Using Models to Persuade

Joshua Schwartzstein (jschwartzstein@hbs.edu) and Adi Sunderam

No 26109, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: G11 G4 L15 M3 (search for similar items in EconPapers)
Date: 2019-07
New Economics Papers: this item is included in nep-mic
Note: AP CF IO
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published as Joshua Schwartzstein & Adi Sunderam, 2021. "Using Models to Persuade," American Economic Review, American Economic Association, vol. 111(1), pages 276-323, January.

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Journal Article: Using Models to Persuade (2021)
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