Mental model evolution in social networks
This paper investigates the impact of different mental models on the formation of belief clusters and the spread of opinions within social networks, using an agent-based model. Every behaviour can be grouped into two categories: fundamental and emergent behaviours. A fundamental behaviour is a property of an agent that is independent of other properties or agents, while emergent behaviours are caused by the interactions of fundamental or other emergent behaviours. This paper studies fundamental behaviours to understand which ones are better to explain the emergent behaviours that are empirically observed. In our simulations, agents interact within a randomly generated network and observe signals about whether climate change is real or is a conspiracy. Then, they use various mental models to update their beliefs, where mental models are defined as functions which agents employ to update their beliefs in response to new information. We create three mental models: a Bayesian updating rule which rational agents use, a heuristic-based updating rule which boundedly rational agents use, and a Dirac-Von Neumann rule which agents who can be irrational use. Furthermore, we use state-of-the-art random networks to understand how community structures respond to these mental models, how clusters are formed to represent conspiracy theory groups, and in which models conspiracy theory groups can grow by convincing rational agents. We aim to reveal which belief update methodologies tend to create more realistic clusters in which polarizations arise due to agents being resistant to novel information. Moreover, we explore if rational agents can end up believing in conspiracy theories under any community structure. This paper is the first one to study multiple belief update methodologies in social networks using agent-based modelling, allowing us to reveal novel behaviours of belief diffusion.