In risk modeling, the idea of regime change is a mathematical expression. A change from one mathematical rule to another.
But in the world, Regime Change can have a totally different meaning. Like what is happening in Egypt.
When someone sits atop a government for 30 years, it is easy to assume that next week they will still be on top.
Until that is no longer true.
When there is a regime change, it happens because the forces that were in a stable equilibrium shift in some way so that they can no longer support a continuation of the past equilibrium. In hindsight, it is possible to see that shift. But the shift is often not so obvious in advance.
Again, as when the Soviet Union fell apart, the intelligence services were seemingly taken by surprise.
But is there really any difference between the two types of regime change? Is it any easier to actually notice an impending regime change on a modeled risk than an impending political risk?
Why are we so bad at seeing around corners?
In the area of public health, it is well known that diseases follow a standard path called an S curve. That is the path of a curve plotting the number of people infected by a disease over time. The path has a slight upward slope at first then the slope gets much, much steeper and eventually it slows down again.
When a new disease is noticed, some observers who come upon information about the disease during that middle period during the rapid upward slope will extrapolate and predict that the disease incidence will grow to be much higher than it ever gets.
The reason for the slowdown in the rate of growth of the disease is because diseases are most often self limiting because people do not usually get the disease twice. Diseases are spread by contact between a carrier and an uninfected person. In the early stages of a disease, the people who make the most contacts with others are the most likely to become infected and themselves become carriers. Eventually, they all lose the ability to be carriers and become immune and the number of times that infected carriers come into contact with uninfected persons starts to drop. Eventually, such contacts become rare.
It is relatively easy to build a model of the progression of a disease. We know what parameters are needed. We can easily estimate those that we cannot measure exactly and can correct our estimates as we make observations.
We start out with of model of a disease that assumes that the disease is not permanent.
We plan for regime change.
Perhaps that is what we need for the rest of our models. We should start out by assuming that no pattern that we observe is permanent. That each regime carries the seeds of its own destruction.
If we start out with that assumption, we will look to build the impermanence of the regime into our models and look for the signs that will show that whatever guesses we had to make initially about the path of the next regime change can be improved.
Because when we build a model that does not include that assumption, we do not even think about what might cause the next regime change. We do not make any preliminary guesses. The signs that the next change is coming are totally ignored.
In the temperate zones where four very different seasons are the norm, the signs of the changes of seasons are well known and widely noticed.
The signs of the changes in regimes of risks can be well known and widely noticed as well, but only if we start out with a model that allows for regime changes.