CERTAINTY, UNCERTAINTY, AND RISK 3

4.4 CERTAINTY, UNCERTAINTY, AND RISK 3

Part of Simon's decision-making process described in Chapter 2 involves evaluating and comparing alternatives, during which it is necessary to predict the future outcome of each proposed alternative. Decision situations are often classified on the basis of

C H A P T E R 4 M O D E L I N G A N D ANALYSIS

what the decision-maker knows (or believes) about the forecasted results. Customary, we classify this knowledge into three categories (Figure 4.1), ranging from complete knowledge to total ignorance. These categories are

• Certainty • Risk • Uncertainty

When we develop models, any of these conditions can occur, and different kinds of models are appropriate for each case. We discuss both the basic definitions of these terms and some important modeling issues for each condition.

DECISION-MAKING UNDER CERTAINTY

In decision-making under certainty, it is assumed that complete knowledge is available so that the decision-maker knows exactly what the outcome of each course of action will be (as in a deterministic environment). It may not be true that the outcomes are 100 percent known, nor is it necessary to really evaluate all the outcomes, but often this assumption simplifies the model and makes it tractable. The decision-maker is viewed as a perfect predictor of the future because it is assumed that there is only one outcome for each alternative. For example, the alternative of investing in U.S. Treasury bills is one for which there is complete availability of information about the future return on the investment. Such a situation occurs most often with structured problems with short time horizons (up to 1 year). Another example is that every time you park downtown, you get a parking ticket because you exceed the time limit on the parking meter—

although once it did not happen. This situation can still be treated as one of decision- making under certainty. Some problems under certainty are not structured enough to

be approached by analytical methods and models; they require a DSS approach. Certainty models are relatively easy to develop and solve, and can yield optimal

solutions. Many financial models are constructed under assumed certainty, even though the market is anything but 100 percent certain. Problems that have an infinite (or a very large) number of feasible solutions are extremely important and are dis- cussed in Sections 4.9 and 4.12.

DECISION-MAKING UNDER UNCERTAINTY

In decision-making under uncertainty, the decision-maker considers situations in which several outcomes are possible for each course of action. In contrast to the risk situation, in this case the decision-maker does not know, or cannot estimate, the proba-

F I G U R E 4 . 1 THE ZONES O F DECISION M A K I N G

Increasing knowledge

Complete knowledge,

Risk

Ignorance, total

certainty uncertainty

Decreasing knowledge

P A R T II DECISION SUPPORT SYSTEMS

bility of occurrence of the possible outcomes. Decision-making under uncertainty is more difficult because of insufficient information. Modeling of such situations involves assessment of the decision-maker's (or the organization's) attitude toward risk (see Nielsen, 2003).

Managers attempt to avoid uncertainty as much as possible, even to the point of assuming it away. Instead of dealing with uncertainty, they attempt to obtain more information so that the problem can be treated under certainty (because it can be "almost" certain) or under calculated (assumed) risk. If more information is not avail- able, the problem must be treated under a condition of uncertainty, which is less defin- itive than the other categories.