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Chapter 2 • Foundations and Technologies for Decision Making 83

The outcome of every proposed alternative must be established. Depending on
whether the decision-making problem is classified as one of certainty, risk, or uncertainty,
different modeling approaches may be used (see Drummond, 2001; and Koller, 2000).
These are discussed in Chapter 9.

Measuring outcomes
The value of an alternative is evaluated in terms of goal attainment. Sometimes an
outcome is expressed directly in terms of a goal. For example, profit is an outcome,
profit maximization is a goal, and both are expressed in dollar terms. An outcome such
as customer satisfaction may be measured by the number of complaints, by the level
of loyalty to a product, or by ratings found through surveys. Ideally, a decision maker
would want to deal with a single goal, but in practice, it is not unusual to have multiple
goals (see Barba-Romero, 2001; and Koksalan and Zionts, 2001). When groups make
decisions, each group participant may have a different agenda. For example, executives
might want to maximize profit, marketing might want to maximize market penetration,
operations might want to minimize costs, and stockholders might want to maximize the
bottom line. Typically, these goals conflict, so special multiple-criteria methodologies
have been developed to handle this. One such method is the AHP. We will study AHP
in Chapter 9.

risk
All decisions are made in an inherently unstable environment. This is due to the many
unpredictable events in both the economic and physical environments. Some risk (meas-
ured as probability) may be due to internal organizational events, such as a valued
employee quitting or becoming ill, whereas others may be due to natural disasters, such
as a hurricane. Aside from the human toll, one economic aspect of Hurricane Katrina was
that the price of a gallon of gasoline doubled overnight due to uncertainty in the port
capabilities, refining, and pipelines of the southern United States. What can a decision
maker do in the face of such instability?
In general, people have a tendency to measure uncertainty and risk badly. Purdy
(2005) said that people tend to be overconfident and have an illusion of control in
decision making. The results of experiments by Adam Goodie at the University of
Georgia indicate that most people are overconfident most of the time (Goodie, 2004).
This may explain why people often feel that one more pull of a slot machine will
definitely pay off.
However, methodologies for handling extreme uncertainty do exist. For example,
Yakov (2001) described a way to make good decisions based on very little information,
using an information gap theory and methodology approach. Aside from estimating the
potential utility or value of a particular decision’s outcome, the best decision makers are
capable of accurately estimating the risk associated with the outcomes that result from
making each decision. Thus, one important task of a decision maker is to attribute a level
of risk to the outcome associated with each potential alternative being considered. Some
decisions may lead to unacceptable risks in terms of success and can therefore be dis-
carded or discounted immediately.
In some cases, some decisions are assumed to be made under conditions of cer-
tainty simply because the environment is assumed to be stable. Other decisions are
made under conditions of uncertainty, where risk is unknown. Still, a good decision
maker can make working estimates of risk. Also, the process of developing BI/DSS
involves learning more about the situation, which leads to a more accurate assessment
of the risks.

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