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General Truthfulness Characterizations via Convex Analysis

  • Rafael Frongillo
  • Ian Kash
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8877)

Abstract

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.

Keywords

Convex Function Mechanism Design Convex Analysis Allocation Rule Full Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rafael Frongillo
    • 1
  • Ian Kash
    • 1
  1. 1.Microsoft ResearchUSA

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