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Disagreeing About What the Buyer Might Learn

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  • Jonathan Libgober

Abstract

I introduce a robustness criterion for settings where players may disagree about the distribution over signals induced by endogenously chosen information. In the spirit of Hansen and Sargent (2008), an information choice is evaluated by its worst-case payoff over beliefs within an $\eta$-entropy ball around the signal distribution it induces. I apply this formulation to buyer-optimal learning in bilateral trade (Roesler and Szentes, 2017). If trade is always efficient, disagreement raises the price and twists the buyer's demand curve while preserving full trade. When trade may be inefficient, the same characterization holds conditional on trade with an endogenously amplified disagreement level.

Suggested Citation

  • Jonathan Libgober, 2026. "Disagreeing About What the Buyer Might Learn," Papers 2609.33057, arXiv.org.
    • Handle: RePEc:arx:papers:2609.33057
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    References listed on IDEAS

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    1. Benjamin Brooks & Songzi Du, 2021. "Optimal Auction Design With Common Values: An Informationally Robust Approach," Econometrica, Econometric Society, vol. 89(3), pages 1313-1360, May.
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