Many years ago, a friend and I were discussing Monopoly strategy (as one does, of course) when we came to a disagreement. He believed that the best players are risk-seeking: they buy as many properties as quickly as possible, always teetering on the edge of bankruptcy. I believed (and I still do) that the best players are risk-averse: they buy cautiously, saving their money to buy superior properties and make all their payments.

I don’t know who is right (though my money is on me), but I know that the way to find out is to collect data. Ideally, we would have risk-seeking players and risk-averse players compete in thousands of games and see which type of player does better than the other. Unfortunately, a Monopoly game lasts about 1,000 hours (not really, but doesn’t it feel like it sometimes?) so this little experiment would take a very long time to conduct.

Thankfully, computers can give us the next best thing. A computer program could simulate a Monopoly game between risk-seeking and risk-averse players thousands, if not millions, of times and determine which type of player does best. Such a program does not currently exist, but I am in the process of writing one in Python to figure it out. I’m not a software engineer so this program may be beyond my ability, but I’m going to give it a shot.

This blog will document my attempt to build this simulator (the code for which is hosted on GitHub). Part of it will deal with the technical hurdles, but most of it will discuss my thought process as I grapple with how to capture the complex human psychology involved in a game of Monopoly in a few lines of code. So should we seek or avoid risk to be the best Monopoly players? Let’s find out.