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Using Models to Persuade


  • Joshua Schwartzstein
  • Adi Sunderam


We present a framework for analyzing “model persuasion.” Persuaders influence receivers’ beliefs by proposing models (likelihood functions) that specify how to organize past data (e.g., on investment performance) to make predictions (e.g., about future returns). Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs over states of the world. Model persuaders face a key tradeoff: models that better fit the data given receivers’ prior beliefs induce less movement in receivers’ beliefs. This tradeoff means that a receiver exposed to the true model can be most misled by persuasion when that model fits poorly—for instance when there is a lot of data that exhibits randomness. In such cases, a wrong model often wins because it provides a better fit. Similarly, competition between persuaders tends to neutralize the data because it pushes towards models that provide overly good fits and therefore do not move receivers’ beliefs much. The fit-movement tradeoff depends on receiver characteristics, so with multiple receivers a persuader is more effective when he can send tailored, private messages. We illustrate with examples from finance, business, politics, and law.

Suggested Citation

  • Joshua Schwartzstein & Adi Sunderam, 2019. "Using Models to Persuade," NBER Working Papers 26109, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26109
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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. Kevin He & Jonathan Libgober, 2020. "Evolutionarily Stable (Mis)specifications: Theory and Applications," Papers 2012.15007,, revised Aug 2022.
    4. José Luis Montiel Olea & Pietro Ortoleva & Mallesh Pai & Andrea Prat, 2021. "Competing Models," Working Papers 2021-89, Princeton University. Economics Department..
    5. Pycia, Marek & Troyan, Peter, 2019. "A Theory of Simplicity in Games and Mechanism Design," CEPR Discussion Papers 14043, C.E.P.R. Discussion Papers.
    6. Eliaz, Kfir & Spiegler, Ran & Thysen, Heidi C., 2021. "Persuasion with endogenous misspecified beliefs," European Economic Review, Elsevier, vol. 134(C).
    7. Cuimin Ba, 2021. "Robust Model Misspecification and Paradigm Shifts," Papers 2106.12727,, revised Mar 2022.
    8. 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.
    9. 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.
    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:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G4 - Financial Economics - - Behavioral Finance
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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