Prioritizing Geologic Mapping with MCDA

July 30, 2008 By: John Dooris

Multi-criteria decision analysis (MCDA) provides methodology and techniques for analyzing complex decision problems, which often involve incommensurable data or criteria. Human decision makers often have difficulty analyzing large amounts of complex information in a consistent manner. The main role of MCDA techniques is to provide a way to consistently analyze such criteria (Dodgson et al., undated).

MCDA techniques are generally used to rank options, commonly referred to as alternatives within the MCDA field, from most preferred to least preferred. However, MCDA can also be used to identify a single most-preferred alternative, to short-list a number of alternatives for subsequent detailed analysis, or to determine acceptable and unacceptable alternatives (Malczewski, 1999; Dodgson et al., undated)

There are a variety of MCDA techniques. However, the general approach is the same: MCDA disaggregates the problem into separate pieces or criteria which have been defined by a decision-making body, weights each criterion in relationship to the defined objective(s) of that body, and then aggregates the weighted criteria to reach a conclusion (Dodgson et al., undated).

MCDA Trumps Informal Decision Making

The advantages of using a system of weighting and scoring multiple criteria to reach a decision have been known for centuries. Benjamin Franklin proposed using what he called Prudential Calculus, a simple method of weighting the pros and cons of an outcome, to help with complex decision making (Dodgson et al., undated).

MCDA was developed in the 1970s by Keeney and Raiffa (1976), who built upon decision theory in the operations research/management sciences. Its advantages in decision making have been well documented (Chakhar and Martel, 2003; Mendoza et al., 1999). The benefits to using MCDA include:

1. Capability to accommodate multiple and mixed criteria in the analysis
2. Allows the direct involvement of multiple experts, interest groups, and stakeholders.
3. Analysis is transparent to participants and leaves an audit trail
4. Includes mechanisms for feedback concerning the consistency of the judgements made (Mendoza et al., 1999; Chakhar and Martel, 2003).

Perhaps the most important of these is the ability for analysis to remain transparent to both participants and observers. In many situations, the ability to communicate and explain how decisions were reached is as important as the decision itself. MCDA's ability to separate the decision elements into individual criteria and formally outline the decision-making process makes it ideally suited to communicate the basis of all decisions (Mendoza et al., 1999; Forestry Research, 1999). This makes MCDA an especially useful tool when the issue is related to public policy or involves multiple decision makers.