 How should decision theory interpret the probability of a state \(S\) if one performs an act \(A\), that is, \(P(S \mbox{ if }A)\)? Probability theory offers a handy suggestion. It has an account of conditional probabilities that decision theory may adopt. Decision theory may take \(P(S \mbox{ if }A)\) as the probability of the state conditional on the act. Then \(P(S \mbox{ if }A)\) equals \(P(S\mid A)\), which probability theory defines as \(P(S \amp A)/P(A)\) when \(P(A) \ne 0\). Some theorists call expected utility computed using conditional probabilities conditional expected utility. I call it expected utility tout court because the formula using conditional probabilities generalizes a simpler formula for expected utility that uses nonconditional probabilities of states. Also, some theorists call an act’s expected utility its utility tout court because an act’s expected utility appraises the act and yields the act’s utility in ideal cases. I call it expected utility because a person by mistake may attach more or less utility to a bet than its expected utility warrants. The equality of an act’s utility and its expected utility is normative rather than definitional.