A Guide to Re-Implementing Agent-based Models: Experiences from the HUMAT Model

TL;DR

This paper provides a comprehensive guide for re-implementing agent-based models, illustrated through the HUMAT socio-cognitive architecture, emphasizing best practices, challenges, and ensuring accurate, reliable simulations of complex systems.

Contribution

It introduces a detailed methodology for re-implementing agent-based models, including practical insights from the HUMAT model's re-implementation, enhancing model accuracy and reliability.

Findings

Identified key challenges in re-implementing agent-based models.

Developed a versatile, language-independent re-implementation approach.

Discussed practical pitfalls and solutions in the process.

Abstract

Replicating existing agent-based models poses significant challenges, particularly for those new to the field. This article presents an all-encompassing guide to re-implementing agent-based models, encompassing vital concepts such as comprehending the original model, utilizing agent-based modeling frameworks, simulation design, model validation, and more. By embracing the proposed guide, researchers and practitioners can gain a profound understanding of the entire re-implementation process, resulting in heightened accuracy and reliability of simulations for complex systems. Furthermore, this article showcases the re-implementation of the HUMAT socio-cognitive architecture, with a specific focus on designing a versatile, language-independent model. The encountered challenges and pitfalls in the re-implementation process are thoroughly discussed, empowering readers with practical insights. Embrace this guide to expedite model development while ensuring robust and precise simulations.