Signaling in Posted Price Auctions
Matteo Castiglioni, Giulia Romano, Alberto Marchesi, Nicola Gatti

TL;DR
This paper investigates signaling strategies in Bayesian posted price auctions with sequential buyers, demonstrating computational hardness and proposing PTAS algorithms for revenue maximization under public and private signaling.
Contribution
The paper introduces a unifying framework for signaling in auctions, proves hardness results, and develops PTAS algorithms for both public and private signaling scenarios.
Findings
Maximizing revenue is NP-hard even for single-buyer cases.
A unifying framework simplifies the analysis of signaling strategies.
PTAS algorithms are developed using linear programming and ellipsoid methods.
Abstract
We study single-item single-unit Bayesian posted price auctions, where buyers arrive sequentially and their valuations for the item being sold depend on a random, unknown state of nature. The seller has complete knowledge of the actual state and can send signals to the buyers so as to disclose information about it. For instance, the state of nature may reflect the condition and/or some particular features of the item, which are known to the seller only. The problem faced by the seller is about how to partially disclose information about the state so as to maximize revenue. Unlike classical signaling problems, in this setting, the seller must also correlate the signals being sent to the buyers with some price proposals for them. This introduces additional challenges compared to standard settings. We consider two cases: the one where the seller can only send signals publicly visible to…
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Taxonomy
TopicsAuction Theory and Applications · Game Theory and Voting Systems · Consumer Market Behavior and Pricing
