Reinforcement Learning and Optimistic Decisions
The main idea of following upper confidence bounds is minimizing regret, that is, the difference between the received reward and the maximum. The paper to read on this is: "Finite-time analysis of the multiarmed bandit problem" by Peter Auer, Nicoló Cesa-Bianchi, and Peter Fischer.

If we interpret life as a Markov Decision Process, then most reinforcement learning theories apply quite well. I.e. we want to keep an open mind, but we want to do what we think is best exponentially more often than exploratory actions.

Of course, interpreting life as an MDP is a bit of a stretch — foresight, for example, has to be interpreted as simulated experience, but I believe one can find quite a lot of wisdom in reinforcement learning and artificial intelligence in general.