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

Author

Listed:
  • 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.
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    4. Pramod K Singh & Harpalsinh Chudasama, 2020. "Evaluating poverty alleviation strategies in a developing country," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-23, January.
    5. Areti D. Kontogianni & Elpiniki Papageorgiou, 2012. "Using Fuzzy Cognitive Mapping in Environmental Decision Making and Management: A Methodological Primer and an Application," Chapters, in: Stephen Young & Steven Silvern (ed.), International Perspectives on Global Environmental Change, IntechOpen.
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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.
    10. 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.
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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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