Probabilistic judgments specified partially in the analytic hierarchy process |
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| Institution: | 1. University of Enna “Kore”, viale delle Olimpiadi, 94100 Enna, Italy;2. Department of Statistical Sciences, University of Padova, via C. Battisti 241, 35121 Padova, Italy;3. Department of Economics, University of Pretoria, 0002 Pretoria, South Africa;1. DRM Finance, Université Paris Dauphine, Place du Maréchal de Lattre de Tassigny, 75775 Paris Cedex 16, France;2. IPAG Business School (IPAG Lab), 184 Boulevard Saint-Germain, 75006 Paris, France;3. Université Paris 8 (LED), 2 rue de la Liberté, 93526 Saint-Denis Cedex, France;1. Mechanical Engineering Department, Anhui Agricultural University, Hefei 230036, China;2. Department of Precision Machinery & Precision Instrumentation, University of Science & Technology of China, Hefei 230027, China;3. Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA;4. Mechanical Engineering Department, University of Johannesburg, Johannesburg 2006, South Africa;1. School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, China;2. Business School, Sichuan University, Chengdu, Sichuan 610064, China |
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| Abstract: | The Analytic Hierarchy Process (AHP) is a decision-making tool which yields priorities for decision alternatives. This paper proposes a new approach to elicit and synthesize expert assessments for the group decision process in the AHP. These new elicitations are given as partial probabilistic specifications of the entries of pairwise comparisons matrices. For a particular entry of the matrix, the partial probabilistic elicitations could arise in the form of either probability assignments regarding the chance of that entry falling in specified intervals or selected quantiles for that entry. A new class of models is introduced to provide methods for processing this partial probabilistic information. One advantage of this approach is that it allows to generate as many pairwise comparison matrices of the decision alternatives as one desires. This, in turn, allows us to determine the statistical significance of the priorities of decision alternatives. |
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