Group Decision-Making Techniques in Multi-Criteria Environments
Summary
Group decision-making in multi-criteria settings addresses the challenge of reconciling diverse expert judgements across several evaluation dimensions. Core methods such as the Analytic Hierarchy Process, Analytic Network Process and other multicriteria decision analysis frameworks structure complex problems into hierarchies or networks of criteria and alternatives. Individual assessments are collected through pairwise comparisons or scoring functions, then synthesised via aggregation operators. Traditional aggregation approaches include the aggregation of individual judgments and the aggregation of individual priorities, each carrying distinct assumptions about consensus and consistency. Recent advances introduce optimisation, Bayesian updating and compositional data techniques to improve coherence and compatibility while respecting the integrity of original inputs. These developments enhance robustness in contexts ranging from environmental management and public health prioritisation to strategic investment and credit risk analysis. Practical applications demonstrate that refined aggregation and consistency-enforcement procedures yield more reliable collective priorities, bolster stakeholder commitment and facilitate transparent, reproducible outcomes at a global scale.
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Group Decision-Making Techniques in Multi-Criteria Environments publication trend
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Technical terms
Multi-criteria decision analysis (MCDA): A structured framework for evaluating alternatives against multiple, often conflicting criteria.
Analytic Hierarchy Process (AHP): A prioritisation method that uses pairwise comparisons to derive ratio-scale weights for criteria or alternatives.
Aggregation: The procedure by which individual decision-maker preferences are mathematically combined into a collective ranking or priority vector.
Consensus: The degree of agreement among decision-makers’ judgments or priorities.
Consistency: A measure of logical coherence in pairwise comparisons, indicating the reliability of elicited preferences.
Compositional data analysis: Statistical techniques tailored for data, such as priority weights, that are constrained by constant-sum requirements.
References
- Reducing incompatibility in a local AHP-group decision making context. Annals of Operations Research (2023).
- Group Aggregation Techniques for Analytic Hierarchy Process and Analytic Network Process: A Comparative Analysis. Group Decision and Negotiation (2015).
- Unveiling and Unraveling Aggregation and Dispersion Fallacies in Group MCDM. Group Decision and Negotiation (2023).
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