当托运人成为算法：候选者曝光、信息设计与大语言模型介导的货运市场集中度
When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
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中文总结 AI 辅助
研究托运人委托大语言模型代理选择承运人对货运市场的影响及平台设计应对策略，通过基于代理的模拟发现代理趋同、集中度随候选列表数量变化等风险，披露承运人剩余日运力可有效应对，凸显平台信息设计的重要性。
中文摘要 AI 辅助
托运人开始将承运人选择委托给大语言模型（LLM）代理。我们研究这种委托对货运匹配市场有何影响，以及哪些平台设计选择能对其加以控制。我们进行了基于代理的模拟，五十个基于OpenAI（GPT）、Anthropic（Claude）和谷歌（Gemini）商业LLM构建的托运人代理采购三十天的卡车运力。市场遵循数字货运匹配规则。研究发现三个风险及一个有效的补救措施。代理会迅速趋同，当展示的候选承运人列表超过约十个时，集中度急剧上升，不同模型的起始点不同。最终占主导地位的承运人在不同抽样市场差异很大，披露承运人剩余日运力可降低集中度并增加托运人盈余，而供应商多样化、列表顺序随机化和人气展示效果不明显。平台信息设计是关键因素。
英文摘要
Shippers are beginning to delegate carrier selection to large language model (LLM) agents. We ask what such delegation does to a freight matching market, and which platform design choices contain it. We carried out agent-based simulations in which fifty shipper agents, built on commercial LLMs from OpenAI (GPT), Anthropic (Claude), and Google (Gemini), procure truckload capacity for thirty days. The market implements the rules of digital freight matching: each load is offered down the shipper's ranked list of carriers (waterfall tendering), carriers have daily capacity limits, spot prices respond to congestion, and carrier ratings accumulate with transactions. We found three risks and one remedy that works. Agents converged at once: for a fixed sampled carrier population, the same carrier was the modal first choice of every model on day one, attracting up to 76% of requests. Because each agent picks from its own randomly drawn list of displayed candidates, the platform controls how many options each shipper sees; concentration rose steeply once lists exceeded about ten carriers, with the onset differing across models. Which carriers ended up dominant varied widely from one sampled market to another, and displaying true quality instead of estimated ratings changed neither the level nor this variability (by design, quality affects only what agents see, never delivery outcomes). Against these risks, disclosing each carrier's remaining daily capacity cut concentration by a third and doubled shipper surplus, while vendor diversification, list-order randomization, and popularity display showed no clearly detectable effect. Platform information design, ahead of model choice or model regulation, is the lever that works.
发表机构
- Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先进科学与技术研究中心）
- Department of Aeronautics and Astronautics, School of Engineering, The University of Tokyo（东京大学工学部航空宇宙学系）
机构由 AI 辅助整理，请以论文原文为准。
