Seller-Directed Descending Price Auction: A Novel Welfare-Maximizing Trading Mechanism for Blockchain-Based Data Markets

The emergence of blockchain-based data markets has revolutionized traditional centralized data trading paradigms. By leveraging immutable ledgers, transparent transactions, and peer-to-peer exchanges that eliminate intermediary fees, these markets progressively enhance transactional trust. Nevertheless, establishing efficient consensus on data value remains challenging for conventional approaches, particularly regarding high-value assets. While auctions offer viable solutions, they typically face dual dilemmas with such homogeneous and infinitely replicable goods as data, that is, ineffective trading mechanisms and inadequate evaluation methodologies. This motivates our work. In this paper, we propose a novel evaluation metric-Auction Utilitarian Welfare with Externalities (AUWE)-specifically designed for data markets. AUWE optimizes collective welfare by incorporating incentive compatibility and theoretically validating tripartite benefits (data owners, users, and externalities). To maximize AUWE, we also develop a Seller-directed Descending Price (SD-DP) auction mechanism tailored to blockchain's information-symmetric environments. This mechanism can achieve optimal resource allocation, minimize price volatility, and approximate incentive compatibility. Furthermore, we establish theoretical strategies for buyers and sellers to stabilize data prices. Through simulations, we compare SD-DP with uniform-price and discriminatory-price auctions in traceable blockchain networks, and analyze bidder strategies under perfect information using our AUWE metrics. The results demonstrate SD-DP Auction's superiority in welfare outcomes and price stability, especially for high-value data transactions.