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

• URL: http://arxiv.org/abs/2411.15356v1
• Date: Fri, 22 Nov 2024 22:02:56 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: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

• Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios. Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
  arXiv  Detail & Related papers  (2025-04-11T15:41:21Z)
• Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective [0.0]
  Agentic systems powered by large language models (LLMs) are becoming progressively more complex and capable. Their increasing agency and expanding deployment settings attract growing attention over effective governance policies, monitoring and control protocols. We analyze the potential liability issues stemming from delegated use of LLM agents and their extended systems from a principal-agent perspective.
  arXiv  Detail & Related papers  (2025-04-04T08:10:02Z)
• Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models [75.4890331763196]
  Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems. LLM-based Agentic RS (LLM-ARS) can offer more interactive, context-aware, and proactive recommendations.
  arXiv  Detail & Related papers  (2025-03-20T22:37:15Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [54.787341008881036]
  We introduce Reinforced Meta-thinking Agents (ReMA), a novel framework that leverages Multi-Agent Reinforcement Learning (MARL) to elicit meta-thinking behaviors. ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions. Experimental results demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Improving Retrospective Language Agents via Joint Policy Gradient Optimization [57.35348425288859]
  RetroAct is a framework that jointly optimize both task-planning and self-reflective evolution capabilities in language agents. We develop a two-stage joint optimization process that integrates imitation learning and reinforcement learning. We conduct extensive experiments across various testing environments, demonstrating RetroAct has substantial improvements in task performance and decision-making processes.
  arXiv  Detail & Related papers  (2025-03-03T12:54:54Z)
• 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)
• Regulating Multifunctionality [0.0]
  Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability are unlikely to be strong candidates for responding to multifunctional AI's risks.
  arXiv  Detail & Related papers  (2025-01-26T00:50:27Z)
• 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)
• RegNLP in Action: Facilitating Compliance Through Automated Information Retrieval and Answer Generation [51.998738311700095]
  Regulatory documents, characterized by their length, complexity and frequent updates, are challenging to interpret. RegNLP is a multidisciplinary subfield aimed at simplifying access to and interpretation of regulatory rules and obligations. ObliQA dataset contains 27,869 questions derived from the Abu Dhabi Global Markets (ADGM) financial regulation document collection.
  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)
• Enhancing Legal Compliance and Regulation Analysis with Large Language Models [0.0]
  This research explores the application of Large Language Models (LLMs) to accurately classify legal provisions and automate compliance checks. Our findings demonstrate promising results, indicating LLMs' significant potential to enhance legal compliance and regulatory analysis efficiency, notably by reducing manual workload and improving accuracy within reasonable time financial constraints.
  arXiv  Detail & Related papers  (2024-04-26T16:40:49Z)
• 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)
• Transforming Competition into Collaboration: The Revolutionary Role of Multi-Agent Systems and Language Models in Modern Organizations [0.0]
  This article explores the influence of computational entities based on multi-agent systems theory (SMA) and large language models (LLM) on human user interaction. In our approach we employ agents developed from large language models (LLM), each with distinct prototyping that considers behavioral elements. We demonstrate the potential of developing agents useful for organizational strategies, based on multi-agent system theories (SMA) and innovative uses based on large language models (LLM based)
  arXiv  Detail & Related papers  (2024-03-12T15:56:10Z)
• 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)
• Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts [52.844741540236285]
  This paper investigates the model-based methods in multi-agent reinforcement learning (MARL) We propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy (AORPO)
  arXiv  Detail & Related papers  (2021-05-07T16:20:22Z)

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.