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an Entry Fee? diamond suggests that to ﬁnalize the event, Fr. Eﬁa must either decide
whether he will collect an entry fee or allow free admission.
From this preliminary analysis, we can also learn where the risk related to uncer-
tainty occurs. Fr. Eﬁa can see that uncertainty is associated with a number of event
processes: (1) the number of parishioners attending Vegas Night at OLPS which is
likely to be associated with weather conditions and the entry fee charged, and (2)
the outcomes of the games (players winning or losing) which are associated with the
odds that Fr. Eﬁa and Voitech will set for the games. The question of setting the odds
of the games is not included at this point, but could be a part of the diagram. In this
example, it is assumed that after these preliminary design issues are resolved we can
return to the question of the game odds. The design process is usually iterative due
to the complexity of the design task, so you may return to a number of the resolved
issues to investigate possible changes. Changes in one design issue can, and will,
affect the design of other event elements. We will return to this problem later and
see how we can incorporate uncertainty deterministically in an Excel based decision
model.
7.4 Understanding the Important Elements of a Model
As we can see from the brief discussion of the OLPS event, understanding the
processes and design of a model is not an easy task. In this section we will cre-
ate a framework for building complex models. Let us begin by considering why
we need models. First, we use models to help us analyze problems and eventually
make decisions. If our modeling is accurate and thorough, we can greatly improve
the quality of our decision making. As we determined earlier in our investment
example, intuition is certainly a valuable personal trait, but one that may not be suf-
ﬁcient in complex and risky decision situations. So what makes a problem complex?
Complexity comes from:
1.
the need to consider the interaction of many factors
2.
the difﬁculty in understanding the nature and structure of the interactions
3.
the uncertainty associated with problem variables and structure
4.
the potentially evolving and changing nature of a problem.
To deal with complexity, we need to develop a formal approach to the modeling
process; that is, how we will organize our efforts for the most effective and efﬁcient
modeling. This does not guarantee success in understanding complex models, but it
contributes mightily to the possibility of a better understanding. It is also important
to realize that the modeling process occurs in stages, and that one iteration through
the modeling process may not be sufﬁcient for completely specifying a complex
problem. It may take a number of iterations with progressively more complex mod-
eling approaches to ﬁnally arrive at an understanding of our problem. This will
become evident as we proceed through the OLPS example.
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