when-shippers-become-algorithms-candidate-exposure

name	when-shippers-become-algorithms-candidate-exposure
description	Skill generated from arXiv paper 2607.19967: When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets
metadata	{"arxiv":{"id":"2607.19967","title":"When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets","authors":["Takahiro Ezaki","Naoto Imura","Katsuhiro Nishinari"],"published":"2026-07-22","categories":["physics.soc-ph","cs.AI","cs.CY"],"url":"https://arxiv.org/abs/2607.19967","utility":1}}

When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets

arXiv: 2607.19967
Published: 2026-07-22
Authors: Takahiro Ezaki, Naoto Imura, Katsuhiro Nishinari
Categories: physics.soc-ph, cs.AI, cs.CY
Utility: 1.00

Key Innovation

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 ...

Potential Application

This paper presents advancements that could be applied to enhance agent capabilities in the areas of physics.soc-ph, cs.AI, cs.CY.

References

• arXiv: https://arxiv.org/abs/2607.19967