A revised cognitive mapping methodology for modeling and simulation

Gonzalo Nápoles

doi:10.1016/j.knosys.2024.112089

2024-09-01

PRE

Gonzalo Nápoles

Isel Grau

Yamisleydi Salgueiro *

DOI：10.1016/j.knosys.2024.112089

原文链接

原文求助

摘要

En 中文

Fuzzy Cognitive Maps (FCMs) hold promise as a mathematical tool for modeling and simulating complex systems due to their transparency, flexibility to operate on prior knowledge structures and recurrent reasoning characteristics. However, they suffer from significant shortcomings that have prevented them from being more widely used. Some of these issues include discrepancies in component interpretation, saturation of neural concepts, arbitrary nonlinearities, and dynamic behaviors that are difficult to align with the problem domain. By integrating theoretical advances with practical needs, this paper proposes a revised modeling and simulation methodology termed neural cognitive mapping that addresses these issues holistically. Firstly, we redefine concepts' activation values in terms of changes rather than absolute values, ensuring a unified interpretation of the model's components. Secondly, we propose a parameterized activation function, called exponential normalized activator , which allows experts to control the neurons' nonlinearities while avoiding saturation states. Furthermore, we provide a twofold reasoning rule that simultaneously computes the concepts' changes and the amounts of resources attached to problem variables. Thirdly, we introduce a framework for interpreting simulation results across various dynamic behaviors, including scenarios with unique fixed-point attractors. The simulations using both real-world case studies and synthetically generated data illustrate the superiority of our proposal compared with the traditional approach in terms of clarity, usefulness, consistency, and controllability. Moreover, the empirical studies opened new research directions to be explored in future research.

Keyword:

Fuzzy cognitive maps

Complex systems

Modeling and simulation

Recurrent neural networks