Judgment under Uncertainty · Heuristics and Biases by Kahneman, Daniel -- Read -- Imperial Library of Trantor

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Index

Title Page Copyright Page Contents List of contributors Preface Part I: Introduction

1 Judgment under uncertainty: Heuristics and biases

Part II: Representativeness

2 Belief in the law of small numbers 3 Subjective probability: A judgment of representativeness 4 On the psychology of prediction 5 Studies of representativeness 6 Judgments of and by representativeness

Part III: Causality and attribution

7 Popular induction: Information is not necessarily informative 8 Causal schemas in judgments under uncertainty 9 Shortcomings in the attribution process: On the origins and maintenance of erroneous social assessments 10 Evidential impact of base rates

Part IV: Availxability

11 Availability: A heuristic for judging frequency and probability 12 Egocentric biases in availability and attribution 13 The availability bias in social perception and interaction 14 The simulation heuristic

Part V: Covariation and control

15 Informal covariation assessment: Data-based versus theory-based judgments 16 The illusion of control 17 Test results are what you think they are 18 Probabilistic reasoning in clinical medicine: Problems and opportunities 19 Learning from experience and suboptimal rules in decision making

Part VI: Overconfidence

20 Overconfidence in case-study judgments 21 A progress report on the training of probability assessors 22 Calibration of probabilities: The state of the art to 1980 23 For those condemned to study the past: Heuristics and biases in hindsight

Part VII: Multistage evaluation

24 Evaluation of compound probabilities in sequential choice 25 Conservatism in human information processing 26 The best-guess hypothesis in multistage inference 27 Inferences of personal characteristics on the basis of information retrieved from one’s memory

Part VIII: Corrective procedures

28 The robust beauty of improper linear models in decision making 29 The vitality of mythical numbers 30 Intuitive prediction: Biases and corrective procedures 31 Debiasing 32 Improving inductive inference

Part IX: Risk perception

33 Facts versus fears: Understanding perceived risk

Part X: Postscript

34 On the study of statistical intuitions 35 Variants of uncertainty

References Index

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