Mathematical expectation has traditionally been used in solving risk‐based decision making problems. However, this concept is not appropriate for decision making that affects public policy because it conceals extremes by commensurating events of different magnitudes and probabilities of occurrence. If a decision maker instead uses an expected value function in addition to a conditional expectation that captures extreme and catastrophic events, he will gain a more meaningful and more encompassing representation of the trade‐offs at hand. A theory relating conditional expectation to the statistics of extremes is developed in this paper. This expectation can now be viewed as the conditional expected risk given the occurrence of an event with a return period that equals or exceedsnyears. The theory highlights the importance of using both the conditional and the unconditional expected risk in decision making. This fact has previously been recognized in the partitioned multi‐objective risk method (PMRM), a risk analysis methodology based on the concept of conditional expectation. The theory proposed in this paper provides a formulation for analyzing the sensitivity of subjectively chosen parameters in the PMRM. This theory will also simplify the practical implementation of the m