Step 4: Predict the Consequences

Depending on the situation, the task of predicting th„ consequences may be straightforward or formidable. Sometimes elementary arithmetic suffices. For instance, the simplest profit calculation requires only subtracting costs from revenues. Or suppose the choice between two safety programs is made according to which saves the greater number of lives per dollar expended. Here the use of arithmetic division is the key to identifying the preferred alternative. MODELS

In more complicated situations, however, the decision maker often must rely on a model to describe how options translate into outcomes. A model is a simplified description of a process, relationship, or other phenomenon. By deliberate intent, a model focuses on a few key features of a problem to examine carefully how they work while ignoring other complicating and less important factors. Of course, the main purposes of models are to explain and to predict — to account for past outcomes and to forecast future ones.

The kinds of predictive models are as varied as the decision problems to which they are applied. Many models rest on economic relationships.

Suppose the multinational steel company predicts that a 10 percent price cut will increase unit sales by 15 percent in the foreign market. The basis for this prediction is the most fundamental relationship in economics: the demand curve.

Other models rest on engineering, statistical, legal, and scientific relationships.

So far as prediction is concerned, a key distinction can be drawn between deterministic and probabilistic models. A deterministic model is one in which the outcome is certain (or close enough to a sure thing that it can be taken as certain).

For instance, a soft-drink manufacturer may wish to predict the numbers of individuals in the 10-to-25 age group over the next ten years. There are ample demographic statistics with which to make this prediction. Obviously, the numbers in this age group five years from now will consist of those who today are between ages 5 and 20, minus a predictable small number of deaths. Thus, a simple deterministic model suffices for the prediction. However, the forecast becomes much less certain when it comes to estimating the total consumption of soft drinks by this age group or the market share of a given product. Obviously, the market share of a particular drink- say, one with ten percent or niore real juice — will depend on many unpredictable factors, including the advertising promotion, and price decisions of the firm and its competitors, as well as consumer tastes. As the term suggests, a probabilistic model accounts for a range of possible future outcomes, each with a probability attached. For instance, the five-year market-share forecast for the natural-juice soft drink might take the following form: a 30 percent chance of less than a 3 percent share, a 25 percent chance of a 3 to 6 percent share, a 30 percent chance of a 6 to 8 percent share, and a 15 percent chance of an 8 to 15 percent share.

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