$\alpha$-Discounting Multi-Criteria Decision Making ($\alpha$-D MCDM)

TL;DR

This paper introduces the lpha-Discounting Method for Multi-Criteria Decision Making, an extension of AHP that adjusts preferences through parameters to handle consistency issues and non-linear preferences.

Contribution

It proposes a novel lpha-Discounting approach that generalizes AHP to handle various types of preference systems and inconsistency levels.

Findings

lpha-Discounting aligns with AHP for consistent problems.

It provides different results from AHP for weak inconsistent problems.

The method is applicable to linear and non-linear, homogeneous and non-homogeneous systems.

Abstract

In this book we introduce a new procedure called \alpha-Discounting Method for Multi-Criteria Decision Making (\alpha-D MCDM), which is as an alternative and extension of Saaty Analytical Hierarchy Process (AHP). It works for any number of preferences that can be transformed into a system of homogeneous linear equations. A degree of consistency (and implicitly a degree of inconsistency) of a decision-making problem are defined. \alpha-D MCDM is afterwards generalized to a set of preferences that can be transformed into a system of linear and or non-linear homogeneous and or non-homogeneous equations and or inequalities. The general idea of \alpha-D MCDM is to assign non-null positive parameters \alpha_1, \alpha_2, and so on \alpha_p to the coefficients in the right-hand side of each preference that diminish or increase them in order to transform the above linear homogeneous system of equations which has only the null-solution, into a system having a particular non-null solution. After finding the general solution of this system, the principles used to assign particular values to all parameters \alpha is the second important part of \alpha-D, yet to be deeper investigated in the future. In the current book we propose the Fairness Principle, i.e. each coefficient should be discounted with the same percentage (we think this is fair: not making any favoritism or unfairness to any coefficient), but the reader can propose other principles. For consistent decision-making problems with pairwise comparisons, \alpha-Discounting Method together with the Fairness Principle give the same result as AHP. But for weak inconsistent decision-making problem, \alpha-Discounting together with the Fairness Principle give a different result from AHP. Many consistent, weak inconsistent, and strong inconsistent examples are given in this book.