From Aggregated Knowledge to Interactive Agents: an Agent Based Approach to Support Policy Making in Social-Ecological Systems

Policy making for complex Social-Ecological Systems (SESs) is a multi-variable and multi-stakeholder decision making process. Therefore, proper policy simulation in a SES should consider both the complex behavior of the system and the multi-stakeholders’ interventions into the system, which requires integrated methodological approaches. In this study, we present an integrated modeling methodology combining an Agent Based Model (ABM) with Fuzzy Cognitive Mapping (FCM) to simulate impacts of policy options in SESs. First, the relations among environmental variables and behavioral rules of stakeholders are captured with FCM that is developed with both qualitative and quantitative data, i.e. stakeholders’ knowledge and empirical data from studies. Then, ABM is used to simulate the dynamic interaction of stakeholders and their impact on environmental variables. Our approach covers four main aspects of complex SESs, crucial for policy simulation purposes: 1) causal relationships, 2) feedback mechanism, 3) social-spatial heterogeneity and 4) time scale. We apply the proposed methodology in the case study of a farming community facing water scarcity in Rafsanjan, Iran, and simulate the impact of different policy options compared to the baseline scenario. Results show that a policy of facilitating people participation in management and control of their groundwater use has the highest impact in reducing overall groundwater use as well as securing farmers’ activities in Rafsanjan.