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Knowledge-Based Mechanisms

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Listed:
  • Yutong Zhang
  • Yangfan Zhou

Abstract

We study robust mechanisms when the designer possesses a Bayesian belief over some components of agents' private information but faces ambiguity over others. The designer evaluates mechanisms by their worst-case performance over all joint distributions consistent with her belief over the Bayesian components. The framework encompasses settings such as multidimensional delegation in which a principal knows the distribution of the state but not the agent's preferences (e.g., his tradeoffs across dimensions), screening in which a seller only has misspecified estimates of buyer preferences, and auction and voting design when agents' beliefs about each other are ambiguous to the designer. We provide conditions under which a \emph{knowledge-based} mechanism---one that conditions only on the Bayesian components but not the ambiguous ones---is robustly optimal. Our results unify earlier work across distinct economic environments and uncover new applications.

Suggested Citation

  • Yutong Zhang & Yangfan Zhou, 2026. "Knowledge-Based Mechanisms," Papers 2609.03439, arXiv.org.
    • Handle: RePEc:arx:papers:2609.03439
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    References listed on IDEAS

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