Growing a Bayesian Conspiracy Theorist: An Agent-Based Model

Conspiracy theories cover topics from politicians to world events. Frequently, proponents of conspiracies hold these beliefs strongly despite available evidence that may challenge or disprove them. Therefore, conspiratorial reasoning has often been described as illegitimate or flawed. In the paper, we explore the possibility of growing a rational (Bayesian) conspiracy theorist through an Agent-Based Model. The agent has reasonable constraints on access to the total information as well its access to the global population. The model shows that network structures are central to maintain objectively mistaken beliefs. Increasing the size of the available network yielded increased confidence in mistaken beliefs and subsequent network pruning, allowing for belief purism. Rather than ameliorating and correcting mistaken beliefs (where agents move toward the correct mean), large networks appear to maintain and strengthen them. As such, large networks may increase the potential for belief polarization, extreme beliefs, and conspiratorial thinking – even amongst Bayesian agents.