Modeling the synergistic relation between social learning and sustainable behaviors in the Italian city of Siena

Promoting sustainable behaviors is a central component of climate-change mitigation and can be framed as a large-scale social dilemma, where individual incentives may conflict with collective benefits. How do urban interaction structures and social-learning mechanisms shape the persistence of environmentally sustainable behaviors among people who live in or regularly frequent a city? In this paper, we use agent-based modeling calibrated with real data and game theory to investigate how different network topologies and an environmental coupling mechanism affect synergistically sustainable behavior in a heterogeneous population composed of different types of players. We analyze four model configurations, combining an idealized regular setting and a georeferenced representation of Siena with geographical and small-world interaction networks. We find that stronger coupling to the local social environment is related to sustainable behavior in all cases. However, the Siena geographical configuration remains the least cooperative, suggesting that its urban structure and the strongly interconnected social structure, may limit the diffusion of cooperation and sustainable behavior. Finally, fixed-strategy agents appear elements reducing the system’s ability to self-organize into cooperative configurations.