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Ned Augenblick

Ned Augenblick

· Associate Professor

University of California, Berkeley · Economic Analysis & Policy

Active 2004–2025

h-index13
Citations1.6k
Papers287 last 5y
Funding—

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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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

Selected publications

  • An Experiment on Time Preference and Misprediction in Unpleasant Tasks

    The Review of Economic Studies · 2018-05-10 · 162 citations

    article1st authorCorresponding

    We experimentally investigate the time-inconsistent taste for immediate gratification and future-preference misprediction. Across 7 weeks, 100 participants choose the number of unpleasant transcription tasks given various wages to complete immediately and at different future dates. Participants preferred 10–12% fewer tasks in the present compared to any future date, leading to an estimated $\beta $ of $0.83$. Comparing predictions with actual immediate-work choices provides evidence against subs…

    PublisherDOI

  • Belief Movement, Uncertainty Reduction, and Rational Updating

    The Quarterly Journal of Economics · 2021 · 47 citations

    1st authorCorresponding

    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,…

    PublisherDOI

  • Overinference from Weak Signals and Underinference from Strong Signals

    SSRN Electronic Journal · 2022 · 42 citations

    1st 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 expe…

    PublisherOA PDFDOI

  • Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning

    National Bureau of Economic Research · 2020-07-01 · 28 citations

    report1st authorCorresponding

    Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test.Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk.We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk.First, repeated testing mechanically lowers prevalence at the time of the next test.This increases…

    PublisherDOI

Frequent coauthors

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)

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