A Truthful Referral Auction Over Networks
Youjia Zhang, Pingzhong Tang

TL;DR
This paper introduces TRDM, a mechanism that incentivizes truthful social network reporting and improves revenue in referral auctions, addressing misreporting and competition issues while ensuring efficiency and budget balance.
Contribution
The paper proposes TRDM, a novel truthful mechanism for referral auctions that enhances revenue, incentivizes honest reporting, and maintains efficiency and budget balance.
Findings
TRDM guarantees truthful network reporting.
TRDM achieves higher revenue than non-referral mechanisms.
TRDM is budget-balanced and socially efficient.
Abstract
This paper studies a mechanism design problem over a network, where agents can only participate by referrals. The Bulow-Klemberer theorem proposes that expanding the number of participants is a more effective approach to increase revenue than modifying the auction format. However, agents lack the motivation to invite others because doing so intensifies competition among them. On the other hand, misreporting social networks is also a common problem that can reduce revenue. Examples of misreporting include Sybil attacks (an agent pretending to be multiple bidders) and coalition groups (multiple agents pretending to be an agent). To address these challenges, we introduce a novel mechanism called the Truthful Referral Diffusion Mechanism (TRDM). TRDM incentivizes agents to report their social networks truthfully, and some of them are rewarded by the seller for improving revenue. In spite of…
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Taxonomy
TopicsAuction Theory and Applications · Experimental Behavioral Economics Studies · Game Theory and Applications
