CONCEPTUALIZING ADAPTIVE UNCERTAINTY MANAGEMENT IN INFORMATION TECHNOLOGY PROJECTS

One of the key challenges of contemporary project management in the information technology domain is coping with a high level of uncertainty. Classical methodologies rely predominantly on a probabilistic paradigm, which proves insufficient for innovative projects that lack a reliable statistical basis. In contrast, agile approaches often fail to provide a systematic formalization of risk and uncertainty management processes. Consequently, a pressing research and practical problem is the development of conceptual models that integrate classical and agile approaches with methods for uncertainty assessment. The purpose of this paper is to develop and formalize a conceptual model for adaptive uncertainty management in IT projects by providing a multi-perspective representation of the management system’s structure, processes, architecture, interaction dynamics, and state evolution. The methodological foundation of the study combines project management theory, fuzzy set theory, decision-making under uncertainty, systems analysis, and object-oriented modelling. The paper proposes a multi-level conceptual model of adaptive uncertainty management composed of interconnected subsystems: uncertainty identification and typology, an analytical subsystem with fuzzy-logic assessment, adaptive decision-making, an execution subsystem, monitoring and control, and organizational learning. The model is formalized through a set of complementary UML diagrams: a class diagram defines the structural organization and domain ontology; an activity diagram operationalizes the process workflow with iterative cycles and decision points; a component diagram specifies a multi-layer technical architecture of the supporting software system; a sequence diagram details the temporal dynamics of interactions in a critical-uncertainty scenario; and a state diagram represents the project life cycle as a finite-state machine with transitions conditioned by the level of uncertainty. The practical relevance of the results lies in the potential use of the developed model as a formal specification for designing intelligent decision-support systems and developing organizational procedures for uncertainty management.