Using Models to Persuade

89 Pages Posted: 2 Aug 2019 Last revised: 4 Jun 2021

Date Written: July 2019


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

Schwartzstein, Joshua and Sunderam, Aditya, Using Models to Persuade (July 2019). NBER Working Paper No. w26109, Available at SSRN:

Joshua Schwartzstein (Contact Author)

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Aditya Sunderam

Harvard University ( email )

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