Belief polarization in a complex world: A learning theory perspective

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.

Automated summary

The two models of belief formation suggest that even with almost identical sources of information, polarized beliefs can still arise. In one model, people form deterministic functions that fit their past data best, while in the other model, individuals pay a cost based on the complexity of the belief function, leading to disagreements even with large training sets drawn from the same distribution.