Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework

• URL: http://arxiv.org/abs/2411.15356v2
• Date: Sat, 21 Dec 2024 12:43:59 GMT
• Title: Regulator-Manufacturer AI Agents Modeling: Mathematical Feedback-Driven Multi-Agent LLM Framework
• Authors: Yu Han, Zekun Guo,
• Abstract summary: This study employs a multi-agent modeling approach, enhanced with Large Language Models (LLMs) to simulate regulatory dynamics. Our findings illuminate the influence of regulatory shifts on industry behaviour and identify strategic opportunities for improving regulatory practices.
• Score: 3.9479574991566193
• License:
• Abstract: The increasing complexity of regulatory updates from global authorities presents significant challenges for medical device manufacturers, necessitating agile strategies to sustain compliance and maintain market access. Concurrently, regulatory bodies must effectively monitor manufacturers' responses and develop strategic surveillance plans. This study employs a multi-agent modeling approach, enhanced with Large Language Models (LLMs), to simulate regulatory dynamics and examine the adaptive behaviors of key actors, including regulatory bodies, manufacturers, and competitors. These agents operate within a simulated environment governed by regulatory flow theory, capturing the impacts of regulatory changes on compliance decisions, market adaptation, and innovation strategies. Our findings illuminate the influence of regulatory shifts on industry behaviour and identify strategic opportunities for improving regulatory practices, optimizing compliance, and fostering innovation. By leveraging the integration of multi-agent systems and LLMs, this research provides a novel perspective and offers actionable insights for stakeholders navigating the evolving regulatory landscape of the medical device industry.

Related papers

• Regulatory Science Innovation for Generative AI and Large Language Models in Health and Medicine: A Global Call for Action [10.124390106392742]
  The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges. We discuss the constraints of the TPLC approach to GenAI and LLM-based medical device regulation. This serves as the foundation for developing innovative approaches including adaptive policies and regulatory sandboxes.
  arXiv  Detail & Related papers  (2025-01-27T06:21:13Z)
• Do as We Do, Not as You Think: the Conformity of Large Language Models [46.23852835759767]
  This paper presents a study on conformity in large language models (LLMs) driven collaborative AI systems. We focus on three aspects: the existence of conformity, the factors influencing conformity, and potential mitigation strategies. Our analysis delves into factors influencing conformity, including interaction time and majority size, and examines how the subject agent rationalizes its conforming behavior.
  arXiv  Detail & Related papers  (2025-01-23T04:50:03Z)
• The New Anticipatory Governance Culture for Innovation: Regulatory Foresight, Regulatory Experimentation and Regulatory Learning [0.0]
  This article advances scholarship on innovation policy and the regulation of technological innovation in the European Union. It systematically excavates a variety of tools and elements that are being put into use in inventive ways. It argues that these need to be more cohesively and systemically integrated into the regulatory toolbox.
  arXiv  Detail & Related papers  (2025-01-10T12:26:38Z)
• RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
  We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages. We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents. We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
  arXiv  Detail & Related papers  (2024-09-09T14:44:19Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• Accelerating Radio Spectrum Regulation Workflows with Large Language Models (LLMs) [0.0]
  This paper demonstrates example applications of Large Language Models (LLMs) to expedite spectrum regulatory processes. We explore various roles that LLMs can play in this context while identifying some of the challenges to address.
  arXiv  Detail & Related papers  (2024-03-26T15:54:48Z)
• Agent Alignment in Evolving Social Norms [65.45423591744434]
  We propose an evolutionary framework for agent evolution and alignment, named EvolutionaryAgent. In an environment where social norms continuously evolve, agents better adapted to the current social norms will have a higher probability of survival and proliferation. We show that EvolutionaryAgent can align progressively better with the evolving social norms while maintaining its proficiency in general tasks.
  arXiv  Detail & Related papers  (2024-01-09T15:44:44Z)
• Uncovering Regulatory Affairs Complexity in Medical Products: A Qualitative Assessment Utilizing Open Coding and Natural Language Processing (NLP) [3.8657431480664717]
  The study involved semi-structured interviews with 28 professionals from medical device companies. The participants highlighted the need for strategies to streamline regulatory compliance. The study concludes that these elements are vital for establishing coherent and effective regulatory procedures.
  arXiv  Detail & Related papers  (2023-12-30T03:39:57Z)
• Regulatory Markets: The Future of AI Governance [0.7230697742559377]
  Overreliance on industry self-regulation fails to hold producers and users accountable to democratic demands. This approach to AI regulation could overcome the limitations of both command-and-control regulation and self-regulation.
  arXiv  Detail & Related papers  (2023-04-11T01:05:55Z)
• Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety [69.59465535312815]
  Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcome-based targets for AI companies to achieve. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal.
  arXiv  Detail & Related papers  (2023-03-06T14:42:05Z)
• Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer [128.19212716007794]
  We propose a Transformer-based Multi-Agent Actor-Critic framework (T-MAAC) to stabilize voltage in power distribution networks. In addition, we adopt a novel auxiliary-task training process tailored to the voltage control task, which improves the sample efficiency.
  arXiv  Detail & Related papers  (2022-06-08T07:48:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.