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the question—what is a model? This might appear to be a simple question, but as is
often the case, simple questions can often lead to complex answers. Additionally, we
need to walk a fine line between an answer that is simple, and one that does not over-
simplify our understanding. Albert Einstein was known to say—“Things should be
made as simple as possible, but not any simpler.” We will heed his advice.
Throughout the initial chapters, we have discussed models in various forms.
Early on, we broadly viewed models as an attempt to capture the behavior of a
system. The presentation of quantitative and qualitative data in Chaps. 2 and 4 pro-
vided visual models of the behavior of a system for a number of examples: sales
data of products over time, payment data in various categories, and auto sales for
sales associates. Each graph, data sort, or filter modeled the outcome of a focused
question. For example, we determined which sales associates sold automobiles in a
specified time period and we determined the types of expenditures a college student
made on particular days of the week. In Chaps. 3 and 5 we performed data analysis
on both quantitative and qualitative data leading to models of general and specific
behavior, like summary statistics and PivotTables . Each of these analyses relied on
the creation of a model to determine behavior. For example, our paired t-Test for
determining the changes in average page views of teens modeled the number of
views before and after website changes. In all these cases, the model was the way
we arranged , viewed , and examined data.
Before we proceed with a formal answer to our question, let’s see where this
chapter will lead. The world of modeling can be described and categorized in many
ways. One important way to categorize models is related to the circumstances of
their data availability . Some modeling situations are data rich ; that is, data for
modeling purposes exists and is readily available for model development. The data
on teens viewing a website was such a situation, and in general, the models we
examined in Chaps. 2, 3, 4, 5, and 6 were all data rich. But what if there is little
data available for a particular question or problem—a data poor circumstance? For
example, what if we are introducing a new product that has no reasonable equivalent
in a particular sales market? How can we model the potential success of the product
if the product has no sales history and no related product exists that is similar in
potential sales? In these situations modelers rely on models that generate data based
on a set of underlying assumptions. Chaps. 7 and 8 will focus on these models that
can be analyzed by the techniques we have discussed in our early chapters.
Since the academic area of Modeling and Simulation is very broad, it will be
necessary to divide the topics into two chapters. Chapter 7 will concentrate on the
basics of modeling. We will learn how models can be used and how to construct
them. Also, since this is our first formal view of models, we will concentrate on
models that are less complex in their content and structure. Although uncertainty
will be modeled in both Chaps. 7 and 8, we will deal explicitly with uncertainty
in Chap. 8. Yet, for both chapters, considering the uncertainty associated with a
process will help us analyze the risk associated with overall model results.
Chapter 8 will also introduce methods for constructing Monte Carlo simulations,
a powerful method for modeling uncertainty. Monte Carlo simulation uses random
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