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Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation

Author

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  • Konstantinos Papageorgiou
  • Pramod K Singh
  • Elpiniki I Papageorgiou
  • Harpalsinh Chudasama
  • Dionysios Bochtis
  • George Stamoulis

Abstract

Participatory modelling is an emerging approach in the decision-making process through which stakeholders contribute to the representation of the perceived causal linkages of a complex system. The use of fuzzy cognitive maps (FCMs) for participatory modelling helps policy-makers develop dynamic quantitative models for strategising development interventions. The aggregation of knowledge from multiple stakeholders provides consolidated and more reliable results. Average aggregation is the most common aggregation method used in FCMs-based modelling for weighted interconnections between concepts. This paper proposes a new aggregation method using learning OWA (ordered weighted averaging) operators for aggregating FCM weights assigned by various stakeholders. Besides, we report a comparative analysis of ‘OWA learning aggregation’ with the conventional average aggregation method, while evaluating the theory of change for the world’s most extensive poverty alleviation programme in India. The results of the FCMWizard web-based tool show that the proposed method provides an opportunity to policy-makers for evaluating outcomes of proposed policies while addressing social resilience and economic mobility.

Suggested Citation

  • Konstantinos Papageorgiou & Pramod K Singh & Elpiniki I Papageorgiou & Harpalsinh Chudasama & Dionysios Bochtis & George Stamoulis, 2020. "Participatory modelling for poverty alleviation using fuzzy cognitive maps and OWA learning aggregation," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    • Handle: RePEc:plo:pone00:0233984
    DOI: 10.1371/journal.pone.0233984
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    References listed on IDEAS

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    1. Konstantinos Papageorgiou & Pramod K. Singh & Elpiniki Papageorgiou & Harpalsinh Chudasama & Dionysis Bochtis & George Stamoulis, 2019. "Fuzzy Cognitive Map-Based Sustainable Socio-Economic Development Planning for Rural Communities," Sustainability, MDPI, vol. 12(1), pages 1-31, December.
    2. Pramod Singh & Abhishek Nair, 2014. "Livelihood vulnerability assessment to climate variability and change using fuzzy cognitive mapping approach," Climatic Change, Springer, vol. 127(3), pages 475-491, December.
    3. Solana-Gutiérrez, Joaquín & Rincón, Gonzalo & Alonso, Carlos & García-de-Jalón, Diego, 2017. "Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla River basin, Spain," Ecological Modelling, Elsevier, vol. 360(C), pages 260-269.
    4. Alexandra S Penn & Christopher J K Knight & David J B Lloyd & Daniele Avitabile & Kasper Kok & Frank Schiller & Amy Woodward & Angela Druckman & Lauren Basson, 2013. "Participatory Development and Analysis of a Fuzzy Cognitive Map of the Establishment of a Bio-Based Economy in the Humber Region," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-14, November.
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    9. Pramod K. Singh & Konstantinos Papageorgiou & Harpalsinh Chudasama & Elpiniki I. Papageorgiou, 2019. "Evaluating the Effectiveness of Climate Change Adaptations in the World’s Largest Mangrove Ecosystem," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
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    11. Alipour, M. & Hafezi, R. & Amer, M. & Akhavan, A.N., 2017. "A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post–sanction period," Energy, Elsevier, vol. 135(C), pages 851-864.
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    1. Espinoza Bustos, Hector & Speckesser, Stefan, 2022. "A comparison of earnings related to higher technical and academic education," LSE Research Online Documents on Economics 113739, London School of Economics and Political Science, LSE Library.

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