Simulation

I find I have to build simulations with increasing frequency in my work and life. Usually this indicates I’m faced with one of the following situations: The need for a quick estimate regarding the quantitative behavior of some situation. The desire to verify the result of a computation or assumption. A situation which is too complex or random to effectively model or understand. Anyone familiar at all with simulation will recognize the last item as the motivating force of the entire field. Simulation models tend to take over when systems become so complex that understanding them is prohibitive in cost and time or entirely infeasible. In a simulation, the modeler can focus on individual interactions between entities while still hoping for useful output in the form of descriptive statistics. ...

I hope you saw “China’s way to the top” on the Post’s website recently. It’s a very clear presentation of their statement and is certainly worth a look. So say you’re an economist and you actually do need to produce a realistic estimate of when China’s GDP surpasses that of the USA. Can you use such an approach? Not really. There are several simplifying assumptions the Post made that are perfectly reasonable. However, if the goal is an analytical output from a highly random system such as GDP growth, one should not assume the inputs are fixed. (I’m not saying I have any gripe with their interactive. This post has a different purpose.) ...

Note: This post was updated to work with Python 3. One of the most useful tools one learns in an Operations Research curriculum is Monte Carlo Simulation. Its utility lies in its simplicity: one can learn vital information about nearly any process, be it deterministic or stochastic, without wading through the grunt work of finding an analytical solution. It can be used for off-the-cuff estimates or as a proper scientific tool. All one needs to know is how to simulate a given process and its appropriate probability distributions and parameters if that process is stochastic. ...