Individual Decision-Making in Coupled Agent-Environment Systems

Understanding how individual decisions shape environmental outcomes is critical as climate targets grow more ambitious. Traditional mean-field and two-strategy frameworks overlook individual-level cognition, dynamic risk perception, and evolving social norms. We develop a spatially explicit agent-based model (ABM) in which heterogeneous agents choose between climate-friendly and degradative actions via adaptive utility functions integrating environmental preference, social pressure, and ecosystem feedback, augmented with sub-models for dynamic risk-perception, shifting-baseline effects, and memory-based spillovers. Complemented by a mean-field approximation, phase plots and clustering analysis reveal critical tipping points, multi-stability, oscillatory behavior, and scale-free cluster formation across rationality and adaptation-speed regimes—dynamics absent from the mean-field model. These results identify the cognitive and social thresholds that determine whether socio-ecological systems collapse or recover, offering a basis for designing targeted, climate-positive interventions.