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Understanding LLM-Based Bargaining of Driving Agents in Mixed-Autonomy Traffic — arXiv:2610.41239

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.41239
License (as archived): CC0-1.0
Archived: 2026-11-03T07:29:17.733Z
SHA-256 of the archived text: c322ee728d327bb9eee7625fb734b2519d2215fb4da83f098e8505f534e4f822
Stable URL of this page: https://metraipolis.com/documento/dc_bb8ea22a79e4

How to cite

Understanding LLM-Based Bargaining of Driving Agents in Mixed-Autonomy Traffic — arXiv:2610.41239 — arXiv (en), https://arxiv.org/abs/2610.41239 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bb8ea22a79e4 (Metraipolis archive dc_bb8ea22a79e4, sha256 c322ee728d327bb9eee7625fb734b2519d2215fb4da83f098e8505f534e4f822)

BibTeX

@misc{dcbb8ea22a79e4,
  title = {Understanding LLM-Based Bargaining of Driving Agents in Mixed-Autonomy Traffic — arXiv:2610.41239},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.41239},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bb8ea22a79e4; sha256 c322ee728d327bb9eee7625fb734b2519d2215fb4da83f098e8505f534e4f822},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.41239. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large Language Models (LLMs) offer a flexible computational approach for modeling social interactions among heterogeneous agents. This paper develops a novel framework towards characterizing and understanding LLM-based population-level bargaining of shared road space between autonomous vehicle (AV) and human-driven vehicle (HV) agents in mixed-autonomy traffic. Through controlled experiments varying traffic density, agent objectives, and traffic-domain knowledge, we first characterize bargaining outcomes and observe heterogeneous agreement rates across different experimental conditions. To understand how the bargaining process shapes the outcomes, we introduce a behavioral taxonomy that jointly characterizes numerical bidding actions and communicative attitudes. We examine two complementary aspects of social interaction: inter-agent bargaining dynamics and private-to-public behavioral transformation. Results reveal distinct interaction patterns associated with agreement and breakdown, including greater constructive reciprocity in agreement trials and more frequent conflict escalation in breakdown trials. Furthermore, private and public communicative attitudes often differ even when numerical bids remain unchanged. These findings illustrate how LLM-based bargaining simulations can support a structured analysis of social interaction behaviors in mixed-autonomy traffic.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Physics and Society (physics.soc-ph) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.41239 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T07:29:17.661Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).