AI 中文总结
该研究针对无需人工干预的广告市场，提出用Eisenberg-Gale凸规划的一次性定价方案替代多轮拍卖，可简化操作并实现交易平台收益最优。
AI 中文摘要
按展示付费拍卖长期以来一直是在线广告中的分配机制。我们认为，在“无需人工干预（HOTW）”市场中，广告商申报预算和“投资回报率（ROI）”目标，而广告交易平台的机器学习模型会预测点击价值，此时拍卖已不再必要：交易平台已掌握最优定价所需的全部信息，其作为垄断者在向下倾斜的需求曲线下进行定价。HOTW市场属于Fisher市场，其竞争均衡可通过Eisenberg-Gale凸规划计算，得到的市场出清价格和分配方案能同时满足所有预算和ROI约束。在所有统一价格机制中，该竞争均衡价格对交易平台而言是收益最优的，可避免典型统一价格多单元拍卖中的需求缩减问题。此外，该竞争均衡与带 pacing 因子的序贯第一价格拍卖结果等价，pacing 乘数可由交易平台事前计算。这种一次性方法用一个凸规划替代了数百万次单独拍卖，不仅比动态演化的竞价策略操作更简单，还能在实现相同均衡结果的同时为交易平台带来最优收益。
英文摘要
Per-impression auctions have long served as the allocation mechanism in online advertising. We argue that in ``hands-off-the-wheel'' (HOTW) markets, where advertisers declare budgets and ``return-on-investment'' (ROI) targets and the exchange's ML models predict click values, auctions are no longer necessary: all information required for optimal pricing is already known to the exchange, which occupies the position of a monopolist pricing against a downward-sloping demand curve. A HOTW market is a Fisher market, whose competitive equilibrium can be computed via the convex program of Eisenberg and Gale, yielding market-clearing prices and allocations satisfying all budget and ROI constraints simultaneously. This competitive-equilibrium price is revenue-optimal for the exchange among all uniform-price mechanisms: avoiding the demand reduction problem in typical uniform-price multi-unit auctions. The resulting competitive equilibrium is moreover outcome-equivalent to sequential first-price auctions with pacing, with pacing multipliers computable ex-ante by the exchange. This one-shot approach replaces millions of individual auctions with one convex program, which is not only operationally simpler than dynamically evolving bidding strategies, but revenue-optimal for the exchange, while delivering the same equilibrium outcome.
