Thinking and Deciding

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262 DESCRIPTIVE THEORY OF CHOICE UNDER UNCERTAINTY utility of Option 1 must be higher than that of Option 2, so, from Table 11.3, .01 u($1, 000) + .10 u($1, 000) >.01 u($0) + .10 u($5, 000) because the term .89 u($1, 000) may be dropped from each side. Exactly the opposite inequality, .01 u($1, 000) + .10 u($1, 000)
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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
Page 504: EXPECTED-UTILITY THEORY 237 that pr
Page 508: EXPECTED-UTILITY THEORY 239 Table 1
Page 512: EXPECTED-UTILITY THEORY 241 are sti
Page 516: WHY EXPECTED-UTILITY THEORY IS NORM
Page 532: THE UTILITY OF MONEY 251 we evaluat
Page 536: THE UTILITY OF MONEY 253 Figure 10.
Page 540: CONCLUSION 255 7. On the basis of y
Page 544: Chapter 11 Descriptive theory of ch
Page 548: BIAS IN DECISIONS UNDER UNCERTAINTY
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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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