LLM-Assisted Modeling and Simulations for Public Sector Decision-Making: Bridging Climate Data and Policy Insights
Track Selection: Track 1: Developing LLM-powered tools for positive outcomes
Keywords: LLMs, Decision Making, Public Policy, Digital Twin
TL;DR: Revolutionizing public sector decision-making with an LLM-assisted framework, enabling non-technical users to effectively engage with complex climate datasets and simulations for informed environmental policy-making.
Abstract: This paper presents a transformative framework aimed at enhancing decision-making in the public sector, especially in the context of environmental policy and climate change. Traditional approaches to handling vast and complex climate data often create barriers due to the need for specialized expertise. Our framework, based on the GPTs platform from OpenAI, overcomes this challenge by enabling users without technical knowledge to easily access and interpret climate datasets and simulations. Through natural language interaction, stakeholders can engage with data and explore various policy scenarios effectively. This approach not only simplifies the decision-making process but also opens doors for a wider range of stakeholders to contribute to policy development and strategic planning in the public sector. The paper discusses the framework's design, its application in real-world scenarios, and its potential to facilitate informed, data-driven decisions in addressing environmental challenges.
Submission Number: 10
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