Discerning media bias within a network of political allies and opponents: the idealized example of a biased coin

TL;DR

This paper models how perceptions of media bias are formed through a Bayesian network of political agents, revealing complex dynamics like false convergence, turbulence, and intermittency in belief updates.

Contribution

It introduces a Bayesian framework for modeling media bias perception among agents with conflicting interactions, capturing uncertainty and antagonism effects.

Findings

Agents can quickly converge to incorrect beliefs due to antagonistic interactions.

Unbalanced networks often exhibit nonconvergent, turbulent belief dynamics.

Long-term intermittency occurs in belief updates within certain network structures.

Abstract

Perceptions of political bias in the media are formed directly, through the independent consumption of the published outputs of a media organization, and indirectly, through observing the collective responses of political allies and opponents to the same published outputs. A network of Bayesian learners is constructed to model this system, in which the bias perceived by each agent obeys a probability density function, which is updated according to Bayes's theorem given data about the published outputs and the beliefs of the agent's political allies and opponents. The Bayesian framework allows for uncertain beliefs, multimodal probability distribution functions, and antagonistic interactions with opponents, not just cooperation with allies. Numerical simulations are performed to test the idealized example of inferring the bias of a coin. It is found that some agents converge on the wrong conclusion faster than others converge on the right conclusion under a surprisingly broad range of conditions, when antagonistic interactions are present which "lock out" some agents from the truth, e.g. in Barab\'asi-Albert networks. It is also found that structurally unbalanced networks routinely experience turbulent nonconvergence, where some agents fail to achieve a steady-state belief, e.g. when they are allies of two agents who are opponents themselves. The subtle phenomenon of long-term intermittency is also explored.