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Decision-making under uncertainty – the integrated approach of the AHP and Bayesian analysis

Author

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  • Predrag Mimović
  • Jelena Stanković
  • Vesna Janković Milić

Abstract

In situations where it is necessary to perform a large number of experiments in order to collect adequate statistical data which require expert analysis and assessment, there is a need to define a model that will include and coordinate statistical data and experts’ opinions. This article points out the new integrated application of the Analytic Hierarchy Process (AHP) and Bayesian analysis, in the sense that the Bayes’ formula can improve the accuracy of input data for the Analytical Hierarchy Process, and vice versa, AHP can provide objectified inputs for the Bayesian formula in situations where the statistical estimates of probability are not possible. In this sense, the AHP can be considered as the Bayesian process that allows decision-makers to objectify their decisions and formalise the decision process through pairwise comparison of elements.

Suggested Citation

  • Predrag Mimović & Jelena Stanković & Vesna Janković Milić, 2015. "Decision-making under uncertainty – the integrated approach of the AHP and Bayesian analysis," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 28(1), pages 868-878, January.
    • Handle: RePEc:taf:reroxx:v:28:y:2015:i:1:p:868-878
    DOI: 10.1080/1331677X.2015.1092309
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    Cited by:

    1. Zewei Zhang & Qingjie Qi & Ye Cheng & Dawei Cui & Jinghu Yang, 2024. "An Integrated Model for Risk Assessment of Urban Road Collapse Based on China Accident Data," Sustainability, MDPI, vol. 16(5), pages 1-17, March.
    2. Helena Gaspars-Wieloch, 2024. "AHP based on scenarios and the optimism coefficient for new and risky projects: case of independent criteria," Annals of Operations Research, Springer, vol. 341(2), pages 937-961, October.

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