Dynamic Detection and Classification of Critical Attention Objects under Crisis Events

Abstract

This article presents the development of a universal methodology for selecting and classifying Critical Objects of Attention (COAs) during crisis events, replacing static, standardized approaches with a dynamic, substantiated model. The authors propose formalizing criticality as an emergent property of the “world–governance–observer” system, where criticality is determined not by an object’s intrinsic attributes, but by its role within crisis dynamics. Leveraging graph theory, information theory, and models of cognitive salience, a phase space of attention is constructed, equipped with a dynamic criticality function κ(o, t) and an attentional energy functional L, enabling optimal selection of a compact subset of COAs. A five-stage methodology – DCSC (Dynamic Criticality Selection & Classification) – is introduced, implemented, and validated on a simulated cyberattack scenario. The model is unsupervised, interoperable with existing monitoring systems (e.g., SIEM, digital twins), and applicable across domains including cybersecurity, critical infrastructure management, and digital public governance.