In this paper, a new approach to multi-criteria decision making is proposed based on linguistic information taken from a group of autonomous experts. This approach provides an opportunity to better analyze and find solutions for poorly structured problems with consideration of their multidimensionality and uncertainty of context. One of the key components of the proposed methodology is the hierarchy of abstractions proposed by John van Gigch, which presents the levels of alternative solutions and criteria for assessing them. By integrating this hierarchy, it is claimed that the problem situation can be comprehensively analyzed. Therefore, we call our approach multi-level multi-attribute linguistic decision making (ML–MA–LDM).
      Our approach includes a methodology that is the particular sequence of steps and the mathematical model, as well as the method to automatically distribute weights of experts’ assessments depending on their confidence level. Furthermore, this novel approach supports both qualitative and quantitative assessments that are strictly propagated through the complete decision making process across all hierarchical levels of abstraction. Finally, we demonstrate a prototype of a multi-agent expert system for solving poorly structured models with regard to their context uncertainty and multiple aspects. This prototype plays the role of simulation engine for competitive solutions and for verification purposes of the proposed methodology.
      Capabilities of the developed approach and the prototype were demonstrated in a practical case of solving a complex conflict problem of strategic management, as well as rigorous analysis of the proposed approach strengths and weakness that defines the direction for further research.