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Agentic LLM Framework for Adaptive Decision Discourse

Antoine Dolant, Praveen Kumar

TL;DR

The paper addresses decision-making under uncertainty in complex socio-ecological systems by introducing a self-governed, agentic LLM framework that simulates human-like decision discourse with multiple stakeholder personas. It presents a bottom-up architectural design featuring a macro-scale conference room coordinating micro-scale agents, each guided by specialized prompts, with mechanisms for self-governance, convergence, and dynamic assembly. Through a hypothetical extreme flood scenario, the approach demonstrates breadth-first exploration of mitigation and adaptation options across varying risk levels, revealing emergent synergies and the role of equity considerations. The work also discusses evaluation, guardrails, transdisciplinary integration, and information-theoretic perspectives, arguing that such agentic discourse can provide scalable, context-aware, and equitable decision support with potential applicability across domains facing uncertainty and complexity.

Abstract

Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces a real-world inspired agentic Large Language Models (LLMs) framework, to simulate and enhance decision discourse-the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents embodying distinct personas. These personas simulate diverse stakeholder roles, each bringing unique priorities, expertise, and value-driven reasoning to the table. The framework incorporates adaptive and self-governing mechanisms, enabling agents to dynamically summon additional expertise and refine their assembly to address evolving challenges. An illustrative hypothetical example focused on extreme flooding in a Midwestern township demonstrates the framework's ability to navigate uncertainty, balance competing priorities, and propose mitigation and adaptation strategies by considering social, economic, and environmental dimensions. Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways. This framework transforms how decisions are approached in high-stakes scenarios and can be incorporated in digital environments. It not only augments decision-makers' capacity to tackle complexity but also sets a foundation for scalable and context-aware AI-driven recommendations. This research explores novel and alternate routes leveraging agentic LLMs for adaptive, collaborative, and equitable recommendation processes, with implications across domains where uncertainty and complexity converge.

Agentic LLM Framework for Adaptive Decision Discourse

TL;DR

The paper addresses decision-making under uncertainty in complex socio-ecological systems by introducing a self-governed, agentic LLM framework that simulates human-like decision discourse with multiple stakeholder personas. It presents a bottom-up architectural design featuring a macro-scale conference room coordinating micro-scale agents, each guided by specialized prompts, with mechanisms for self-governance, convergence, and dynamic assembly. Through a hypothetical extreme flood scenario, the approach demonstrates breadth-first exploration of mitigation and adaptation options across varying risk levels, revealing emergent synergies and the role of equity considerations. The work also discusses evaluation, guardrails, transdisciplinary integration, and information-theoretic perspectives, arguing that such agentic discourse can provide scalable, context-aware, and equitable decision support with potential applicability across domains facing uncertainty and complexity.

Abstract

Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces a real-world inspired agentic Large Language Models (LLMs) framework, to simulate and enhance decision discourse-the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents embodying distinct personas. These personas simulate diverse stakeholder roles, each bringing unique priorities, expertise, and value-driven reasoning to the table. The framework incorporates adaptive and self-governing mechanisms, enabling agents to dynamically summon additional expertise and refine their assembly to address evolving challenges. An illustrative hypothetical example focused on extreme flooding in a Midwestern township demonstrates the framework's ability to navigate uncertainty, balance competing priorities, and propose mitigation and adaptation strategies by considering social, economic, and environmental dimensions. Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways. This framework transforms how decisions are approached in high-stakes scenarios and can be incorporated in digital environments. It not only augments decision-makers' capacity to tackle complexity but also sets a foundation for scalable and context-aware AI-driven recommendations. This research explores novel and alternate routes leveraging agentic LLMs for adaptive, collaborative, and equitable recommendation processes, with implications across domains where uncertainty and complexity converge.

Paper Structure

This paper contains 32 sections, 3 figures.

Table of Contents

  1. A Case for LLMs in Human Discourse
  2. Multi-Agent Framework for Decision Discourse
  3. Agentic Foundation for Decision Discourse
  4. Structural Design
  5. Agent Design
  6. Orchestration Mechanisms
  7. Self-Governance and Convergence
  8. Decision Generation and Evaluation
  9. Application
  10. Scenario
  11. Initial Pool of Agent Persona
  12. Local Government Representation
  13. Environmental Science Representation
  14. Community Advocacy Representation
  15. Results
  16. ...and 17 more sections

Figures (3)

  • Figure 1: Illustration of the multi-agent decision discourse workflow. First (A), a natural disaster threat is selected to generate a scenario that will act as a bootstrap to LLM agents. Second (B), initial persona prompts are specified or generated for the starting assembly (for example, composed of a mayor, community advocate, environmental scientist, and moderator for an extreme flooding scenario). With those two necessary conditions, the assembly (C) starts and iterates for a specified number of runs or until convergence. Each iteration consists of an agent continuing the discussion at time state t from the conversation at time state t-1 as an input. Agents can bring in new participants (D) if they judge that there is an imbalance in the skills and knowledge domains represented within the assembly. Each execution results in the generation of a conversation output (E), from which can be extracted recommended courses of action with their advantages and drawbacks. Actionable measures are colored here as green or red (F) depending on their positive or negative impact on resilience to the situation. The space of possible outcomes represents all realizations of the different action pathways, and can be enriched with additional executions with slightly varying inputs, evaluating scenarios that are causally defined through counterfactuals.
  • Figure 2: Summary message of the conversation, including selected recommendations, their advantages and shortcomings. For details of the discourse and the summary, please see supplementary information \ref{['titles:appendix']}.
  • Figure :