Noise : A Flaw in Human Judgment

Olivier Sibony , Daniel Kahneman


In Circulation

The Sunday Times bestseller‘A monumental, gripping book … Outstanding’ Sunday TimesWherever there is human judgement, there is noise.‘Noise may be the most important book I've read in more than a decade. A genuinely new idea so exceedingly important you will immediately put it into practice. A masterpiece’Angela Duckworth, author of Grit‘An absolutely brilliant investigation of a massive societal problem that has been hiding in plain sight’Steven Levitt, co-author of FreakonomicsFrom the world-leaders in strategic thinking and the multi-million copy bestselling authors of Thinking Fast and Slow and Nudge, the next big book to change the way you think.

Imagine that two doctors in the same city give different diagnoses to identical patients – or that two judges in the same court give different sentences to people who have committed matching crimes. Now imagine that the same doctor and the same judge make different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday, or they haven’t yet had lunch.

These are examples of noise: variability in judgements that should be identical.In Noise, Daniel Kahneman, Olivier Sibony and Cass R. Sunstein show how noise produces errors in many fields, including in medicine, law, public health, economic forecasting, forensic science, child protection, creative strategy, performance review and hiring. And although noise can be found wherever people are making judgements and decisions, individuals and organizations alike commonly ignore its impact, at great cost.

Packed with new ideas, and drawing on the same kind of sharp analysis and breadth of case study that made Thinking, Fast and Slow and Nudge international bestsellers, Noise explains how and why humans are so susceptible to noise and bias in decision-making. We all make bad judgements more than we think. With a few simple remedies, this groundbreaking book explores what we can do to make better ones.

What will you learn from this book

  1. Noise vs. Bias: The book distinguishes between bias (systematic errors) and noise (random variability) in human judgment. While biases have received more attention, noise can be equally problematic and lead to inconsistent decisions.

  2. Judgment Errors: Human judgment is susceptible to noise, resulting in decisions that can vary widely even when faced with the same information.

  3. Noise in Decision-Making: The authors explore various domains where noise can affect decision-making, including legal judgments, medical diagnoses, and financial assessments.

  4. Impact of Noise: Noise can have significant consequences, leading to suboptimal decisions, increased variability, and inefficiencies in various processes.

  5. Decision-Making Processes: The book discusses how organizational processes and decision-making systems can contribute to noise and provides insights into reducing it.

  6. Calibration and Training: The authors suggest methods such as calibration and training to improve decision-makers' consistency and reduce the impact of noise.

  7. Role of Algorithms: Algorithms and decision aids are explored as tools to mitigate noise, providing a more systematic and consistent approach to decision-making.

  8. Importance of Feedback: Regular feedback is highlighted as a crucial element in reducing noise, allowing decision-makers to learn from their mistakes and refine their judgment.

  9. Group Decision-Making: The authors discuss how group decision-making processes can either amplify or mitigate noise, depending on factors such as group dynamics and communication.

  10. Practical Strategies: The book provides practical strategies for organizations and individuals to identify and reduce noise in decision-making processes, leading to more accurate and consistent outcomes.

Language English
ISBN-10 0008309000
ISBN-13 9780008309008
No of pages 464
Font Size Medium
Book Publisher William Collins
Published Date 01 Jun 2021

About Author

Author : Daniel Kahneman

1 Books

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