Book contents
- Making Decisions: Analytics, Cognition, and Application
- Making Decisions: Analytics, Cognition, and Application
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Preface
- Acknowledgments
- Copyright Acknowledgements
- Part I Analytics: Probability, Evaluation, Decision Trees, and Strategies for Decision Making
- Introduction to Part II: Cognitive Processes of Decision Making
- Part III Applications, Examples, and Selected Topics
- 10 Signal Detection Theory: How to Detect the Important Signal against the Distracting Background and Take Action as Appropriate Based on the Signal
- 11 Game Theory as Applied to Real-World Decisions
- 12 Decision Making under Stress: Staying Ahead of Rapidly Changing Events and Making Difficult, Time-Critical Decisions
- 13 Decision Making Involving Disputes, Negotiation, and Conflict
- 14 Multi-attribute Utility Decisions: Making Complex and Multidimensional Decisions
- 15 Common Decision Traps and How to Avoid Them
- 16 Overcoming Indecisiveness by Understanding What Causes It
- 17 Looking Back and Looking Ahead: What about Emotion, Culture, Artificial Intelligence, and Intuition?
- References
- Index
- References
10 - Signal Detection Theory: How to Detect the Important Signal against the Distracting Background and Take Action as Appropriate Based on the Signal
from Part III - Applications, Examples, and Selected Topics
Published online by Cambridge University Press: 23 October 2025
Book contents
- Making Decisions: Analytics, Cognition, and Application
- Making Decisions: Analytics, Cognition, and Application
- Copyright page
- Dedication
- Contents
- Figures
- Tables
- Preface
- Acknowledgments
- Copyright Acknowledgements
- Part I Analytics: Probability, Evaluation, Decision Trees, and Strategies for Decision Making
- Introduction to Part II: Cognitive Processes of Decision Making
- Part III Applications, Examples, and Selected Topics
- 10 Signal Detection Theory: How to Detect the Important Signal against the Distracting Background and Take Action as Appropriate Based on the Signal
- 11 Game Theory as Applied to Real-World Decisions
- 12 Decision Making under Stress: Staying Ahead of Rapidly Changing Events and Making Difficult, Time-Critical Decisions
- 13 Decision Making Involving Disputes, Negotiation, and Conflict
- 14 Multi-attribute Utility Decisions: Making Complex and Multidimensional Decisions
- 15 Common Decision Traps and How to Avoid Them
- 16 Overcoming Indecisiveness by Understanding What Causes It
- 17 Looking Back and Looking Ahead: What about Emotion, Culture, Artificial Intelligence, and Intuition?
- References
- Index
- References
Summary
How do people determine if they are hearing a faint sound or if they are hearing only background noise? How do people differentiate between important information and distractions? Signal detection theory was first developed as a model for understanding and interpreting electronic signals in early radar. The model was quickly adopted to explain people’s perception and sensation of light and sound.
Signal detection theory, with a …criterion or detection threshold that separates hits from misses and correct non-detections from false alarms… is a model for any decision-making setting that requires the decision maker to make a binary decision based on the presence or absence of the signal. Signal detection is explicitly understood and used in medical testing, fire detection, spam detectors, shoot/don’t shoot decisions by police and the military, psychophysics, artificial-intelligence algorithms, and in a variety of other settings.
Keywords
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- Making Decisions: Analytics, Cognition, and Application , pp. 127 - 146Publisher: Cambridge University PressPrint publication year: 2025
References
Recommended Reading
Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics (Vol. 1, pp. 1969–2012). Wiley.Google Scholar
Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown.Google Scholar
Silver, N. (2015). The signal and the noise: Why so many predictions fail – but some don’t. Penguin.Google Scholar
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