Integration of AI and agent-based modeling for simulating human-ecological systems

Abstract

This study investigates the integration of Artificial Intelligence (AI) and Agent-Based Modeling (ABM) for simulating human-ecological systems, aiming to enhance our understanding of complex system dynamics and inform evidence-based decision-making in environmental management and policy development. The research methodology combines computational modeling techniques with data visualization approaches to analyze simulation results and performance metrics comprehensively. The simulation of human-ecological systems utilizes Python programming language and the NumPy library to incorporate AI-enhanced decision-making within an ABM framework. Model performance metrics such as accuracy, precision, recall, and F1 score are computed to evaluate the effectiveness of the integrated approach. Additionally, simulation results and performance metrics are visualized using the Matplotlib library to facilitate interpretation and communication of research findings. The results demonstrate the initial spatial distribution of agents within the human-ecological system, the emergence of uniform and localized clusters of agent activity over subsequent simulation steps, and the strengths and weaknesses associated with the integrated AI-ABM approach. Overall, this study contributes to advancing research in environmental science and sustainability by providing insights into the capabilities and limitations of AI-enhanced ABM models for simulating human-ecological systems.