Simulation
Simulation provides a way to examine the outcomes of a
set of process rules (a model) given a set of starting conditions.
People use simulations in a number of ways. The least acceptable way is
to use it as a replacement for empirical studies that use real data.
However if a model has been validated against real data, then with some
caveats, it can be used to predict outcomes using different starting
conditions that haven't previously been examined empirically (in short,
what-if scenarios).
Where simulation shines is in proving that, given a
set of starting conditions, a proposed process can indeed lead to a
claimed output. In other words, it provides log check, which is
particularly useful when the proposed is simply to complicated to
understand directly, or is not amenable to mathematical proof.
for example, suppose you are trying to explain why
there are more girls in India the boys. You propose a provocative
theory: that Indians have a preference for girl babies, so when a couple
has their first child, if its a girl they may stop. But if it's a boy,
they are likely try again in hopes of getting a boy. Since gender of
children tends to run in families, what happens is that "girl families"
have a ton of children, while "boy families" have few kids.
A simulation can test your logic. You can program a
world of simulated couples that follow these rules perfectly. Letting
the simulation run, you can see whether, at the end, there are more
girls than boys, and this "experiment" can be repeated thousands of
times to see how often that set of rules leads to the result of more
girls than boys.
The important thing to realize is that this says
nothing about the actual reasons why there are more girls than boys in
India. It is a theory, and we have tested the theory with respect to its
internal logic. We have shown that in fact, the proposed process will
lead to an excess of girls. But it doesn't mean that other processes
couldn't lead to the same result. |
