From Aggregated Knowledge to Collective Behaviour; A Participatory Policy Analysis Methodology

Climate change adaptation includes activities and interventions attempting to reduce the vulnerability of social and ecological sub-systems to changes of temperature, rainfall, sea level, etc. Decision making and policy analysis for such interventions in the Social-Ecological Systems (SESs) is a multi-factorial and multi-stakeholder decision making process. This means, a proper policy analysis should consider both the dynamic behavior of the system’s social and ecological elements and the impacts of the stakeholders’ interventions on the system, which requires integrated methodological approaches. In this study, we simulate impacts of policy options on a farming community facing water scarcity in Rafsanjan, Iran, using an integrated modeling methodology combining an Agent Based Model (ABM) with Fuzzy Cognitive Mapping (FCM). First, the behavioral rules of farmers and the causal relations among environmental variables are captured with FCMs that are developed with both qualitative and quantitative data, i.e. farmers' knowledge and empirical data from studies. Then, an ABM is developed to model decisions and actions of farmers and simulate their impacts on overall groundwater use and emigration of farmers in this case study. Finally, the impacts of different policy options are simulated and compared with a baseline scenario. The results suggest that a policy of facilitating farmers' participation in management and control of their groundwater use leads to the highest reduction of groundwater use and would help to secure farmers’ activities in Rafsanjan. Our approach covers four main aspects that are crucial for policy simulation in SESs: 1) causal relationships, 2) feedback mechanisms, 3) social-spatial heterogeneity and 4) temporal dynamics. This approach is particularly useful for ex-ante policy options analysis.