ABMIND: An empirically informed agent-based model of psychological distance and environmental protection behaviour

Understanding environmental protection behaviour in social–ecological systems requires models that represent not only economic incentives but also individuals’ cognitive processes related to ecological risk, future benefits, and social influence. However, many agent-based models still simplify human decision-making as static preferences or cost–benefit calculations, leaving cognitive mechanisms such as psychological distance insufficiently represented. To address this gap, we propose ABMIND, the Agent-Based Model of Individual Psychological Distance, an empirically informed agent-based model that formalises psychological distance as a dynamic state variable linking uncertainty, social interaction, ecological feedback, and environmental behaviour. Using household survey data from coastal farming communities in Guangxi, China, we initialised heterogeneous farmer agents, parameterised behavioural mechanisms, and provided empirical support for key cognitive pathways. We applied ABMIND to simulate mangrove conservation outcomes under eight policy scenarios, including rewards, penalties, publicity guidance, and their combinations. The results show that, within the current model setting, reward-based intervention most effectively shortens temporal distance and improves ecological outcomes; publicity guidance primarily reduces social distance and promotes protection behaviour; and policy mixes do not necessarily produce synergistic effects, as higher levels of protection behaviour do not always lead to better ecological recovery. ABMIND provides a transferable modelling framework for embedding cognitive–behavioural mechanisms into agent-based environmental models and for evaluating policy interventions in complex social–ecological systems.