IA ideas library

Simulation and Monte Carlo IA ideas: when the exact answer is too hard

Some questions are too messy to answer exactly: a queue with impatient customers, a board game with special rules, a disease spreading through a year group. A simulation answers them by running the random process thousands of times and measuring what happens. A Monte Carlo IA is strongest when it also does some exact mathematics, to check the simulation, or to find a simpler case where theory and simulation should agree.

You can simulate in a spreadsheet with RAND(), in a few lines of Python or on a GDC. Code is a tool, not the mathematics: explain in words and notation what each run does, and how many runs you need for the answer to be reliable.

Monte Carlo estimate of pi: 400 points spread over a unit square, coloured by whether they fall inside a quarter circle, with the resulting estimate
A Monte Carlo estimate: the proportion of points inside the quarter circle estimates π/4.

Simulation & Monte Carlo methods ideas (8)

Also relevant (9)

Frequently asked questions

Is a simulation enough mathematics for an IA?

Not on its own. Pair it with exact calculation for a simple case, a probability distribution that explains the results, or an estimate of the simulation's error. The simulation then tests and extends the mathematics rather than replacing it.

Do I have to write code?

No. A spreadsheet with RAND() or RANDBETWEEN() can run thousands of trials. If you do write code, put it in an appendix and describe the algorithm in the body.

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