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On the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management — arXiv:2610.23673

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.23673
License (as archived): CC0-1.0
Archived: 2026-10-19T08:38:48.634Z
SHA-256 of the archived text: 04ac1894f46c5346a998892ceca40ef060d7c81f0d57a61862d9157a619896a3
Stable URL of this page: https://metraipolis.com/documento/dc_7dee167eee27

How to cite

On the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management — arXiv:2610.23673 — arXiv (en), https://arxiv.org/abs/2610.23673 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7dee167eee27 (Metraipolis archive dc_7dee167eee27, sha256 04ac1894f46c5346a998892ceca40ef060d7c81f0d57a61862d9157a619896a3)

BibTeX

@misc{dc7dee167eee27,
  title = {On the possibilities and limitations of LLM-based multi-agent systems for multi-criteria decision-making in healthcare operations management — arXiv:2610.23673},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.23673},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7dee167eee27; sha256 04ac1894f46c5346a998892ceca40ef060d7c81f0d57a61862d9157a619896a3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.23673. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-criteria models for healthcare operations need weights that express how stakeholders trade off patient waiting time, provider workload, and operating cost, but these weights are difficult to quantify. Teams of large language model (LLM) agents, each prompted to represent a stakeholder, have been used to supply them. We propose a framework for assessing the reliability of LLM-generated preferences as inputs to multi-objective optimization and quantifying how their uncertainty affects decisions. We study explicit preferences by asking agents to rate the importance of the criteria in an outpatient appointment scheduling problem, and implicit preferences by inferring trade-offs from the agents' pairwise choices between emergency department configurations. The computational experiments show that the explicit preferences respond to the clinic's operating conditions and differ across roles, but deliberation among the agents can erase these differences, whereas simple averaging preserves them. The pairwise choices used to reveal the agents' implicit preferences depend largely on which option is presented first, and we find limited evidence that a statistical model using contextual information alongside presentation order predicts the agents' choices on new comparison pairs better than a model using presentation order alone. We then formulate robust weighted-sum optimization models to account for preference uncertainty. In most cases, these models select different schedules or configurations from those obtained using average preference weights. These results indicate that LLM-generated preferences need an audit of this kind before they are used as optimization weights in healthcare decision support.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Optimization and Control (math.OC) ; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.23673 — 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 2026-10-19T08:38:39.971Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).