Modeling the Dynamics of Belief in Climate Change with Statistical Physics

We simulate the dynamics of belief in climate change using agents on a social network who update their beliefs according to a classical XY Model Hamiltonian. The initial conditions of the model are set by contemporary empirical insights in the social distribution of climate beliefs (Hornsey et al. 2016). The social network dynamics are inspired by dispersion rules proposed by Galesic and Stein (2019) but extend with additional social and environmental rules. Environmental effects include extreme weather events and disasters. The frequency, spatial-distribution, and intensity of extreme weather events are calibrated with real world data on climate impacts (Siscoet al. 2017). The model enables us to analyse the interactions between social belief dynamics in networks and climate impacts and suggest when and if a consensus about climate change will form in the wake of extreme climate-related weather events.