Addressing Data Limitations to Explore Management Strategies and Adaptations Using Stylized Agent-Based Modeling: A Case Study of Socio-Environmental Systems

This research employs a stylized agent-based model to investigate the complex interactions between human activities and environmental processes, focusing on the Lake Urmia Basin. A key contribution of this study is promoting the application of stylized ABMs to tackle socio-environmental challenges, especially when nonlinearity and multifaceted dynamics make it difficult to compile comprehensive data for developing management strategies. The primary objective of the model is to simplify these intricate dynamics, making it easier to explore key drivers of water resource depletion and system resilience. By abstracting the system, the model allows examination of various scenarios—such as improvements in irrigation efficiency and crop management strategies—without the need for detailed real-world data. Over 100 agricultural years (timesteps), the model simulates agricultural impacts on lake water area, irrigation practices, and farmer decision-making, highlighting critical feedback loops between human behavior and environmental outcomes. The benefits of this stylized modeling approach include its flexibility in testing hypothetical scenarios and its capacity to isolate key variables that influence the dynamics of human-environment systems. This simplicity allows researchers to identify broad patterns of behavior and system responses, making it an effective tool for evaluating water management strategies. The results show that while increasing irrigation efficiency and reducing water-intensive crops can delay system collapse, the most successful scenario requires both interventions at their highest levels. However, a more practical approach involving moderate improvements in both areas could significantly enhance the system's resilience. This research emphasizes the advantages of stylized ABMs in supporting sustainable water management practices.