arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2610.20587cs.GTcs.AIcs.LG
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智能体市场中第一价格拍卖的无政府状态价格

Price of Anarchy of First-Price Auctions in Agentic Markets

Google DeepMind(谷歌DeepMind);Google Research(谷歌研究)

Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal, Ian Gemp, Michael Kaisers, Adam Fisch

AI总结:

本文研究智能体市场中第一价格拍卖的无政府状态价格,发现竞争底价和价值近独立性可降低福利损失,优于一般情况,且通过EmbedLLM数据集的模拟验证了结果。

摘要:

智能体市场是一种去中心化的服务时间市场,其中AI智能体通过竞价来争夺每个用户的提示词。智能体的竞价基于其对该提示词的预期价值,即其响应的奖励减去推理成本的期望值。第一价格拍卖(FPAs)是分配提示词的自然方式,因为它运行简单透明,且不需要掌握私有能力和成本的中心知识。然而,FPAs并非真实的:参与者可能会以低于其真实价值的竞价,以期获得更大利润,此时提示词可能分配给并非最重视它的竞价者。这种福利损失由无政府状态价格(PoA)量化。本文中,我们认为智能体市场可能具有降低无政府状态价格的特征。第一个特征是竞争底价,即当多个竞价者通常对提示词的估值高于最优福利的某个基准份额时出现(例如,因为最优模型通常被蒸馏为性能相似但成本更低的替代模型)。我们证明,它们之间的竞争会限制福利损失,且随着底价上升,福利损失可趋近于零。第二个特征是在给定提示词后价值的近独立性,即当预期价值的大部分可预测变化由提示词的难度决定时出现。随着价值分布趋近于条件独立性,我们可推导出效率保证,其接近最优福利的1 - 1/e²倍。因此,这两个特征产生的福利下界明显优于著名的一般情况1 - 1/e。我们在EmbedLLM提示词路由数据集上模拟策略竞价者的拍卖,对这些结果进行了实证验证。

英文摘要:

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 $\textit{price-of-anarchy}$ (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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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