A Truthful Referral Auction Over Networks

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 the fact that some agents overbid in TRDM, the revenue is fixed, and it is higher than the revenue of any mechanism without referrals. TRDM is budget-balanced (non-negative revenue) and generates an efficient outcome (maximized social welfare), making it attractive for both the seller and the buyers as it improves revenue and reward.