY MEASUREMENT Simple dire
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322 UTILITY MEASUREMENT I could con
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324 UTILITY MEASUREMENT be useful f
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326 UTILITY MEASUREMENT Of course t
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328 UTILITY MEASUREMENT is equivale
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330 UTILITY MEASUREMENT their healt
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332 UTILITY MEASUREMENT money as 0
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334 UTILITY MEASUREMENT really deci
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336 UTILITY MEASUREMENT pay about t
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338 UTILITY MEASUREMENT from SGs. I
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340 UTILITY MEASUREMENT at maximizi
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342 DECISION ANALYSIS AND VALUES ot
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344 DECISION ANALYSIS AND VALUES Ke
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346 DECISION ANALYSIS AND VALUES of
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348 DECISION ANALYSIS AND VALUES de
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350 DECISION ANALYSIS AND VALUES Ta
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352 DECISION ANALYSIS AND VALUES It
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354 DECISION ANALYSIS AND VALUES Ta
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356 DECISION ANALYSIS AND VALUES si
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358 DECISION ANALYSIS AND VALUES Th
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360 DECISION ANALYSIS AND VALUES of
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Chapter 15 Quantitative judgment Qu
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THE LENS MODEL 365 Table 15.1: Data
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THE LENS MODEL 367 In addition to t
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THE LENS MODEL 369 (von Winterfeldt
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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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