Unified Bayesian Frameworks for Multi-criteria Decision-making Problems
- URL: http://arxiv.org/abs/2208.13390v4
- Date: Wed, 6 Sep 2023 13:44:40 GMT
- Title: Unified Bayesian Frameworks for Multi-criteria Decision-making Problems
- Authors: Majid Mohammadi
- Abstract summary: This paper introduces Bayesian frameworks for tackling various aspects of multi-criteria decision-making (MCDM) problems.
The proposed frameworks offer statistically elegant solutions to key challenges in MCDM, such as group decision-making problems and criteria correlation.
- Score: 2.1833781995073416
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper introduces Bayesian frameworks for tackling various aspects of
multi-criteria decision-making (MCDM) problems, leveraging a probabilistic
interpretation of MCDM methods and challenges. By harnessing the flexibility of
Bayesian models, the proposed frameworks offer statistically elegant solutions
to key challenges in MCDM, such as group decision-making problems and criteria
correlation. Additionally, these models can accommodate diverse forms of
uncertainty in decision makers' (DMs) preferences, including normal and
triangular distributions, as well as interval preferences. To address
large-scale group MCDM scenarios, a probabilistic mixture model is developed,
enabling the identification of homogeneous subgroups of DMs. Furthermore, a
probabilistic ranking scheme is devised to assess the relative importance of
criteria and alternatives based on DM(s) preferences. Through experimentation
on various numerical examples, the proposed frameworks are validated,
demonstrating their effectiveness and highlighting their distinguishing
features in comparison to alternative methods.
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