Assessing Parameter Identifiability in Agent-Based Models with Bayesian Amortized Inference

Estimating the parameters of agent-based models from empirical data is of fundamental importance, as uncertainty in these estimates can expose non-identifiability issues that undermine inferences drawn from the underlying generative mechanism. This has implications for out-of-sample prediction, policy recommendations, and decision-making. In this study, we present an amortized Bayesian parameter estimation pipeline based on the generative artificial intelligence framework of normalizing flows, which enables a fast and systematic assessment of parameter identifiability. This approach can be scaled efficiently across multiple datasets and can be used for both rapid theory testing and computationally intensive Bayesian diagnostics, including parameter recoverability tests and simulation-based calibration. Using the Python package BayesFlow, we demonstrate the pipeline using two illustrative models: one from opinion dynamics and one from infectious disease modelling. Furthermore, we provide a general introduction to BayesFlow for agent-based models, integrating identifiability assessment into a comprehensive Bayesian workflow and emphasize its significance at every stage of the inference process.