About This Architecture

NetLogo simulation engine generates synthetic data that feeds into a regression model for predictive analysis, which then informs an LLM-based policy generator. The pipeline ingests simulation outputs and external policy constraints, processes them through statistical regression to identify patterns, and serves refined policy proposals. This architecture demonstrates how agent-based modeling can augment machine learning workflows to generate evidence-backed policy recommendations. Fork this diagram on Diagrams.so to customize the regression model, swap the LLM component, or integrate live policy feedback loops.

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How can I combine NetLogo simulations with machine learning and LLMs to generate evidence-backed policy recommendations?

This pipeline uses NetLogo to generate synthetic data from agent-based simulations, feeds that data into a regression model for pattern discovery, and passes both the regression insights and external policy constraints to an LLM policy generator. The result is a system that grounds policy proposals in simulation evidence and predictive analytics.