Ordinary probability theory and expected utility are sufficient to handle this puzzle. You just have to calculate the expected utility of each strategy before choosing a strategy. In this puzzle a strategy is more complicated than simply putting some number of coins in the machine: it requires deciding what to do after each coin either succeeds or fails to succeed in releasing two coins.
In other words, a strategy is a choice of what you’ll do at each point in the game tree—just like a strategy in chess.
We don’t expect to do well at chess if we decide on a course of action that ignores our opponent’s moves. Similarly, we shouldn’t expect to do well in this probabilistic game if we only consider strategies that ignore what the machine does. If we consider all strategies, compute their expected utility based on the information we have, and choose the one that maximizes this, we’ll do fine.
I’m saying essentially the same thing Jeremy Salwen said.
I agree that math can teach all these lessons. It’s best if math is taught in a way that encourages effort and persistence.
One problem with putting too much time into learning math deeply is that math is much more precise than most things in life. When you’re good at math, with work you can usually become completely clear about what a question is asking and when you’ve got the right answer. In the rest of life this isn’t true.
So, I’ve found that many mathematicians avoid thinking hard about ordinary life: the questions are imprecise and the answers may not be right. To them, mathematics serves as a refuge from real life.
I became very aware of this when I tried getting mathematicians interested in the Azimuth Project. They are often sympathetic but feel unable to handle the problems involved.
So, I’d say math should be done in conjunction with other ‘vaguer’ activities.