ClimateAgents: A Multi-Agent Research Assistant for Social-Climate Dynamics Analysis
Shan Shan

TL;DR
ClimateAgents introduces a multi-agent AI framework that enhances social-climate analysis by integrating diverse data sources, supporting hypothesis generation, and enabling adaptive, interpretable exploration of socio-environmental dynamics.
Contribution
This work presents a novel multi-agent system that collaboratively supports social-climate research through integrated data analysis, reasoning, and reporting, surpassing traditional narrow-focused climate analysis methods.
Findings
Enables interdisciplinary exploration of climate-social relationships.
Supports hypothesis generation and scenario analysis.
Augments analytical reasoning with AI agents.
Abstract
The complex interaction between social behaviors and climate change requires more than traditional data-driven prediction; it demands interpretable and adaptive analytical frameworks capable of integrating heterogeneous sources of knowledge. This study introduces ClimateAgents, a multi-agent research assistant designed to support social-climate analysis through coordinated AI agents. Rather than focusing solely on predictive modeling, the framework assists researchers in exploring socio-environmental dynamics by integrating multimodal data retrieval, statistical modeling, textual analysis, and automated reasoning. Traditional approaches to climate analysis often address narrowly defined indicators and lack the flexibility to incorporate cross-domain socio-economic knowledge or adapt to evolving research questions. To address these limitations, ClimateAgents employs a set of…
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Taxonomy
TopicsComputational and Text Analysis Methods · Climate change impacts on agriculture · Climate Change Communication and Perception
