摘要
ABSESpy is a novel agent-based modeling (ABM) framework that enhances socio-ecological systems (SES) research fidelity. Addressing critical needs in SES study, such as complex decision-making, scaling, and data integration, it features a Branch-Leaf architecture for clear separation and integration of human and natural subsystems, promoting replicability and model coupling. ABSESpy also supports modeling human behavior through well-recognized workflows of perception, decision-making definitions, and responses. Moreover, it advances real-world modeling with multiple time operating modes, accommodating the diverse temporal scales of SES phenomena and integrating time-sensitive event simulations. These innovations position ABSESpy as a crucial tool in addressing current gaps in SES research, fostering more ABMs for real-world SES issues.
