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Agent-Based Modeling and Simulation (OR Essentials)

Taylor, Simon (ed.)
Palgrave Macmillan Ltd.: Houndmills, Basingstoke, Hampshire, 2014
ISBN 978-1137453624 (pb)

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Reviewed by Duncan A. Robertson
Loughborough University

Cover of book Agent-Based Modeling and Simulation is the first in the Operational Research Society’s OR Essentials series. OR Essentials brings together multidisciplinary research from the management, decision, and computer sciences. This edition within the series is edited by Simon Taylor, who is co-founder of the Journal of Simulation, also published by the OR Society.

The edited book is divided into 14 chapters, and the bulk of its contents covers the application of agent based modelling (ABM) to specific problem domains. The book adds to this by introducing agent-based modelling as a technique, and setting it in context with other simulation approaches. A very helpful chapter by Macal and North of Argonne National Laboratory offers a tutorial on what agent-based modelling is, focusing on the autonomy and interconnectedness of agents, and showing how agent-based models should be built. The book ends with thoughtful chapters on a testing framework for ABM (Gürcan, Dikenelli, Bernon), and a comparison with discrete-event simulation by Brailsford, elegantly closing the package opened by Taylor’s introduction comparing ABM with system dynamics and discrete event simulation within the context of modelling and simulation more generally.

The academic rigour of the book is confirmed by each article being reprinted from published articles from the Journal of Simulation. The book brings together several excellent examples of agent-based modelling, together with a very clear understanding of how ABM fits in with more traditional simulation techniques such as DES (Discrete Event Simulation) and SD (System Dynamics) – both Talyor and Brailsford show how and when ABM should be used. Macal and North offer a very useful tutorial for understanding the building blocks of an ABM simulation, while Heath and Hill show ABM’s evolution from cellular automata and complexity science through complex adaptive systems.

Domain specific chapters cover applications in the management of hospital-acquired infection (Meng, Davies, Hardy, and Hawkey); product diffusion of a novel biomass fuel (Günther, Stummer, Wakolbinger, Wildpaner); urban evacuation (Chen, Zhan); people management (Siebers, Aickelin, Celia, Clegg); pharmaceutical supply chains (Jetly, Rossetti, Handfield); workflow scheduling (Merdan, Moser, Sunindyo, Biffl, Vrba); credit risk (Jonsson); and historical infantry tactics (Rubio-Campillo, Cela, Cardona).

Agent-Based Modeling and Simulation offers a very useful collection of applications of ABM, and showcases how ABM can be successfully incorporated in to mainstream, published research. The contributions to the book are diverse, and from internationally regarded scholars. It is also useful to see the diverse ways that agent-based modelling research is presented, from some papers that show code, some that show running models, and some that do not show the model or code but instead describe results.

The glue that binds the book is methodological. Seeing how ABM has been used in diverse application areas is important, given the trans-disciplinary nature of the approach. It is an excellent introduction into agent-based modelling within a wide range of business and operations applications, and should be read by scholars and practitioners alike.


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