Definition of Fuzzy Conditions in the Model of Dynamic Collective Decision-Making in Emergency Evacuation Tasks in Fuzzy Conditions

Abstract

Quantitative assessment in collective behavior and decision-making in fuzzy conditions is crucial for ensuring the health and safety of the population, ensuring effective response to various emergencies. The task of modeling and predicting behavior in fuzzy conditions, as is known, has increased complexity due to many factors from which an NP-complete multi-criteria task is formed. There is a difficulty in determining the quantitative assessment of the influence of fuzzy factors using a mathematical model. The paper proposes a stochastic model of human decision-making to describe the empirical behavior of subjects in an experiment simulating an emergency scenario. The developed fuzzy model combines fuzzy logic into a conventional model of social behavior. Unlike existing models and applications, this approach uses fuzzy sets and membership functions to describe the evacuation process in an emergency. The purpose of this work is to define fuzzy rules and analyze existing solutions. The scientific novelty lies in the formation of a set of factors that form fuzzy rules for making dynamic decisions. The problem statement in this paper is as follows: to form a set of factors affecting the behavior of pedestrians, which are modeled as fuzzy input data. The practical value of the work lies in the creation of a new set of fuzzy rules that allow them to be used in the evacuation algorithm [2] for the effective solution of the task. The fundamental difference from the known approaches is in the application of a new set of fuzzy rules, which contains factors: perception, intention, attitude. To implement the proposed model, the process of social behavior during evacuation, independent variables are determined. These variables include measurements related to social factors, in other words, the behavior of individual subjects and individual small groups, which are of fundamental importance at the early stage of evacuation. The results of modeling the proposed models of decision-making in fuzzy conditions are carried out, quantifying the degree of optimality of human decisions, and determining the conditions under which optimal or quasi-optimal decisions are made. Modeling has shown acceptable results of the proposed approach in solving the problem of evacuation in emergency situations in fuzzy conditions.