Maximization of expected utility is the most well-known choice rule in decision theory. Given an agent’s preferences (represented as utility functions) and beliefs (represented as subjective probability measures), the expected utility of an action, or option, is the sum of the utilities of the outcomes of the action weighted by the probability that they will occur (according to the agent’s beliefs). The recommendation is to choose the action that maximizes this weighted average. This idea underlies the Bayesian view on practical rationality, and can be straightforwardly defined in type spaces.[12]  We start by defining expected utility for a player in a game.