It is very true: exploring and learning from risky strategies on a small scale (ostensibly resulting in losses) is a strategy for meta-winning. You provided good examples in your post. This is such an important idea, I would like to add two more.
Losing when stakes are low is a strategy for winning when stakes are high
A common real life scenario is that it easy to win when the stakes are low and harder to win when the stakes are high. If you are always winning in a low-stakes scenario, then you have information that your strategy is good, but only for that context. You would like to test and hone your strategy in more challenging contexts, but perhaps there it is more important to win (not practice winning).
The solution is to handicap yourself in the low-stakes scenario. Challenge yourself by not permitting full use of the winning strategy. You already knew your strategy was sufficient for winning but now you will learn: what aspects of your strategy were necessary for winning and what aspects can be modified for even more effective winning. You will have developed a better strategy for the high-stakes scenario.
An analogy from statistical mechanics:
A ball is passively rolling around a landscape of hills and valleys. The ball, being passive, will roll down valleys (but not roll up hills) until it finds itself in the “lowest” location. The ball is in the lowest location because if it were to consider moving in any direction it would have to move up. This is a local minimum. How does the ball know that there isn’t a lower valley just over the next hill? The ball will be much more effective at finding the absolute lowest valley if it is able to climb some hills to explore what’s on the other side. By allowing yourself to explore less effective strategies (climbing hills), you increase the probability of finding an even better winning strategy (a lower minimum) than your original one.
Fortunately, these ideas are very well developed in physics and guidelines are provided for determining how to choose your tolerance for losing (ability to roll up hills/temperature) in order to optimize finding the best winning strategy (verses exploring up and down indefinitely without net progress).
Awesome post!
It is very true: exploring and learning from risky strategies on a small scale (ostensibly resulting in losses) is a strategy for meta-winning. You provided good examples in your post. This is such an important idea, I would like to add two more.
Losing when stakes are low is a strategy for winning when stakes are high
A common real life scenario is that it easy to win when the stakes are low and harder to win when the stakes are high. If you are always winning in a low-stakes scenario, then you have information that your strategy is good, but only for that context. You would like to test and hone your strategy in more challenging contexts, but perhaps there it is more important to win (not practice winning).
The solution is to handicap yourself in the low-stakes scenario. Challenge yourself by not permitting full use of the winning strategy. You already knew your strategy was sufficient for winning but now you will learn: what aspects of your strategy were necessary for winning and what aspects can be modified for even more effective winning. You will have developed a better strategy for the high-stakes scenario.
An analogy from statistical mechanics:
A ball is passively rolling around a landscape of hills and valleys. The ball, being passive, will roll down valleys (but not roll up hills) until it finds itself in the “lowest” location. The ball is in the lowest location because if it were to consider moving in any direction it would have to move up. This is a local minimum. How does the ball know that there isn’t a lower valley just over the next hill? The ball will be much more effective at finding the absolute lowest valley if it is able to climb some hills to explore what’s on the other side. By allowing yourself to explore less effective strategies (climbing hills), you increase the probability of finding an even better winning strategy (a lower minimum) than your original one.
Fortunately, these ideas are very well developed in physics and guidelines are provided for determining how to choose your tolerance for losing (ability to roll up hills/temperature) in order to optimize finding the best winning strategy (verses exploring up and down indefinitely without net progress).