Monte Carlo methods are a class of algorithms that help to show all possible outcomes for an event with a random sample of input. The basic idea is that a large number of random inputs will help us get the whole range of outcomes, and also how likely they are to occur. It shows the extreme possibilities — the outcomes of going for broke and for the most conservative decision — along with all possible consequences in between.
The general way to perform a Monte Carlo simulation is remarkably simple: 1. Randomly generate inputs 2. plug them into the problem 3. Compute an output for each input 4. Approximate the solution by aggregating the outputs
Let’s take an example. In the ideal world, we may have a formula to calculate, say, return on an investment. However, in the real world, the input values and output values won’t always fit into this formula because of variability. With Monte Carlo, you can input the whole range of possible input values 1000s of time to get a simulated distribution of all the possible outputs and their likeliness. Hence, this allows people to account for risk in quantitative analysis and decision making, and also answers ‘what ifs’.
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From the desk of Aditya Khanduri
About me (Why I started Polygyan)
