Mathematical models can be great. They do, however, have some
limitations. Suppose, for example, that you are trying to predict some
data that you suspect has some mathematical relationship and you want to
know the future behavior. A mathematical model might be useful to
predict the future results of the data. Your predictive abilities will
only be as good as the model (or formula) that you are using.
Presumably, if your model accounts for past data, it should work for
future data as well. We'll keep this fairly simple.

Lets
say that you start with an initial condition and it starts at zero. The
next data point to come out is a one. So at x = 0, y = 0 and x=1, y=1.
This gives us a nice formula: x = y. We are ready to predict the future.
Our guess is that when x = 2, y =2. Our graph of the function looks
like this:

This
provides nice steady increase. If it is a graph of your investments,
you will not be getting rich very quick, but you might not be getting
poor either. If it is global temperatures, it might cause some concern.
If it is crop yields per square meter, then it is steady and
predictable.

But when x =2 comes out, it turns out
that y = 0. Our prediction was off by 2. Our graph comparing our
prediction with actual results looks like this:

This
looks like a simple problem to fix. We simply change our equation to y =
-x^2 + 2x. This equation also works for the first three values. Our
graph comparing our prediction with actual results now looks like this:

Those
curves are pretty close. We seem to be on the right track. Let's expand
our prediction graph and predict what is going to happen in the future:

We
predict that the next point on the graph will be -3. It looks as though
the graph is going increasingly downward. If this is your return on
investment, then it looks like you better get out of the market now. If
this is global temperatures, then stock up on winter clothes.

In fact, the next point is -1. Again, we are off by 2. Out graph comparing our prediction with actual results looks like this:

This is a not so easy fix. We change our equation to y = (x^3)/3 - 2x^2 + 8x/3. This gives us the following graph:

This is not exact but it is close. If we look down the road, we can predict the following:

So
if this is our investments, we should just ride it out because things
look better down the road. If it is global temperatures, then hang on
because things will get a lot hotter really quick.

When the next number comes in, it comes in as 0, exactly as our model predicted:

Surely, we are on the right track.

The next number, however, comes in as 1 rather then the 5 our model predicted.

Something is wrong again. If we look at our various model graphs, we can see that they end up going all over the place:

Clearly,
while each of these graphs works for a bit, they all fail in the end.
They all end up flying off on a tangent. This is even more clear when we
look at the long term trajectories:

All
of these graphs were based on the actual data, but they differ markedly
in their projections (all of which turn out to be wrong in the long
term). Remember that the extreme models accounted for almost the same
range of data, but after a point made widely divergent predictions.

So,
one take away is that the models, at some point, break down. We could
make the models much more complicated and account for the first twenty
points but they would then still go wildly wrong. The general point
would remain. If you are looking at a fluctuating phenomenon and
suddenly your model becomes monotonically increasing or decreasing (that
is, it stops fluctuating) then that is the point where your model
probably has broken down.