Decision Support for Management in Emergency Situations based on the Construction of Functional, Information and Dynamic Models

In recent years, there has been a global surge in the occurrence and magnitude of natural and man-made emergencies, necessitating precise modeling and prediction of behavior in uncertain conditions. However, the inherent complexity of this task is compounded by a multitude of factors, resulting in a multi-criteria problem that is classified as NP-complete. Decision support in emergency management encounters significant challenges due to the inherent uncertainties and doubts that surround such situations. To address these challenges, this study proposes a stochastic model of human decision-making that incorporates the uncertainties and doubts expressed by experts. The developed model integrates fuzzy logic into a conventional model of social behavior, presenting a novel approach to describing the evacuation process during emergency situations. Unlike existing models and applications, this approach employs fuzzy sets and membership functions to capture and represent the inherent vagueness and uncertainty associated with the evacuation process. The primary objective of this research is to address the pressing scientific and technical problem of providing support for managerial decision-making in emergency situations. This involves the development of methodological and theoretical foundations that incorporate cognitive and dynamic models, and the application of the obtained insights to tackle practical challenges related to emergency prevention and mitigation. Research findings enhance emergency management strategies, improving prevention and mitigation of consequences. By considering social factors and uncertainties in decision-making, this study contributes to a comprehensive understanding of the evacuation process and facilitates effective emergency management.