We all face decisions in our jobs, our communities and in our personal lives.
- Where should a new airport, manufacturing plant, power plant, or health care clinic be located?
- Which college should I attend, or which job should I accept?
- Which car, house, computer, stereo or even health plan should I buy?
- Which supplier or building contractor should be hired?
Decisions such as these involve comparing alternatives that have strengths or weaknesses with regard to multiple objectives of interest to the decision maker. Multi-Attribute Utility Theory (MAUT) is a structured methodology designed to handle the tradeoffs among multiple objectives. One of the first applications of MAUT involved a study of alternative locations for a new airport in Mexico City in the early 1970s. The factors that were considered included cost, capacity, access time to the airport, safety, social disruption and noise pollution.
Utility theory is a systematic approach for quantifying an individual's preferences. It is used to rescale a numerical value on some measure of interest onto a 0-1 scale with 0 representing the worst preference and 1 the best. This allows the direct comparison of many diverse measures. That is, with the right tool, it really is possible to compare apples to oranges! The end result is a rank ordered evaluation of alternatives that reflects the decision makers' preferences. An analogous situation arises when individuals, college sports teams, MBA degree programs, or even hospitals are ranked in terms of their performance on multiple disparate measures. Another example is the Bowl Coalition Series (BCS) in college football that attempts to identify the two best college football teams in the United States to play in a national championship bowl game. This process has reduced but not eliminated the annual end of the year arguments as to which college should be crowned national champion.
Early applications of MAUT focus on public sector decisions and public policy issues. These decisions not only have multiple objectives, they also often involve multiple constituencies that will be affected in different ways by the decision. Under the guidance of Ralph Keeney, a leading researcher in the field, many power plant-related decisions were made using MAUT. The military is also a leading user of this technique. The design of major new weapons systems always involves tradeoffs of cost, weight, durability, lethality and survivability. The federal government requires its defense contractors to use a structured method to make these design trade-off decisions. MAUT is one methodology in the broader field of Multi-Criteria Decision Making (MCDM). The Analytic Hierarchy Process (AHP) is a direct competitor to MAUT as an efficient technique for rank ordering alternatives. MCDM also encompasses Multiobjective Mathematical Programming. This technique is used to tackle complex problems involving a large number of decision variables that are subject to constraints. Logical Decisions is a leading software product designed to facilitate MAUT and AHP studies. Its website contains a comprehensive bibliography of books and applications.