Using Models to Persuade Joshua Schwartzstein Adi Sunderam NBER Working Paper No. 26109

Issued in July 2019, Revised in May 2020

NBER Program(s):Asset Pricing, Corporate Finance, Industrial Organization

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. You may purchase this paper on-line in .pdf format from SSRN.com ($5) for electronic delivery. Access to NBER Papers You are eligible for a free download if you are a subscriber, a corporate associate of the NBER, a journalist, an employee of the U.S. federal government with a ".GOV" domain name, or a resident of nearly any developing country or transition economy. If you usually get free papers at work/university but do not at home, you can either connect to your work VPN or proxy (if any) or elect to have a link to the paper emailed to your work email address below. The email address must be connected to a subscribing college, university, or other subscribing institution. Gmail and other free email addresses will not have access. E-mail:

Acknowledgments Machine-readable bibliographic record - MARC, RIS, BibTeX Document Object Identifier (DOI): 10.3386/w26109