Combining SD & ABM : frameworks, benefits, challenges, and future research directions

Howick, Susan and Megiddo, Itamar and Nguyen, Le Khanh Ngan and Wurth, Bernd and Kazakov, Rossen; Fakhimi, Masoud and Mustafee, Navonil, eds. (2024) Combining SD & ABM : frameworks, benefits, challenges, and future research directions. In: Hybrid Modelling and Simulation. Springer, Cham. ISBN 9783031599989 (In Press)

[thumbnail of Howick-etal-Springer-2023-Combining-SD-&-ABM-frameworks-benefits-challenges-and-future-research-directions] Text. Filename: Howick-etal-Springer-2023-Combining-SD-_-ABM-frameworks-benefits-challenges-and-future-research-directions.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 1 January 2099.
License: Strathprints license 1.0
Download (1MB) | Request a copy

Abstract

System Dynamics (SD) and Agent-Based modelling (ABM) are two commonly used simulation methods with different characteristics and benefits. When tackling a complex problem, the use of one of these methods may be insufficient and, instead, a combination of the two methods in a hybrid simulation may be required. To support modellers in the development of SD-ABM hybrid simulations, this chapter provides a comprehensive overview of methodological and practical considerations. Frameworks are presented to facilitate the implementation of hybrid SDABM models including the development of a conceptual SD-ABM hybrid model. The chapter then presents key benefits associated with SD-ABM hybrid modelling, which include being able to model an appropriate level of complexity, facilitate communication of the model design, enhance confidence building and reduce compute intensity. Two case studies are used to illustrate these benefits. Although there are many benefits, there are also key challenges associated with the development of a SD-ABM hybrid model and these are discussed. The chapter concludes with a discussion of opportunities and areas for future research.

CORE (COnnecting REpositories)