arXiv：cs.LG· Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal, Ian Gemp, Michael Kaisers, Adam Fisch·· 3 小时前
智能体市场中的第一价格拍卖：无政府状态代价分析
Price of Anarchy of First-Price Auctions in Agentic Markets
AI 导读
论文分析了AI智能体在服务时市场中以第一价格拍卖（FPA）竞价认领用户提示词时的福利损失（无政府状态代价，PoA）。
正文
Abstract: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 $\textit{price-of-anarchy}$ (PoA). In this paper, we argue that agentic markets are likely to possess features that reduce the price-of-anarchy. The first is a $\textit{competitive floor}$, which occurs when multiple bidders usually value the prompt above some baseline share of the optimal welfare (e.g., because the best models are often distilled into cheaper alternatives, which perform similarly). We show that competition among them then limits the welfare loss, which can be driven to zero as the floor rises. The second feature is $\textit{near-independence}$ of values after conditioning on the prompt, which occurs when most of the predictable variation in the expected value is determined by the difficulty of the prompt. As the value distribution approaches conditional independence, we can derive an efficiency guarantee that approaches a factor of $1 - 1/e^2$ of the optimal welfare. Thus, both features give rise to welfare lower bounds that are significantly better than the well-known general case of $1 - 1/e$. We validate these results empirically using auctions with strategic bidders simulated on the EmbedLLM prompt routing dataset.
| Comments: | 40 pages |
| Subjects: | Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20587 [cs.GT] |
| (or arXiv:2610.20587v1 [cs.GT] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20587 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shubhendu Trivedi [view email]
[v1]
Wed, 14 Oct 2026 17:57:57 UTC (145 KB)
来源：arXiv：cs.LG · arxiv.org