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Overinference from Weak Signals and Underinference from Strong Signals

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  • Ned Augenblick
  • Eben Lazarus
  • Michael Thaler

Abstract

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.

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  • Ned Augenblick & Eben Lazarus & Michael Thaler, 2021. "Overinference from Weak Signals and Underinference from Strong Signals," Papers 2109.09871, arXiv.org, revised Jun 2024.
    • Handle: RePEc:arx:papers:2109.09871
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    References listed on IDEAS

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    Cited by:

    1. Luca Braghieri, 2023. "Biased Decoding and the Foundations of Communication," CESifo Working Paper Series 10432, CESifo.
    2. Mel Win Khaw & Ziang Li & Michael Woodford, 2022. "Cognitive Imprecision and Stake-Dependent Risk Attitudes," CESifo Working Paper Series 9923, CESifo.
    3. Charlotte Cordes & Jana Friedrichsen & Simeon Schudy, 2023. "Motivated Procrastination," Rationality and Competition Discussion Paper Series 471, CRC TRR 190 Rationality and Competition.
    4. Larry G Epstein & Kaushil Patel, 2024. "Identifying Heterogeneous Decision Rules From Choices When Menus Are Unobserved," Papers 2405.09500, arXiv.org.
    5. Philipp Denter & Boris Ginzburg, 2024. "Troll Farms," Papers 2411.03241, arXiv.org.

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