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Analysis of Fuzzy Cognitive Maps

In: Fuzzy Cognitive Maps

Author

Listed:
  • Ryan Schuerkamp

    (Miami University, Department of Computer Science and Software Engineering)

  • Philippe J. Giabbanelli

    (Miami University, Department of Computer Science and Software Engineering)

Abstract

Structural analysis of Fuzzy Cognitive Maps leveraging techniques from network science and graph theory can answer several questions about the system of interest without performing simulations. This chapter focuses on widely used methods to answer two questions about FCMs and applies them to a guiding example from a real-world case study. First, what are the important concepts? We introduce transmitter, receiver, and ordinary concepts and five concept centrality measures (degree, betweenness, closeness, eigenvector, and Katz) to determine the critical concepts. Second, is the FCM facilitation (i.e., construction) process good? We define commonly used metrics (e.g., receiver-transmitter ratio and density) to assess the quality of FCM facilitation and support model comparison. Readers should be able to confidently analyze and compare FCMs after reading this chapter and completing its exercises.

Suggested Citation

  • Ryan Schuerkamp & Philippe J. Giabbanelli, 2024. "Analysis of Fuzzy Cognitive Maps," Springer Books, in: Philippe J. Giabbanelli & Gonzalo Nápoles (ed.), Fuzzy Cognitive Maps, chapter 0, pages 87-104, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-48963-1_5
    DOI: 10.1007/978-3-031-48963-1_5
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