Monte Carlo I - Claims Fluctuations and Economic value of Stop-Loss
Monte Carlo II - Economic Value of Plan Benefits and Managed Care Arrangements
Background Reading
In General
The “Monte Carlo Method” is actually very general. Monte Carlo methods are based on the use of a stochastic technique in that they employ the use of random numbers and probability statistics to investigate problems. One can find Monte Carlo methods used in everything from economics to nuclear physics to regulating the flow of traffic. Of course, the way they are applied varies widely from field to field, and there are dozens of subsets of Monte Carlo even within chemistry. But, strictly speaking, to call something a Monte Carlo simulation, all you need to do is use random numbers to examine some problem.
The use of Monte Carlo methods to model physical problems allows us to examine more complex systems. Solving equations which describe the interactions between two atoms is faily simple; solving the same equations for hundreds of thousands of atoms is impossible. With Monte Carlo methods, a large system can be sampled in a number of random configurations, and that data can be used to describe the system as a whole.
Applications
Monte Carlo simulation methods are especially useful for modeling phenomena with significant uncertainty in inputs and in studying systems with a large number of coupled degrees of freedom. Specific areas of application include:
- Physical sciences
- Design and Visual modeling
- Finance and Business
- Telecommunications
- Mathematics
- Demographics
