[论文] Price of Anarchy of First-Price Auctions in Agentic Markets

论文概要

研究领域: ML
作者: Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal, Ian Gemp, Michael Kaisers
发布时间: 2026-10-14
arXiv: 2610.20587

中文摘要

智能体市场是一个去中心化的服务时市场，AI智能体在其中竞价以获取每个用户提示。智能体的出价基于其对提示的期望价值，即其响应的期望奖励减去推理成本。一价拍卖（FPA）中出价最高者支付其出价并获得提示，是此类市场中分配提示的自然方式，因为它简单透明，且不需要集中了解私有能力和成本。然而FPA不具有真实性：参与者可能出价低于真实价值以期获得更大利润，提示可能被分配给并非最看重它的人。这种福利损失由无序代价（PoA）量化。本文论证智能体市场可能具有降低无序代价的特征。第一个是竞争底线：当多个出价者通常对提示的估值高于最优福利的某个基线份额时（例如因为最好的模型常被蒸馏为更便宜的替代方案，性能相近），它们之间的竞争会限制福利损失，且随底线提高可趋近于零。第二个特征是条件化于提示后价值的近似独立性：当期望价值中大部分可预测变化由提示难度决定时发生。当价值分布趋近条件独立时，我们可推导出趋近1-1/e^2因子的最优福利效率保证。因此，两个特征产生的福利下界都显著优于众所周知的1-1/e一般情形。我们在EmbedLLM提示路由数据集上使用策略性出价者模拟拍卖来验证这些结果。

原文摘要

An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value for the prompt, which is the expectation of the reward for its response minus its inference costs. First-price auctions (FPAs), in which the highest bidder pays its bid and receives the prompt, are a natural way to allocate prompts in such a market, since they are simple and transparent to run, and do not require central knowledge of private capabilities and costs. However, FPAs are not truthful: participants may bid less than their true value in the hope of a larger profit, and the prompt may then be allocated to someone other than the bidder who values it the most. This welfare loss is quantified by the price-of-anarchy (PoA). In this paper...

自动采集于 2026-10-16

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