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

Yu Han, Zekun Guo

TL;DR

This paper addresses how continuous regulatory updates impact medical device manufacturers and regulators. It proposes a two-layer framework that combines nonlinear transcendental-equation modeling of regulatory flow with LLM-based agent role-play to simulate regulator–manufacturer interactions, leveraging Regulatory Flow Theory. A BRR-based evaluation metric is used in a scenario with 10 manufacturer agents and 10 regulations (5 strict, 5 lenient), enabling sensitivity analyses to identify critical parameters that drive system dynamics. The findings show how resource constraints shape adaptive strategies, how dynamic BRR thresholds balance safety and innovation, and how the integrated MAS–LLM approach can inform regulatory practice and strategic industry planning across high-regulation domains.

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.

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

TL;DR

This paper addresses how continuous regulatory updates impact medical device manufacturers and regulators. It proposes a two-layer framework that combines nonlinear transcendental-equation modeling of regulatory flow with LLM-based agent role-play to simulate regulator–manufacturer interactions, leveraging Regulatory Flow Theory. A BRR-based evaluation metric is used in a scenario with 10 manufacturer agents and 10 regulations (5 strict, 5 lenient), enabling sensitivity analyses to identify critical parameters that drive system dynamics. The findings show how resource constraints shape adaptive strategies, how dynamic BRR thresholds balance safety and innovation, and how the integrated MAS–LLM approach can inform regulatory practice and strategic industry planning across high-regulation domains.

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.

Paper Structure

This paper contains 22 sections, 8 equations, 12 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Adaptive Dynamics and Emergent Behaviors in Regulatory Affairs
  3. Multi-Agent Modeling and Complexity Theory
  4. Literature Review
  5. Methodology
  6. Model Framework and Initialization
  7. 1st Mathematical Layer
  8. 2nd Agents Role Play
  9. Environmental Setup
  10. Evaluation and Validation
  11. Results
  12. Regulatory Response and Parameter Evolution of Manufacturer Agents
  13. Effect of Resource Constraints on Adaptive Strategies
  14. Decision made by Authority Agent
  15. Module evaluation and sensitivity result
  16. ...and 7 more sections

Figures (12)

  • Figure 1: Interaction Between Regulatory Authority and Manufacturers Agents in Approval Processes
  • Figure 2: Agent-Based Simulation of Pharmaceutical Regulatory Review Process
  • Figure 3: Diagram of interdependencies among variables, illustrating the regulatory feedback loop.
  • Figure 4: Prompts Example
  • Figure 5: Analysis of Parameter Change and Value Distribution
  • ...and 7 more figures