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Noise : a flaw in human judgment
Daniel Kahneman, Olivier Sibony, Cass R. Sunstein.
Kahneman, Daniel, 1934- author
New York : Little, Brown Spark, 2021.
First edition. Book Print
Where to find it
FORD LIBRARY — STACKS
BF448 .K35 2021
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PERKINS & BOSTOCK LIBRARY — NEW & NOTEWORTHY
BF448 .K35 2021
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Authors, etc.
Kahneman, Daniel, 1934-, author
Sibony, Olivier, author
Sunstein, Cass R., author
Summary
From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones--"a tour de force" ( New York Times ).



Imagine that two doctors in the same city give different diagnoses to identical patients--or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants--or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.



In Noise , Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions.



Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment--and what we can do about it.
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Contents
Introduction: Two Kinds of Error p. 3
Part I Finding Noise p. 11
1 Crime and Noisy Punishment p. 13
2 A Noisy System p. 23
3 Singular Decisions p. 34
Part II Your Mind Is a Measuring Instrument p. 39
4 Matters of Judgment p. 43
5 Measuring Error p. 55
6 The Analysis of Noise p. 69
7 Occasion Noise p. 79
8 How Groups Amplify Noise p. 94
Part III Noise in Predictive Judgments p. 107
9 Judgments and Models p. 111
10 Noiseless Rules p. 123
11 Objective Ignorance p. 137
12 The Valley of the Normal p. 148
Part IV How Noise Happens p. 159
13 Heuristics, Biases, and Noise p. 161
14 The Matching Operation p. 176
15 Scales p. 187
16 Patterns p. 200
17 The Sources of Noise p. 210
Part V Improving Judgments p. 221
18 Better Judges for Better Judgments p. 225
19 Debiasing and Decision Hygiene p. 236
20 Sequencing Information in Forensic Science p. 245
21 Selection and Aggregation in Forecasting p. 259
22 Guidelines in Medicine p. 273
23 Defining the Scale in Performance Ratings p. 287
24 Structure in Hiring p. 300
25 The Mediating Assessments Protocol p. 312
Part VI Optimal Noise p. 325
26 The Costs of Noise Reduction p. 329
27 Dignity p. 339
28 Rules or Standards? p. 350
Review and Conclusion: Taking Noise Seriously p. 361
Epilogue: A Less Noisy World p. 377
Appendix A How to Conduct a Noise Audit p. 379
Appendix B A Checklist for a Decision Observer p. 387
Appendix C Correcting Predictions p. 391
Acknowledgments p. 397
Notes p. 399
Index p. 439
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Related subjects
Decision making
Reasoning (Psychology)
Judgment
Cognitive styles
PSYCHOLOGY / Applied Psychology
PSYCHOLOGY / Cognitive Psychology & Cognition
PSYCHOLOGY / Social Psychology
Other details
Published
New York : Little, Brown Spark, 2021.
©2021
Language
English
Notes
Includes bibliographical references and index.
Description
ix, 454 pages : illustrations ; 25 cm
Genre or Form
Self-help publications
ISBN
9780316451406
0316451401
OCLC Number
1249942231
Other Identifiers
LCCN: 2020951207
System ID
010061332
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