A New Approach for Handling Uncertainty of Expert Judgments in Complex Decision Problems

Decision-making in complex real-world scenarios requires careful analysis to manage conflicting criteria and optimize outcomes. Many decision support systems leverage expert knowledge to increase the quality of recommendations. However, relying solely on expert judgment can introduce biases and inaccuracies. Subjective criteria weighting methods attempt to quantify the importance of criteria, but often ignore the uncertainty associated with expert judgment. Their results are presented as a single vector of weights, which may not precisely reflect the actual problem. Highlights the information gap in current methodologies. To overcome this challenge, this paper proposes a novel approach that considers the partial or complete order of the criteria provided by experts to generate multiple weight vectors for the criteria. Enhances the robustness and reliability of decision recommendations by exploring possible weight distributions and examining decision variants in different scenarios. The proposed approach is compared with existing subjective weighting methods through illustrative examples and practical applications, demonstrating its efficacy in handling uncertainties and incomplete knowledge. The study fills a crucial information gap in decision support methodologies, offering a comprehensive approach to criteria weighting that improves decision-making outcomes in multi-criteria problems.