In the first example above, the biggest bet Steve has to make to keep himself in business is $8,192. Let me show you another few rounds of 10,000 scenarios that I just generated using Monte Carlo simulation: He showed you 10,000 scenarios, which sounds like it would be enough to show you a trend, but he picked one of the better rounds of 10,000 scenarios. Steve was also a little tricky in the way he developed his graph above. In the real world, casinos don’t just let you walk in with no cash in hand and start gambling. You might have noticed one funny thing about the example above. Both of these things have some pretty profound implications and describe some important things about how people, firms and governments act in the real world. Part of the answer has to do with liquidity constraints, and the other part has to do with what most people would consider to be an appropriate return for a given level of risk. Is it really this simple? You have probably guessed that it is not. If beating roulette is so easy, why do casinos have it? Why aren’t statistical geniuses like Steve robbing casinos blind all the time? Starting from a $0 balance, and a $1 bet, after 10,000 rounds, Steve finishes with $4,828 in the bank. See, there are some bumps, but in the long run, you’re guaranteed to make money! Genius! Those stupid casinos don’t stand a chance against a genius like Steve!
