Probabilistic Mechanism Design in Diffusion Auctions
Xinlun Zhang, Zhechen Li, Yongzhi Cao, Yu Huang, Hanpin Wang

TL;DR
This paper introduces novel probabilistic mechanisms for diffusion auctions on social networks, achieving incentive compatibility, revenue, efficiency, and resistance to collusion and Sybil attacks, including extensions to multi-unit settings.
Contribution
The paper proposes the Probabilistic Diffusion Mechanism (PDM) for path graphs and extends it to general networks, addressing incentive compatibility, revenue, efficiency, and robustness against collusion and Sybil attacks.
Findings
PDM satisfies incentive compatibility, non-negative revenue, and approximate efficiency.
f-PDM preserves key properties and can be Sybil-proof under certain conditions.
MUPDM and SP-MUPDM effectively handle multi-unit auctions with robustness against attacks.
Abstract
A diffusion auction refers to a selling process conducted over a social network, where each participant submits a bid and may invite other potential buyers to join the auction. Although various mechanisms have been proposed, none of them can simultaneously achieve incentive compatibility, non-negative revenue, and approximate efficiency with a constant approximation bound. In this paper, we propose the Probabilistic Diffusion Mechanism (PDM), a novel mechanism tailored for path graphs, which satisfies all three desired properties. We further extend PDM to general network structures through a map , resulting in the -PDM mechanism, which preserves the key properties of the original design. Beyond these, when satisfies properties such as breadth-first order, -PDM also ensures Sybil-proofness and provides approximate revenue. Furthermore, to address buyer collusion, we…
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