Integrating Perception-Based and Data-Driven Knowledge to Support Policy-Making in Social-Ecological Systems: A Fuzzy Cognitive Mapping Approach

Sara Mehryar, R.V. Sliuzas, D. Reckien, Mohammed Ali Sharifi, M.F.A.M. van Maarseveen

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In modelling social-ecological systems (SESs) social and ecological variables interact, implying that both subjective and objective data are crucial, complementary and need to be integrated to enable a full understanding of the system for policy making purposes. Fuzzy Cognitive Mapping (FCM) is a well-known participatory modelling method that uses stakeholders’ perceptions to build semi-quantified models. We develop a FCM model for policy option analysis in a SES by combining two types of knowledge from formal objective data and stakeholders’ perceptions. This model is focussed on the issue of water scarcity in Rafsanjan, Iran. It includes many social and ecological variables, and allows the impact of different policy options on the system to be simulated. The simulation results of the mixed-FCM have been compared with those of a standard, perceived FCM (P-FCM), of the same SES. The results show that when simulating policies with direct impact on data-driven concepts, the mixed-FCM produces substantially different results from those of a P-FCM, thereby showing the benefit of this approach in such settings. Yet, for policies with direct impact on perceived concepts both mixed-FCM and P-FCM produce similar simulation results, which support continued use of P-FCM for these type of policies. Therefore, a mixed-FCM is useful for the study of SESs in which part of environmental changes are gradual and invisible to stakeholders’ direct observation e.g. groundwater level change. Whereas, for the study of environmental changes that can be perceived immediately by stakeholders over relatively short time frames, a P-FCM would suffice.
Original languageEnglish
Number of pages19
JournalRegional environmental change
Publication statusSubmitted - 2018

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policy making
stakeholder
environmental change
policy analysis
modeling
simulation
groundwater
policy

Keywords

  • Participatory modelling
  • Social-ecological systems
  • Policy Making
  • Fuzzy Cognitive Mapping
  • Water Scarcity
  • UT-Hybrid-D
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

@article{796b4b7fd7f248a1939a4e72da8fd129,
title = "Integrating Perception-Based and Data-Driven Knowledge to Support Policy-Making in Social-Ecological Systems: A Fuzzy Cognitive Mapping Approach",
abstract = "In modelling social-ecological systems (SESs) social and ecological variables interact, implying that both subjective and objective data are crucial, complementary and need to be integrated to enable a full understanding of the system for policy making purposes. Fuzzy Cognitive Mapping (FCM) is a well-known participatory modelling method that uses stakeholders’ perceptions to build semi-quantified models. We develop a FCM model for policy option analysis in a SES by combining two types of knowledge from formal objective data and stakeholders’ perceptions. This model is focussed on the issue of water scarcity in Rafsanjan, Iran. It includes many social and ecological variables, and allows the impact of different policy options on the system to be simulated. The simulation results of the mixed-FCM have been compared with those of a standard, perceived FCM (P-FCM), of the same SES. The results show that when simulating policies with direct impact on data-driven concepts, the mixed-FCM produces substantially different results from those of a P-FCM, thereby showing the benefit of this approach in such settings. Yet, for policies with direct impact on perceived concepts both mixed-FCM and P-FCM produce similar simulation results, which support continued use of P-FCM for these type of policies. Therefore, a mixed-FCM is useful for the study of SESs in which part of environmental changes are gradual and invisible to stakeholders’ direct observation e.g. groundwater level change. Whereas, for the study of environmental changes that can be perceived immediately by stakeholders over relatively short time frames, a P-FCM would suffice.",
keywords = "Participatory modelling, Social-ecological systems, Policy Making, Fuzzy Cognitive Mapping, Water Scarcity, UT-Hybrid-D, ITC-ISI-JOURNAL-ARTICLE",
author = "Sara Mehryar and R.V. Sliuzas and D. Reckien and Sharifi, {Mohammed Ali} and {van Maarseveen}, M.F.A.M.",
year = "2018",
language = "English",
journal = "Regional environmental change",
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TY - JOUR

T1 - Integrating Perception-Based and Data-Driven Knowledge to Support Policy-Making in Social-Ecological Systems

T2 - A Fuzzy Cognitive Mapping Approach

AU - Mehryar, Sara

AU - Sliuzas, R.V.

AU - Reckien, D.

AU - Sharifi, Mohammed Ali

AU - van Maarseveen, M.F.A.M.

PY - 2018

Y1 - 2018

N2 - In modelling social-ecological systems (SESs) social and ecological variables interact, implying that both subjective and objective data are crucial, complementary and need to be integrated to enable a full understanding of the system for policy making purposes. Fuzzy Cognitive Mapping (FCM) is a well-known participatory modelling method that uses stakeholders’ perceptions to build semi-quantified models. We develop a FCM model for policy option analysis in a SES by combining two types of knowledge from formal objective data and stakeholders’ perceptions. This model is focussed on the issue of water scarcity in Rafsanjan, Iran. It includes many social and ecological variables, and allows the impact of different policy options on the system to be simulated. The simulation results of the mixed-FCM have been compared with those of a standard, perceived FCM (P-FCM), of the same SES. The results show that when simulating policies with direct impact on data-driven concepts, the mixed-FCM produces substantially different results from those of a P-FCM, thereby showing the benefit of this approach in such settings. Yet, for policies with direct impact on perceived concepts both mixed-FCM and P-FCM produce similar simulation results, which support continued use of P-FCM for these type of policies. Therefore, a mixed-FCM is useful for the study of SESs in which part of environmental changes are gradual and invisible to stakeholders’ direct observation e.g. groundwater level change. Whereas, for the study of environmental changes that can be perceived immediately by stakeholders over relatively short time frames, a P-FCM would suffice.

AB - In modelling social-ecological systems (SESs) social and ecological variables interact, implying that both subjective and objective data are crucial, complementary and need to be integrated to enable a full understanding of the system for policy making purposes. Fuzzy Cognitive Mapping (FCM) is a well-known participatory modelling method that uses stakeholders’ perceptions to build semi-quantified models. We develop a FCM model for policy option analysis in a SES by combining two types of knowledge from formal objective data and stakeholders’ perceptions. This model is focussed on the issue of water scarcity in Rafsanjan, Iran. It includes many social and ecological variables, and allows the impact of different policy options on the system to be simulated. The simulation results of the mixed-FCM have been compared with those of a standard, perceived FCM (P-FCM), of the same SES. The results show that when simulating policies with direct impact on data-driven concepts, the mixed-FCM produces substantially different results from those of a P-FCM, thereby showing the benefit of this approach in such settings. Yet, for policies with direct impact on perceived concepts both mixed-FCM and P-FCM produce similar simulation results, which support continued use of P-FCM for these type of policies. Therefore, a mixed-FCM is useful for the study of SESs in which part of environmental changes are gradual and invisible to stakeholders’ direct observation e.g. groundwater level change. Whereas, for the study of environmental changes that can be perceived immediately by stakeholders over relatively short time frames, a P-FCM would suffice.

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KW - Social-ecological systems

KW - Policy Making

KW - Fuzzy Cognitive Mapping

KW - Water Scarcity

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M3 - Article

JO - Regional environmental change

JF - Regional environmental change

SN - 1436-3798

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