Structuring and Weighting Criteria in Multi Criteria Decision Making (MCDM)

Abstract

Abstract:The implications of qualitative distinctions between multiple criteria are considered. Some contributions to theory about the Analytical Hierarchy Process (AHP) are challenged. Experiments on alternative criteria structures are reported. These suggest that confusing structures are bad, but good structures are better than none. Guidelines on how to develop a structure are given for a well known case of the purchase of a house. It is suggested that differences between decision alternatives should provide a first phase basis for discovering criteria. A criteria tree should be structured 'top down' as a second phase by clustering criteria on the basis of qualitative difference. On any level the differences between criteria should follow relatively simple patterns. The rules used suggest the relevance of work on the structure of qualitative decision-making which is determined by Nomology, the science of the laws of the mind. Implications are considered for weighting trade-offs between homogeneous clusters of criteria.This should be done as a later 'bottom up' phase. The AHP scoring system is challenged. Some tests of alternative scoring methods are reported.

A semi-automated approach for structuring multi criteria decision problems

“…While the resulting clusters approach the specified cluster size, some of the clusters will exceed C max (see Figure 2). We argue that homogeneity is more important than strict upper limits of cluster sizes due to three reasons: Firstly, inhomogeneity can render hierarchies completely useless since, instead of being supportive, such hierarchies hamper human information processing and measurement of preferences (Brugha, 1998). Secondly, there are ways to deal with disadvantages of larger clusters in MCDM settings (Bernroider et al, 2010).…”

“…The structure itself has also a significant effect on the outcome of the decision process (Borcherding and Von Winterfeldt, 1988;Brugha, 1998). Although the initial activities of analytical decision making are usually considered as the most important, valuable and also difficult steps (Von Winterfeldt and Fasolo, 2009) the questions how to derive a complete list of criteria and how to reveal the latent structure of such a list does not receive much attention within the MCDM literature.…”

An evaluation tool for design quality: PFI sheltered housing

“…The main role of this technique is to handle large quantities of complex information in a consistent way. A key feature is its emphasis on the judgement of the decision-making team -in establishing objectives and criteria, assessing the relative importance of criteria through an explicit weighting system, and evaluating the contribution of responses to each criterion (Keeney and Raiffa, 1993;Brugha, 1998;Wallenius et al, 2008). The most common way to combine scores on criteria, is to calculate a simple weighted average.…”

A comprehensive uncertainty analysis of the analytic hierarchy process methodology applied in the context of environmental decision making

“…Uncertainty associated with the development of the model structure is inherently related to the formulation of a decision hierarchy (Belton & Stewart, 2002;Jakeman, Voinov, Rizzoli, & Chen, 2008 Brugha (1998Brugha ( , 2004 offers methodological suggestions how to elaborate structured criteria trees. Also, Wedley (1990) provides guidelines what to include in hierarchies.…”

“…(Brugha, 1998(Brugha, , 2004Finan & Hurley, 2002;Levary & Wan, 1998;Maleki & Zahir, 2013;Saaty, 2007;Saaty & Begicevic, 2010;Warren, 2006) Uncertainty associated with the incorporation of important, but "unknown" factors-How to include important, but only suspected and not explicitly articulated factors into the problem modelling? (Ozdemir & Saaty, 2006) Weights valuation Measurement theoretical debate-Is the original preference measurement scale (linear; Saaty scale) a ratio scale?…”