Unified Bayesian Frameworks for Multi-criteria Decision-making
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This paper presents a Bayesian framework predicated on a probabilistic interpretation of the MCDM problems and encompasses several well-known multi-criteria decision-making (MCDM) methods. Owing to the flexibility of Bayesian models, the proposed framework can address several long-standing, fundamental challenges in MCDM, including group decision-making problems and criteria correlation, in a statistically elegant way. Also, the model can accommodate different forms of uncertainty in the preferences of the decision makers (DMs), such as normal and triangular distributions and interval preferences. Further, a probabilistic mixture model is developed that can group the DMs into several exhaustive classes. A probabilistic ranking scheme is also designed for both criteria and alternatives, where it identifies the extent to which one criterion/alternative is more important than another based on the DM(s) preferences. The experiments validate the outcome of the proposed framework on several numerical examples and highlight its salient features compared to other methods.
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