Harnessing the power of large language models for agent-based model development

This blog post discusses how large language models (LLMs) like Claude 3 Sonnet can be leveraged to accelerate the development of agent-based models (ABMs) for simulating complex systems, even in domains where the developer has limited expertise.

Specifically, the article covers:

• Using an LLM to gather relevant knowledge from academic literature and iteratively generate code for an ABM simulating wildfire propagation
• Incorporating geospatial data and computer vision techniques to create a realistic spatial environment for the ABM
• Running simulations of the developed ABM and visualizing the results
• An AWS architecture leveraging Amazon Bedrock for iterative LLM-assisted development and AWS Batch for parallel execution of large numbers of ABM simulations
• The potential for LLMs to augment human expertise and enable rapid development of accurate ABMs across various domains