Bayesian decision theory
Bayesian decision theory refers to decision theory which is informed by Bayesian probability. An agent operating under such a decision theory uses the concepts of Bayesian statistics to estimate the expected value of its actions, and update its expectations based on new information.
Computer algorithms such as those studied in the subject of Machine learning can use Bayesian methods explicitly, but it also has been observed that naturally evolved [systems] mirror these probabilistic methods when they adapt to an uncertain environment. What Less Wrong refers to as Rationality is an effort to make conscious thoughts a better approximation of Bayesian decision theory, in order to better understand the world and achieve one's goals.