About

Ned Augenblick holds the Edward J. and Mollie Arnold Chair in Business Administration and is an Associate Professor in the Economic Analysis and Policy Group at the Haas School of Business, UC Berkeley. His research focuses on behavioral economics, which involves integrating psychological insights into economic models to better understand deviations from rational decision-making. By exploring these deviations through theoretical models, experimental data, and empirical environments—ranging from online markets to voting booths and stock markets—he aims to produce more accurate predictions and policy recommendations. Augenblick's work has been published in top economics journals and discussed in prominent outlets such as the Financial Times, the New York Times, and the Atlantic. He has also taught core strategy courses at Berkeley MBA programs, combining game theory with behavioral economics to help executives make thoughtful decisions that foster sustainable competitive advantage.

Research topics

• Computer Science
• Statistics
• Mathematics
• Artificial Intelligence
• Machine Learning
• Social psychology
• Cognitive science
• Cognitive psychology
• Econometrics
• Biology
• Medicine
• Internal medicine
• Psychology
• Environmental health

Selected publications

• Excess Movement in Option-Implied Beliefs

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Overinference from Weak Signals and Underinference from Strong Signals

  The Quarterly Journal of Economics · 2024-10-14 · 33 citations

  articleOpen access1st authorCorresponding

  Abstract When people receive new information, sometimes they revise their beliefs too much, and sometimes too little. We show that a key driver of whether people overinfer or underinfer is the strength of the information. Based on a model in which people know which direction to update in, but not exactly how much to update, we hypothesize that people will overinfer from weak signals and underinfer from strong signals. We then test this hypothesis across four different environments: abstract experiments, a naturalistic experiment, sports betting markets, and financial markets. In each environment, our consistent and robust finding is overinference from weak signals and underinference from strong signals. Our framework and findings can help harmonize apparently contradictory results from the experimental and empirical literatures.

  PublisherOA PDFDOI

• The Large Number of Incompatible Choices Implied by Representing Risk Preference Through Curvature

  AEA Randomized Controlled Trials · 2023-10-10

  datasetSenior author
  PublisherDOI

• The Large Number of Incompatible Choices Implied by Representing Risk Preference Through Curvature

  AEA Randomized Controlled Trials · 2023-10-10

  datasetSenior author
  PublisherDOI

• The Large Number of Incompatible Choices Implied by Representing Risk Preference Through Curvature

  AEA Randomized Controlled Trials · 2023-10-10

  datasetSenior author
  PublisherDOI

• A New Test of Excess Movement in Asset Prices

  SSRN Electronic Journal · 2022-01-01 · 8 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Pooled testing efficiency increases with test frequency

  Proceedings of the National Academy of Sciences · 2022 · 9 citations

  1st authorCorresponding
  • Computer Science
  • Medicine
  • Statistics

  Frequent mass testing can slow a rapidly spreading infectious disease by quickly identifying and isolating infected individuals from the population. One proposed method to reduce the extremely high costs of this testing strategy is to employ pooled testing, in which samples are combined and tested together using one test, and the entire pool is cleared given a negative test result. This paper demonstrates that frequent pooled testing of individuals with correlated risk—even given large uncertainty about infection rates—is particularly efficient. We conclude that frequent pooled testing using natural groupings is a cost-effective way to suppress infection risk in a pandemic.

  DOI

• Overinference from Weak Signals and Underinference from Strong Signals

  SSRN Electronic Journal · 2022 · 42 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  PublisherDOI

• Overinference from Weak Signals and Underinference from Strong Signals

  arXiv (Cornell University) · 2021-09-20 · 1 citations

  preprintOpen access1st authorCorresponding

  When people receive new information, sometimes they revise their beliefs too much, and sometimes too little. In this paper, we show that a key driver of whether people overinfer or underinfer is the strength of the information. Based on a model in which people know which direction to update in, but not exactly how much to update, we hypothesize that people will overinfer from weak signals and underinfer from strong signals. We then test this hypothesis across four different environments: abstract experiments, a naturalistic experiment, sports betting markets, and financial markets. In each environment, our consistent and robust finding is overinference from weak signals and underinference from strong signals. Our framework and findings can help harmonize apparently contradictory results from the experimental and empirical literatures.

  PublisherOA PDFDOI

• Belief Movement, Uncertainty Reduction, and Rational Updating

  The Quarterly Journal of Economics · 2021 · 47 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  Abstract When a Bayesian learns new information and changes her beliefs, she must on average become concomitantly more certain about the state of the world. Consequently, it is rare for a Bayesian to frequently shift beliefs substantially while remaining relatively uncertain, or, conversely, become very confident with relatively little belief movement. We formalize this intuition by developing specific measures of movement and uncertainty reduction given a Bayesian’s changing beliefs over time, showing that these measures are equal in expectation and creating consequent statistical tests for Bayesianess. We then show connections between these two core concepts and four common psychological biases, suggesting that the test might be particularly good at detecting these biases. We provide support for this conclusion by simulating the performance of our test and other martingale tests. Finally, we apply our test to data sets of individual, algorithmic, and market beliefs.

  PublisherDOI

Frequent coauthors

• Jesse M. Cunha

  Naval Postgraduate School

  21 shared

• Ernesto Dal Bó

  19 shared

• Justin M. Rao

  19 shared

• Muriel Niederle

  15 shared

• Charles Sprenger

  14 shared

• Eben Lazarus

  University College London

  5 shared

• Michael Thaler

  3 shared

• Kristina Hallez

  Center for Effective Global Action

  3 shared

Education

• Ph.D., Economics

  University of California, Berkeley

  1998

• M.A., Economics

  University of California, Berkeley

  1994

• B.A., Economics

  University of California, Berkeley

  1991

Awards & honors

• Leonard W. and Shirley R. Ely Dissertation Fellowship (2009…
• George Shultz Fellowship Funding (Swoopo Project) (2009)
• Centennial TA Award: University-wide Annual Teaching Award (…
• George Shultz Fellowship Funding (Election Project) (2008)
• John M. Olin Law and Economics Program Fellowship (2006)