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Using Models to Persuade


  • Joshua Schwartzstein
  • Adi Sunderam


We present a framework where "model persuaders" influence receivers' beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers' prior beliefs. Model persuaders face a trade-off: better-fitting models induce less movement in receivers' beliefs. Consequently, a receiver exposed to the true model can be most misled by persuasion when that model fits poorly, competition between persuaders tends to neutralize the data by pushing toward better-fitting models, and a persuader facing multiple receivers is more effective when he can send tailored, private messages.

Suggested Citation

  • Joshua Schwartzstein & Adi Sunderam, 2021. "Using Models to Persuade," American Economic Review, American Economic Association, vol. 111(1), pages 276-323, January.
  • Handle: RePEc:aea:aecrev:v:111:y:2021:i:1:p:276-323
    DOI: 10.1257/aer.20191074

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    References listed on IDEAS

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    Cited by:

    1. Sören Harrs & Lara Marie Müller & Bettina Rockenbach, 2021. "How Optimistic and Pessimistic Narratives about COVID-19 Impact Economic Behavior," ECONtribute Discussion Papers Series 091, University of Bonn and University of Cologne, Germany.
    2. Christina Korting & Carl Lieberman & Jordan Matsudaira & Zhuan Pei & Yi Shen, 2020. "Visual Inference and Graphical Representation in Regression Discontinuity Designs," Working Papers 638, Princeton University, Department of Economics, Industrial Relations Section..
    3. Eliaz, Kfir & Spiegler, Ran & Thysen, Heidi C., 2021. "Persuasion with endogenous misspecified beliefs," European Economic Review, Elsevier, vol. 134(C).
    4. Cuimin Ba, 2021. "Robust Model Misspecification and Paradigm Shifts," Papers 2106.12727,, revised Mar 2022.
    5. Kevin He & Jonathan Libgober, 2020. "Evolutionarily Stable (Mis)specifications: Theory and Applications," Papers 2012.15007,, revised Aug 2022.
    6. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    7. Mira Frick & Ryota Iijima & Yuhta Ishii, 2021. "Welfare Comparisons for Biased Learning," Cowles Foundation Discussion Papers 2274R, Cowles Foundation for Research in Economics, Yale University, revised Mar 2021.
    8. Müller, Lara Marie & Harrs, Sören & Rockenbach, Bettina, 2022. "How Narratives Impact Financial Behavior - Experimental Evidence from the COVID-19 Pandemic," VfS Annual Conference 2022 (Basel): Big Data in Economics 264089, Verein für Socialpolitik / German Economic Association.
    9. Pycia, Marek & Troyan, Peter, 2019. "A Theory of Simplicity in Games and Mechanism Design," CEPR Discussion Papers 14043, C.E.P.R. Discussion Papers.
    10. Fudenberg, Drew & Lanzani, Giacomo & Strack, Philipp, 0. "Pathwise concentration bounds for Bayesian beliefs," Theoretical Economics, Econometric Society.
    11. Colo, Philippe, 2021. "Expert-based Knowledge: Communicating over Scientific Models," MPRA Paper 110434, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness


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