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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Accesso online: | https://arxiv.org/abs/2509.09906 |
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| _version_ | 1866916947282624512 |
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| author | Fetsch, Alexandra Savvateev, Iurii Romdhane, Racem Ben Wiedmann, Martin Dimov, Artemiy Durkalec, Maciej Teichmann, Josef Zinsstag, Jakob Koutsoumanis, Konstantinos Rajkovic, Andreja Mann, Jason Tonolla, Mauro Ehling-Schulz, Monika Filter, Matthias Johler, Sophia |
| author_facet | Fetsch, Alexandra Savvateev, Iurii Romdhane, Racem Ben Wiedmann, Martin Dimov, Artemiy Durkalec, Maciej Teichmann, Josef Zinsstag, Jakob Koutsoumanis, Konstantinos Rajkovic, Andreja Mann, Jason Tonolla, Mauro Ehling-Schulz, Monika Filter, Matthias Johler, Sophia |
| contents | Key global challenges of our times are characterized by complex interdependencies and can only be effectively addressed through an integrated, participatory effort. Conventional risk analysis frameworks often reduce complexity to ensure manageability, creating silos that hinder comprehensive solutions. A fundamental shift towards holistic strategies is essential to enable effective negotiations between different sectors and to balance the competing interests of stakeholders. However, achieving this balance is often hindered by limited time, vast amounts of information, and the complexity of integrating diverse perspectives. This study presents an AI-assisted negotiation framework that incorporates large language models (LLMs) and AI-based autonomous agents into a negotiation-centered risk analysis workflow. The framework enables stakeholders to simulate negotiations, systematically model dynamics, anticipate compromises, and evaluate solution impacts. By leveraging LLMs' semantic analysis capabilities we could mitigate information overload and augment decision-making process under time constraints. Proof-of-concept implementations were conducted in two real-world scenarios: (i) prudent use of a biopesticide, and (ii) targeted wild animal population control. Our work demonstrates the potential of AI-assisted negotiation to address the current lack of tools for cross-sectoral engagement. Importantly, the solution's open source, web based design, suits for application by a broader audience with limited resources and enables users to tailor and develop it for their own needs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building Fetsch, Alexandra Savvateev, Iurii Romdhane, Racem Ben Wiedmann, Martin Dimov, Artemiy Durkalec, Maciej Teichmann, Josef Zinsstag, Jakob Koutsoumanis, Konstantinos Rajkovic, Andreja Mann, Jason Tonolla, Mauro Ehling-Schulz, Monika Filter, Matthias Johler, Sophia Multiagent Systems Artificial Intelligence Key global challenges of our times are characterized by complex interdependencies and can only be effectively addressed through an integrated, participatory effort. Conventional risk analysis frameworks often reduce complexity to ensure manageability, creating silos that hinder comprehensive solutions. A fundamental shift towards holistic strategies is essential to enable effective negotiations between different sectors and to balance the competing interests of stakeholders. However, achieving this balance is often hindered by limited time, vast amounts of information, and the complexity of integrating diverse perspectives. This study presents an AI-assisted negotiation framework that incorporates large language models (LLMs) and AI-based autonomous agents into a negotiation-centered risk analysis workflow. The framework enables stakeholders to simulate negotiations, systematically model dynamics, anticipate compromises, and evaluate solution impacts. By leveraging LLMs' semantic analysis capabilities we could mitigate information overload and augment decision-making process under time constraints. Proof-of-concept implementations were conducted in two real-world scenarios: (i) prudent use of a biopesticide, and (ii) targeted wild animal population control. Our work demonstrates the potential of AI-assisted negotiation to address the current lack of tools for cross-sectoral engagement. Importantly, the solution's open source, web based design, suits for application by a broader audience with limited resources and enables users to tailor and develop it for their own needs. |
| title | Tackling One Health Risks: How Large Language Models are leveraged for Risk Negotiation and Consensus-building |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2509.09906 |