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Principles of Simulations with FCMs

In: Fuzzy Cognitive Maps

Author

Listed:
  • Gonzalo Nápoles

    (Tilburg University, Department of Cognitive Science and Artificial Intelligence)

  • Philippe J. Giabbanelli

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

Abstract

In Chap. 1 , we defined FCMs as mathematical objects and explained that they could be used to model complex systems and simulate what-if scenarios. Although some software hide away these aspects, modelers who seek to build FCMs from the ground up are faced with numerous questions: how do I choose an update equation or activation function? How do I know whether my FCM has stabilized, and what influences these dynamics? In this chapter, we provide practical guidance on these core questions. By completing this chapter, readers will be able to (i) identify options and choose a solution for each aspect of the design; (ii) relate choices such as the update equation or activation function to the dynamics of the FCM; (iii) apply these concepts by programming an FCM in native Python.

Suggested Citation

  • Gonzalo Nápoles & Philippe J. Giabbanelli, 2024. "Principles of Simulations with FCMs," Springer Books, in: Philippe J. Giabbanelli & Gonzalo Nápoles (ed.), Fuzzy Cognitive Maps, chapter 0, pages 45-59, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-48963-1_3
    DOI: 10.1007/978-3-031-48963-1_3
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