But while you were testing, you put a fair amount of money into machines which have a lower return rate. Once you’ve finished you’ll have a good idea of which machine has the highest return, and you can spend the rest of your money (assuming you have any) on it. You could put the same amount of money into each machine, and compare the return. How do you figure out which machine that is? One strategy is to try and figure out the return for each machine before spending the majority of your money. In fact, you’re fairly certain that one of the machines has a higher rate of return than the others. In this hypothetical casino, there is no gaming control board to guarantee that each machine has the same return. Imagine you walk into a casino and you see three slot machines. In this article I’ll go into more details as to what multi-arm bandit testing is, how it compares to A/B testing, and some of the reasons you should, and should not, use it. When applied to A/B testing it can be a valuable tool. The problem posed is as follows: given a limited amount of resources, what is the best way to maximize returns when gambling on one-arm bandit machines that have different rates of return? This is often phrased as a choice between “exploitation,” or maximizing return, and “exploration,” or maximizing information. Multi-Arm Bandit testing comes from the Multi-Arm Bandit problem in mathematics.
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