Agentic LLM Framework for Adaptive Decision Discourse

• URL: http://arxiv.org/abs/2502.10978v1
• Date: Sun, 16 Feb 2025 03:46:37 GMT
• Title: Agentic LLM Framework for Adaptive Decision Discourse
• Authors: Antoine Dolant, Praveen Kumar,
• Abstract summary: This study introduces a real-world inspired agentic Large Language Models (LLMs) framework.<n>Unlike traditional decision-support tools, the framework emphasizes dialogue, trade-off exploration, and the emergent synergies generated by interactions among agents.<n>Results reveal how the breadth-first exploration of alternatives fosters robust and equitable recommendation pathways.
• Score: 2.4919169815423743
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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