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General Truthfulness Characterizations Via Convex Analysis

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We present a model of truthful elicitation which generalizes and extends both mechanisms and scoring rules. Our main result is a characterization theorem using the tools of convex analysis, of which characterizations of mechanisms and scoring rules represent special cases. Moreover, we demonstrate that a variety of existing results in the mechanism design literature are often simpler and more direct when first phrased in terms of convexity. We then generalize our main theorem to settings where agents report some alternate representation of their private information rather than reporting it directly, which gives new results about both scoring rules and mechanism design.

This talk is part of the Optimization and Incentives Seminar series.

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