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arXiv cs.AI· Avid Fayaz, Harry Zhang, H{\aa}vard Alstadheim, Erik Nordl\"of, Vilhelm Karlin, Andr\'e Silva·· 3 小时前AI 评分57

arXiv 论文研究交易智能体群中的测试时通信与羊群效应

Test-Time Communication in Trading Agent Swarms

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arXiv 论文(arXiv:2610.37448)研究了交易智能体群中的测试时通信:来自四个模型家族的 200 个智能体以十只一组、在五种通信机制下运行七个美国交易日(2026 年 10 月 1 日至 9 日)。

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Abstract:Communication between agents has mostly been studied on tasks with a shared objective and fast, clear feedback. Trading has neither: each agent manages its own account, with decisions being scored by markets late and noisily. A trading swarm is thus a population of independent decision-makers rather than a team, for which convergence is a risk and not the goal. We ran 200 agents from four model families in groups of ten under five communication regimes, from isolation to message boards and shared notes, over seven US trading days (1 to 9 October 2026), logging everything each agent read before every decision. In two of the four families, communicating groups came to hold far more of the same stocks than the isolated group. A group's return was largely the return on what its members held in common. Seeing a post from a groupmate made an agent 7 percentage points more likely to reuse an eight-word phrase from it. Agents that started too close together to see each other's posts shared such a phrase in 14% of cases, the rate that common inputs produce. Seeing the post did not detectably change that morning's trades. Case studies traced the adoption of positions and decision rules over subsequent days, and communicating groups sold in step more often than isolated ones. Because isolated groups also converged on the same stocks, sometimes completely, assessments of herding need to measure excess convergence relative to a no-peer baseline.
Comments: 31 pages, 10 figures, 10 tables
Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2610.37448 [cs.MA]
  (or arXiv:2610.37448v1 [cs.MA] for this version)
  https://doi.org/10.48550/arXiv.2610.37448

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Avid Fayaz [view email]
[v1] Tue, 27 Oct 2026 15:54:05 UTC (166 KB)

来源:arXiv cs.AI · arxiv.org