Methods That Support the Validation of Agent-Based Models: An Overview and Discussion

Andrew Collins (), Matthew Koehler () and Christopher Lynch ()
Additional contact information
Andrew Collins: http://www.drandrewjcollins.com
Christopher Lynch: http://www.vmasc.odu.edu/lynch.html

Journal of Artificial Societies and Social Simulation, 2024, vol. 27, issue 1, 11

Abstract: Validation is the process of determining if a model adequately represents the system under study for the model’s intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models’ mechanisms and in the real-world situations they attempt to represent. To aid both the novice and expert in conducting a validation process for an agent-based simulation, this article reviews nine methods that are useful for this process, including foundational topics of docking, empirical validation, sampling, and visualization, as well as advanced topics of bootstrapping, causal analysis, inverse generative social science, and role-playing. Each method is reviewed with respect to its benefits and limitations as a validation-supporting method for ABMS. Suggestions that may support a validation plan for an agent-based simulations, are also provided. This article is an introductory guide for understanding and conducting ABMS validation for developers of all experience levels.

Keywords: Agent-Based Modeling; Docking; Empirical Validation; Model Validation; Simulation Validation; Validation (search for similar items in EconPapers)
Date: 2024-01-31
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Citations: View citations in EconPapers (1)

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