Calibrating a Global Trade Agent-Based Model with an HPC–ABC–SMC Framework

This study presents a High-Performance Computing and Approximate Bayesian Computation-Sequential Monte Carlo (HPC-ABC-SMC) framework for calibrating complex agent-based models. Applied to a global food trade model, the framework calibrates trade partner and trade volume objectives separately, using precomputed result mapping and task-level parallelisation to achieve up to a 42.1-fold computational efficiency improvement over traditional methods. Unlike conventional optimisation approaches, our method provides posterior distributions rather than single-point estimates, enabling richer interpretation of parameter uncertainty. The results reveal systematic differences between partner-based and volume-based calibrations: partner calibration yields smaller parameter magnitudes due to higher sensitivity, while volume calibration requires larger weights under looser constraints. Comparative analysis shows consistency with genetic algorithms but demonstrates superior interpretability, while sensitivity analysis highlights the dominant role of GDP per capita weight in shaping international trade patterns. Overall, the framework offers a scalable and uncertainty-aware solution for calibrating empirical agent-based models with high-dimensional parameter spaces and alternative evaluation criteria.