Belief polarization in a complex world: A learning theory perspective

Nika Haghtalab, Matthew Jackson and Ariel D. Procaccia
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Nika Haghtalab: Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720
Ariel D. Procaccia: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138

Proceedings of the National Academy of Sciences, 2021, vol. 118, issue 19, e2010144118

Abstract: We present two models of how people form beliefs that are based on machine learning theory. We illustrate how these models give insight into observed human phenomena by showing how polarized beliefs can arise even when people are exposed to almost identical sources of information. In our first model, people form beliefs that are deterministic functions that best fit their past data (training sets). In that model, their inability to form probabilistic beliefs can lead people to have opposing views even if their data are drawn from distributions that only slightly disagree. In the second model, people pay a cost that is increasing in the complexity of the function that represents their beliefs. In this second model, even with large training sets drawn from exactly the same distribution, agents can disagree substantially because they simplify the world along different dimensions. We discuss what these models of belief formation suggest for improving people’s accuracy and agreement.

Keywords: belief polarization; learning theory (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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