Social networks, confirmation bias and shock elections

TL;DR

This paper models how confirmation bias in social networks influences political polarization and the likelihood of shock election outcomes, highlighting the role of network structure and information availability.

Contribution

It introduces a model linking confirmation bias, social network learning, and election shocks, revealing effects on influence, polarization, and media extremism.

Findings

Confirmation bias slows societal learning in networks.

It increases societal polarization and influence disparities.

It raises the probability of shock election outcomes.

Abstract

In recent years online social networks have become increasingly prominent in political campaigns and, concurrently, several countries have experienced shock election outcomes. This paper proposes a model that links these two phenomena. In our set-up, the process of learning from others on a network is influenced by confirmation bias, i.e. the tendency to ignore contrary evidence and interpret it as consistent with one's own belief. When agents pay enough attention to themselves, confirmation bias leads to slower learning in any symmetric network, and it increases polarization in society. We identify a subset of agents that become more/less influential with confirmation bias. The socially optimal network structure depends critically on the information available to the social planner. When she cannot observe agents' beliefs, the optimal network is symmetric, vertex-transitive and has no self-loops. We explore the implications of these results for electoral outcomes and media markets. Confirmation bias increases the likelihood of shock elections, and it pushes fringe media to take a more extreme ideology.