How to calibrate a large-scale agent-based model? (mcrcsm.substack.com)

Macrocosm’s latest technical deep dive showcases how to calibrate large-scale agent-based economic models using Generalized Variational Inference (GVI) implemented in JAX. Calibration is critical for aligning simulation outputs with real-world data, but classical Bayesian inference often struggles due to intractable posteriors in complex models. By leveraging GVI—a flexible framework that relaxes assumptions about likelihood and prior accuracy—this approach adapts the variational objective to improve inference robustness, even when traditional assumptions fail. The post includes a hands-on JAX implementation, demonstrating how to harness JAX’s automatic differentiation and XLA compilation to efficiently optimize custom inference objectives. The authors illustrate this methodology through the Lotka-Volterra predator-prey model, a nonlinear dynamical system describing interacting populations. They encode the system’s differential equations using JAX’s `lax.scan` for accelerated integration, impose log-normal priors for positive parameters, and incorporate Gaussian noise to mimic observational uncertainty. Using NumPyro, a probabilistic programming layer atop JAX, they build a Bayesian model where variational inference optimizes an approximate posterior over model parameters. This seamless integration enables fast, differentiable, and scalable inference workflows that are crucial for tuning high-dimensional, large-scale agent-based simulations. This work is significant for the AI/ML community as it highlights an advanced inference toolkit bridging simulation-based modeling and modern machine learning. By combining GVI’s theoretical flexibility with JAX’s computational efficiency, it paves the way for principled calibration of complex stochastic models, a key challenge in economics, ecology, and beyond. The open-source Jupyter notebook further facilitates adoption and experimentation, serving as a valuable resource for researchers and practitioners working on data-driven simulation and probabilistic modeling.