# Multi-Criteria Analysis

Multi-Criteria Analysis (MCA) is a method aimed at supporting decision makers who are faced with making numerous and conflicting evaluations. MCA aims at highlighting these conflicts and deriving a way to come to a compromise in a transparent process.

It involves judging the expected performance of each option against a number of criteria or objectives. These techniques can deal with complex situations, involving uncertainty as well as the preferences of many stakeholders. This is particularly highlighted when the problem presents conflicting objectives and when these objectives cannot be easily expressed in monetary terms. The essence of MCA lies in the preparation of a performance matrix with several rows and columns in which each row describes one of the objectives or performance dimensions and each column describes one option. Thereafter, scores for each option with respect to each objective are assigned. These scores are supposed to represent performance indicators and may range on a scale from -5 to +5.

In the more sophisticated versions of MCA, weights are assigned to each objective. Thereafter, a weighted average of scores is worked out. This average provides the overall indicator of performance of each option. The higher the weighted average of scores, the better the option. The size of the matrix can be increased to take care of a large number of criteria as well as options. It is a physical method based on numerical rating and scaling of various environmental and societal impacts. As such, the difficulties faced in quantification in monetary terms are avoided.

The usefulness of MCA is most apparent when one option emerges as the dominant one in the matrix with respect to all the criteria. However, the methodology has several shortcomings. How can one judge whether a good performance against one objective would compensate for poor performance against another? This, again, is a question of trade-off which occupies a central position in decision-making related to multi-dimensional aspects. Weighting or ranking becomes necessary to handle such cases. But how does one determine weights? This becomes the most critical question to which no satisfactory answer is available. Weights determined by experts cannot be regarded as free from subjective biases. Weights determined by the concerned public may be suggested without full awareness of the flood formation and impact. Hence, the problems of weighting in MCA procedures are similar to the problems faced by a comprehensive CBA.

Notwithstanding the above shortcomings, MCA can be very useful for shortlisting options, which can then be subjected to the more rigorous CBA for a final decision. In this respect MCA is best understood as a complementary approach to CBA.