Data-Driven Auction Design for Blockchain-Based Digital Asset Trading: A Mixed Method

Abstract

This paper is motivated by a real project with a leading culture assets and equity swap orga nization in South China. Our preliminary survey assesses the potential of digital collectibles swap, with professionals aiming to boost liquidity and consumers driven by social attributes and meta-item diversity. In our setting, each agent owns a digital asset and wants another meta-item. However, the traditional Vickrey-Clarke-Groves (VCG) auction runs at a deficit. We thus introduce a novel mechanism that combines the VCG auction with limited supply and platform escrow concepts, called LSE-VCG auction. To improve the surplus of plat form (auctioneer), we use limited supply to constrain the number of winners and platform escrow to increase market demand. The LSE-VCG auction satisfies both truthful telling and participation rationality. If multilateral matching achieves maximal social welfare, then the substitute condition does not hold (impossibility theorem). We prove that the platform's surplus can be improved by limited supply in some conditions. Our experimental results show that the VCG auction solely with limited supply could reach greater social welfare, agents'profits and ratio of swap relative to the sequential Vickrey auctions. Moreover, a mix of limited supply and platform escrow schemes can further improve the platform's profit and successful trading ratio. Truthful telling is almost an optimal strategy for the platform, thus promoting a transparent and beneficial auction environment. Besides, the impacts of auction timing, market size, value distribution and size of XOR bids are investigated. Fi nally, we apply an effective machine learning method to predict the limited supply number with partial information before the auction.

Keywords: Metaverse swap, Auction design, Platform escrow, Limited supply, Incentive compatibility, Data-driven methods