Fuzzy-Cognitive Maps for Forecasting of Socio-Economic Indicators

EasyChair Preprint 7216

Abstract

In this paper, cognitive modeling methods were investigated. As a result of the study, it was concluded that the fuzzy cognitive map model is most suitable for solving the forecasting problem. Approaches to the construction of subjective models of the situation are investigated, problems solved with the help of fuzzy cognitive maps are investigated. To support decision-making in poorly structured dynamic situations, the methodology of cognitive modeling is used, based on the construction of a subjective model of the situation, reflecting the subject's knowledge of the laws of its development. The subjective model of the situation is constructed in an expert way and is presented in the form of an oriented sign graph (cognitive map), in which the vertices are the factors of the situation, and the weighted arcs are the cause-and-effect relationships, the weight of which reflects the strength of the influence of the factors of the situation.

Keyphrases: Artificial Intelligence, Convolutional Neural Network, deep learning, explanational artificial intelligence, machine learning, neural networks, rule extraction

@booklet{EasyChair:7216,
  author    = {Alexey Averkin and Sergey Yarushev},
  title     = {Fuzzy-Cognitive Maps for Forecasting of Socio-Economic Indicators},
  howpublished = {EasyChair Preprint 7216},
  year      = {EasyChair, 2021}}