Consider a problem in which you and a group of other experts must report your individual predictive distributions for an observable random variableXto some decision maker. Suppose that the report of each expert is assigned a prior weight by the decision maker and that these weights are then updated based on the observed value ofX. In this situation you will try to maximize your updated, or posterior, weight by appropriately choosing the distribution that you report, rather than necessarily simply reporting your honest predictive distribution. We study optimal reporting strategies under various conditions regarding your knowledge and beliefs aboutXand the reports of the other experts, and under various utility functions for your posterior weight. We present the only utility functions for which it is always optimal to report your honest predictive distribution. Attention is restricted to problems in whichXcan take only a finite number of values.