Qualitative Decision Making (MCDM)

What is MCDM:

Decision making can be optimised through the utilisation of a decision making structure, optimising value and reducing error. Multi-Criteria Analysis is one such method, it is used in a wide range of applications, from large scale projects in government and business to the every days decisions of individuals. Common forms of decision analysis are cost-effectiveness analysis and cost benefit analysis which, at their core, compare the monetary costs and benefits of alternatives (Dodgson et al., 2009). Such a method is useful and appropriate when, for example, governments are making large scale quantitative decisions which do not involve qualitative decision making.

Qualitative aspects in decision-making complicate what is, arguably,

Adjustments take place between three dichotomies. The first dichotomy corresponds to ‘planning versus putting’ where the DA plans to solve the decision-making problem by getting the information from the DM and building it into a structure, to then put the plan into effect. The second dichotomy corresponds to ‘place versus people’ where place refers to systems and people to the decision makers. “The third dichotomy is about the mix of using personal interaction, as against using one’s position when making a decision.” (Brugha 2004, p. 1157) The DM and DA’s interaction should dictate how the information is shaped and thus the decision process must be clear in coming to a

A real DM must currently be in the process of making a decision to their problem. This requirement ensures that the information accessed from the DM accurately represents the criteria affecting the DM’s decisions and that the weight and scores given by the DM represent his/her current situation and preferences. The DM must be accessible by the DA during the process as the formation of the criteria tree requires an on-going interaction between the DA and the DM, in forming, refining and weighing the tree. The DA through his dominant position is able to ‘pounce’ on the DM in order to obtain elaborations and elucidations so as to explore all possibilities for criteria and their clusters.

Differentiable

The information accessed enables the DA to evince a provisional criteria tree by differentiating the constructs into clusters. In evincing the tree the DA has three requirements. Firstly, the alternatives must be comparable so that the tree can represent the aspects which affect the decision of all those alternatives. Secondly, the criteria must be differentiable into clusters each relevant to the decision problem. Lastly the attributes must be measurable.