Towards Standardizing Validation Practices in Agent-Based Modeling: A Hierarchical ABM Validation Framework

Agent-Based Modeling (ABM) has gained significant traction due to its ability to represent diverse agents’ behaviors and interactions accurately. However, ensuring the reliability and widespread acceptance of ABM necessitates rigorous validation. Unfortunately, existing literature often lacks integration of representative validation methods into a cohesive framework, hindering standardized validation practices. This study aims at proposing a comprehensive and pragmatic validation framework. Initially, we clarify three fundamental concepts: calibration, verification, and validation. Subsequently, we review 17 distinct validation approaches and categorize them based on their data requirements and suitability for various simulation methodologies. Aligned with the ABM procedures, we introduce a Hierarchical ABM Validation (HAV) framework structured across three tiers: agent level, model level, and output level. Each tier recommends appropriate validation methods contingent upon data availability, enhancing the HAV’s applicability across diverse modeling scenarios. Finally, we develop an accessible Python code package (hav) and provide two examples of validating a traffic model and a wealth model. These cases exemplify the HAV’s implementation and underscore its efficacy in promoting standardized validation practices within ABM research.