{"id":48842,"date":"2019-11-15T12:30:56","date_gmt":"2019-11-15T12:30:56","guid":{"rendered":"https:\/\/www.lse.ac.uk\/granthaminstitute\/?post_type=publication&p=48842"},"modified":"2020-03-10T09:28:45","modified_gmt":"2020-03-10T09:28:45","slug":"from-individual-fuzzy-cognitive-maps-to-agent-based-models-modeling-multi-factorial-and-multi-stakeholder-decision-making-for-water-scarcity","status":"publish","type":"publication","link":"https:\/\/www.lse.ac.uk\/granthaminstitute\/publication\/from-individual-fuzzy-cognitive-maps-to-agent-based-models-modeling-multi-factorial-and-multi-stakeholder-decision-making-for-water-scarcity\/","title":{"rendered":"From individual Fuzzy Cognitive Maps to Agent Based Models: Modeling multi-factorial and multi-stakeholder decision-making for water scarcity"},"content":{"rendered":"\n

Abstract<\/h2>\n
\n

Policy making for complex (SESs) is a multi-factorial 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 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.<\/p>\n

Sara Mehryar, Richard Sliuzas, Nina Schwarz, Ali Sharifi, Martin van Maarseveen. In:<\/strong> Journal of Environmental Management, Volume 250, 2019, 109482, ISSN 0301-4797,<\/strong><\/em>
\nhttps:\/\/doi.org\/10.1016\/j.jenvman.2019.109482.<\/strong><\/em><\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"\n

Abstract Policy making for complex (SESs) is a multi-factorial and multi-stakeholder decision making process. 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