Cognitive biases can move opinion dynamics from consensus to signatures of transient chaos

TL;DR

This paper explores how cognitive biases like confirmation and in-group bias influence opinion dynamics, leading to phenomena such as consensus, polarization, and transient chaos, challenging previous models predicting fixed points.

Contribution

It introduces a Bayesian opinion model incorporating confirmation and in-group biases, revealing complex dynamics including transient chaos and ongoing opinion fluctuations.

Findings

Without biases, opinions always reach consensus.

Confirmation bias alone can cause polarization.

Both biases can lead to transient chaos and persistent opinion fluctuations.

Abstract

Interest in how democracies form consensus has increased recently, with statistical physics and economics approaches both suggesting that there is convergence to a fixed point in belief networks, but with fluctuations in opinions when there are ``stubborn'' voters. We modify a model of opinion dynamics in which agents are fully Bayesian to account for two cognitive biases: confirmation bias and in-group bias. Confirmation bias occurs when the received information is considered to be more likely when it aligns with the receiver's beliefs. In-group bias occurs when the receiver further considers the information to be more likely when the receiver's beliefs and the sender's beliefs are aligned. We find that when there are no cognitive biases, a network of agents always converges to complete consensus. With confirmation bias alone, polarization can occur. With both biases present, consensus and polarization are possible, but when agents attempt to counteract confirmation bias, there can be signatures of transient chaos and ongoing opinion fluctuations. Based on this simple model, we conjecture that complex opinion fluctuations might be a generic feature of opinion dynamics when agents are Bayesian with biases.