3 articles tagged with #agent-based-models. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce GASim, a graph-accelerated framework that combines large language models with agent-based models for large-scale social simulations. The system achieves 9.94x speedup and reduces computational token usage by 80% while maintaining accuracy in modeling real-world opinion dynamics.
Researchers present a controlled simulation benchmark for agent-based models (ABMs) that evaluates emissions regulation by comparing four policy-agent adaptation regimes. The study demonstrates that regulatory conclusions can differ significantly based on whether policies and agents adapt, even when average outcomes appear identical, establishing a methodological framework for more rigorous policy evaluation in complex systems.
Researchers propose a machine-coached policy-revision layer for adaptive agent-based models (ABMs) used in regulatory simulation, enabling real-time feedback and contestability of policy decisions through explainable symbolic rules rather than black-box optimization. The approach demonstrates practical application in emissions-regulation scenarios, balancing policy objectives while maintaining regulatory guardrails.
