The study addresses the problem of integrating cognitive and mathematical modeling in research on the development directions of the fuel and energy complex, taking into account energy security requirements. The relevance of the work is due to the fact that in the existing two-level research methodology, the transition from the results of qualitative analysis using cognitive modeling to the parameters of the mathematical model is largely performed manually, which reduces the reproducibility of numerical experiments and limits the efficiency of accumulated knowledge usage. The aim of the work is to develop a software component that ensures the combined use of cognitive and mathematical models within an Energy Knowledge Ecosystem. A software component is proposed, implemented as part of the INTEC‑SAW suite, which provides the transformation of changes in the cognitive model into the parameters of the economic-mathematical model, as well as the reverse interpretation of calculation results. Technology for conducting numerical experiments has been developed, including the construction of semantic (ontological and cognitive) models, formation of computational scenarios, execution of optimization calculations, and presentation of results, distinguished by the automation of the joint use of ontological, cognitive, and economic-mathematical models. To account for uncertainty, a numerical method of stochastic parameter adjustment based on cognitive weights is proposed. The effectiveness of the approach is demonstrated through a numerical experiment investigating the impact of CO₂ emission constraints on the energy balances of the Siberian Federal District. The practical significance of the work lies in increasing the validity and reproducibility of research on the development of the fuel and energy complex through the coordinated use of qualitative and quantitative analysis tools.

The paper proposes an approach to multi-criteria decision support based on a cognitively oriented multi-agent information-analytical system. Cognitive modeling methods are developed, including a formal ontological representation of knowledge about production planning and a coalition–holonic agent architecture that ensures adaptability and transparency of computations. A hybrid evolutionary multi-criteria algorithm is introduced, in which agents generate alternative plans at the local level using a parallel genetic algorithm that optimizes a combination of several criteria. At the global level, a multi-stage selection of alternatives is implemented with filtering of resource overloads and similar solutions, followed by final aggregation using the PROMETHEE and ELECTRE multi-criteria decision-making methods.

An experimental study is carried out comparing manual planning with planning supported by the developed system, as well as analyzing the impact of dynamic adaptation of the genetic algorithm parameters. The results show that the use of the system makes it possible to reduce plan generation time by a factor of 20–30 while maintaining or improving solution quality. At the same time, resource overloads are completely eliminated, and early termination of evolutionary computations is ensured without loss of solution quality. The system and proposed algorithms are intended for use in planning project activities at manufacturing enterprises.

The article investigates cognitive load in users of a multi-role 3D platform. An experiment (71 participants: 34 designers, 37 customers; 639 observations) using objective metrics and subjective scales revealed different load profiles: designers are slowed down by data entry forms, while customers are hindered by 3D content. Based on 34 problem areas, seven interface design principles and a task classification by cognitive complexity level, accounting for role-specific factors, were formulated. Retesting (21 participants) confirmed their effectiveness: task completion time decreased by 21–28%, errors by 33–44%, frustration by 25–35%, and the use of 3D features increased from 40.5% to 90.9%. The developed three-component model (taxonomic, diagnostic, and design levels) is recommended for designing similar multi-role platforms with 3D functionality.