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82 Part I • Decision Making and Analytics: An Overview

level of performance and then searches the alternatives until one is found that achieves
this level. The usual reasons for satisficing are time pressures (e.g., decisions may lose
value over time), the ability to achieve optimization (e.g., solving some models could
take a really long time, and recognition that the marginal benefit of a better solution is
not worth the marginal cost to obtain it (e.g., in searching the Internet, you can look at
only so many Web sites before you run out of time and energy). In such a situation, the
decision maker is behaving rationally, though in reality he or she is satisficing. Essentially,
satisficing is a form of suboptimization. There may be a best solution, an optimum, but it
would be difficult, if not impossible, to attain it. With a normative model, too much com-
putation may be involved; with a descriptive model, it may not be possible to evaluate all
the sets of alternatives.
Related to satisficing is Simon’s idea of bounded rationality. Humans have a
limited capacity for rational thinking; they generally construct and analyze a sim-
plified model of a real situation by considering fewer alternatives, criteria, and/or
constraints than actually exist. Their behavior with respect to the simplified model
may be rational. However, the rational solution for the simplified model may not be
rational for the real-world problem. Rationality is bounded not only by limitations on
human processing capacities, but also by individual differences, such as age, educa-
tion, knowledge, and attitudes. Bounded rationality is also why many models are
descriptive rather than normative. This may also explain why so many good managers
rely on intuition, an important aspect of good decision making (see Stewart, 2002; and
Pauly, 2004).
Because rationality and the use of normative models lead to good decisions, it is
natural to ask why so many bad decisions are made in practice. Intuition is a critical
factor that decision makers use in solving unstructured and semistructured problems.
The best decision makers recognize the trade-off between the marginal cost of obtain-
ing further information and analysis versus the benefit of making a better decision. But
sometimes decisions must be made quickly, and, ideally, the intuition of a seasoned,
excellent decision maker is called for. When adequate planning, funding, or informa-
tion is not available, or when a decision maker is inexperienced or ill trained, disaster
can strike.

Developing (generating) alternatives

A significant part of the model-building process is generating alternatives. In optimization
models (such as linear programming), the alternatives may be generated automatically by
the model. In most decision situations, however, it is necessary to generate alternatives
manually. This can be a lengthy process that involves searching and creativity, perhaps
utilizing electronic brainstorming in a GSS. It takes time and costs money. Issues such as
when to stop generating alternatives can be very important. Too many alternatives can be
detrimental to the process of decision making. A decision maker may suffer from informa-
tion overload.
Generating alternatives is heavily dependent on the availability and cost of informa-
tion and requires expertise in the problem area. This is the least formal aspect of problem
solving. Alternatives can be generated and evaluated using heuristics. The generation of
alternatives from either individuals or groups can be supported by electronic brainstorm-
ing software in a Web-based GSS.
Note that the search for alternatives usually occurs after the criteria for evaluating the
alternatives are determined. This sequence can ease the search for alternatives and reduce
the effort involved in evaluating them, but identifying potential alternatives can sometimes
aid in identifying criteria.

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