Decision modeling attempts to replicate the actual behavior of decision making. A model first identifies specific criteria to be used in making a decision, then allows the decision makers to assess how well competing options meet those criteria. For example, when purchasing a product, such as an automobile, a consumer might consider price, quality, service, and options. In decision modeling, each attribute is weighted by its relative importance, and each car model is judged on how well it matches each criterion.
Decision modeling can also explore the market potential of new technologies by assessing how well the new technology meets criteria already established by the marketplace. In these applications, decision modeling quantifies the potential of a product or technology to gain share from products already on the market.
The behavior of a large number of systems is determined, to a great extent, by decisions made by people or groups within those systems. In population systems, the behavior of couples of childbearing age determines the dynamics of the system; in market systems, the collective decisions of consumers constitute market behavior; in industries, such as the electric utility industry, decisions by corporate executives on generation expansion determine many characteristics of that system. Thus, in order to understand the behavior of systems, we must understand the nature of decision making within the system.
Strategic-management professor Michel Godet has also made some interesting additions to the technique of decision modeling. His MULTIPOL method assumes an alternative future environment rather than the single-valued future of most decisions. Godet implements that view by adjusting the weights involved in the decision according to the environment that is being forecast. In buying a car, for instance, the criterion of fuel efficiency would receive a higher weight in a low-energy future than in a high-energy one. This flexibility makes possible the discussion of relative advantages of different policies or strategies across a spectrum of alternative futures.
Methodology and Applications
Decision modeling is related to utility theory in systems analysis. In utility theory, a rational decision maker selects an alternative product, policy, or action that best meets his or her criteria for success. As an example, consider a young person deciding which college to attend. When asked what's important in a college, the factors mentioned might include: excels in my field, has good football, is co-ed. The parent might add price to the set of criteria. These four criteria would be weighted according to their relative importance. Then, using this method, a matrix could be constructed in which possible colleges would be compared on the basis of these criteria. See Table 1.
Table 1: School Choice Decision Matrix Criteria [right arrow] Teaches Good Co-ed Cost Score what football I want Weights [right arrow] 8 4 4 9 College 1: University of 3 5 3 3 83 Big State College 2: State Tech A&M 4 4 4 4 100 College 3: Little School 5 3 5 3 99 of Liberal Arts
The column labeled "score" is the weighted sum for each college. All other things being equal, the most rational choice in this example is college 2. If, when such an analysis is performed, the results seem subjectively wrong, it is appropriate to ask if a criterion has been omitted (e.g., location--what about urban versus rural setting or distance from home?), or the weights are improper (the cost could be less of a factor if the student wins a scholarship).
Although a decision maker may not actually list these decision factors or consciously weigh them, they are implicit in the perceived value of alternatives. …