Automatic Calibration Framework of Agent-Based Models for Dynamic and Heterogeneous Parameters

TL;DR

This paper presents an automatic calibration framework for agent-based models that dynamically adjusts parameters over time and accounts for heterogeneity among agents to improve validation accuracy.

Contribution

It introduces a novel calibration framework combining dynamic and heterogeneous methods for better agent-based model validation.

Findings

Effective adjustment of simulation parameters to real-world data.

Reduction in distributional discrepancy among agents.

Enhanced validation accuracy of agent-based models.

Abstract

Agent-based models (ABMs) highlight the importance of simulation validation, such as qualitative face validation and quantitative empirical validation. In particular, we focused on quantitative validation by adjusting simulation input parameters of the ABM. This study introduces an automatic calibration framework that combines the suggested dynamic and heterogeneous calibration methods. Specifically, the dynamic calibration fits the simulation results to the real-world data by automatically capturing suitable simulation time to adjust the simulation parameters. Meanwhile, the heterogeneous calibration reduces the distributional discrepancy between individuals in the simulation and the real world by adjusting agent related parameters cluster-wisely.