Learning in a Post-Truth World

26 Pages Posted: 8 Jul 2021

See all articles by Mohamed Mostagir

Mohamed Mostagir

University of Michigan, Stephen M. Ross School of Business

James Siderius

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science

Date Written: July 2, 2021

Abstract

Misinformation has emerged as a major societal challenge in the wake of the 2016 U.S. elections, Brexit, and the COVID-19 pandemic. One of the most active areas of inquiry into misinformation examines how the cognitive sophistication of people impacts their ability to fall for misleading content. In this paper, we capture sophistication by studying how misinformation affects the two canonical models of the social learning literature: sophisticated (Bayesian) and naive (DeGroot) learning. We show that sophisticated agents can be more likely to fall for misinformation. Our model helps explain several experimental and empirical facts from cognitive science, psychology, and the social sciences. It also shows that the intuitions de- veloped in a vast social learning literature should be approached with caution when making policy decisions in the presence of misinformation. We conclude by discussing the relationship between misinformation and increased partisanship, and provide an example of how our model can inform the actions of policymakers trying to contain the spread of misinformation.

Keywords: social learning, (boundedly-)rational agents, misinformation

Suggested Citation

Mostagir, Mohamed and Siderius, James, Learning in a Post-Truth World (July 2, 2021). Available at SSRN: https://ssrn.com/abstract=3879174 or http://dx.doi.org/10.2139/ssrn.3879174

Mohamed Mostagir (Contact Author)

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States

James Siderius

Massachusetts Institute of Technology (MIT) - Electrical Engineering and Computer Science ( email )

77 Massachusetts Avenue
Cambridge, MA 02139-4307
United States

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