Allocative Efficiency in Online Auctions: Improving the Performance of Multiple Online Auctions Via Seek-and-Protect Agents

• 成果类型: Article
• 署名作者: Bapna, Ravi; Day, Robert; Rice, Sarah
• 署名单位: University of Connecticut; Texas A&M University System; Texas A&M University College Station; Mays Business School
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13194
• 发表日期: 2020
• 页码: 1878-1893
• 关键词: internet auctions; rules

摘要

Much of the prominent literature describing behavior in eBay-like marketplaces emphasizes the successful use of sniping agents that wait until the last moments of an auction to bid (truthfully) on behalf of a human user. These agents fare well against naive agents (typically assumed to be those who bid incrementally on the most profitable open auction) who do not get the chance to respond to the snipe-bid placed in the final seconds. This reasoning, however, tends to ignore the effect of the poor coordination that occurs as more and more players attempt the sniping agent strategy, thereby raising prices above their minimum possible competitive equilibrium levels. Using proprietary data purchased from eBay, encompassing all bids submitted on four specific product types over a 3-month period, we analyze the allocative efficiency, price, and bidder surplus using a software agent and compare this to the historical performance. After showing a significant amount of money left on the table in the historical record, we proceed to demonstrate how bidders can significantly improve their surplus (i.e., observed profit) by adopting a seek-and-protect agent. If bidders go further and implement sequential-auction shading strategies, they can incrementally improve their surplus, but sometimes at the expense of allocative efficiency. Acknowledging that each bidder's time window of interest is inherently unobservable, we vary the length of bidders' consumption windows and find similar results.