General Truthfulness Characterizations via Convex Analysis

We present a model of truthful elicitation which generalizes and extends mechanisms, scoring rules, and a number of related settings that do not quite qualify as one or the other. Our main result is a characterization theorem, yielding characterizations for all of these settings, including a new characterization of scoring rules for non-convex sets of distributions. We generalize this model to eliciting some property of the agent’s private information, and provide the first general characterization for this setting. We also show how this yields a new proof of a result in mechanism design due to Saks and Yu.