Vasant Dhar and Daniel Kahneman on Noise: The Undesirable Variability within Decision Making

NYU Center for Data Science
2 min readOct 26, 2021

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Vasant Dhar, Professor of Information Systems at Stern and CDS associated faculty, hosts podcast A Brave New World.

Vasant Dhar, Professor of Information Systems at Stern and CDS associated faculty, invited Daniel Kahneman, winner of the Nobel Memorial Prize in Economic Sciences and co-author of Noise: A Flaw Within Human Judgement, to his podcast, A Brave New World. They sat down in a studio in New York to discuss why noise arises in human judgment and how we can address it.

When we think about noise, what might come to mind is ambulance sirens or jackhammers! When applied to human decision-making, Kahneman defines noise as the “undesirable variability in human judgment.” He describes why noise occurs in virtually all areas of life, including hiring, justice, healthcare, investing, and insurance. Equally importantly, he describes methods to measure the extent of noise with an organization and how to reduce it by following a simple protocol that breaks down decision-making into simple modular components that can be combined to make less noisy decisions. In contrast, machine-based decision-making is typically noise free, but has other drawbacks that fail to consider the nuances of “edge cases.” Dhar and Kahneman discuss such tradeoffs as they apply to various areas of our lives.

For more information on understanding noise, check out Vasant’s conversation with Daniel Kahneman on his podcast, A Brave New World.

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NYU Center for Data Science
NYU Center for Data Science

Written by NYU Center for Data Science

Official account of the Center for Data Science at NYU, home of the Undergraduate, Master’s, and Ph.D. programs in Data Science.

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