Managing business complexity: discovering strategic solutions with agent-based modelling and simulation

This book, Managing Business Complexity by Michael North and Charles Macal, is not your typical agent-based modelling text. To date, it seems, most agent-based modelling texts fall into two broad categories: a collection of readings (such as from a conference), or a specific how-to text (for a specific methodology or software platform). Although serving a valued purpose, such works fail to provide that text targeted at that new technically savvy audience of readers interested in the agent-based modelling paradigm that require a relatively complete exposition based on a broadly defined view of agents and agent-based models. While the landmark text by Epstein and Axtell (1996) remains a necessary initial reference for anyone commencing their studies into agents and agent-based modelling, this text by North and Macal should be reserved as a key reference text. As noted in an earlier review of this book (Van Dyke Parunak, 2007), the 15 chapters in the book fall into three categories; a categorization that works for this review as well. Chapters 1–5 really provide foundational information about what are agents and what is agent-based modelling. Chapters 6–10 provide a series of ‘how to’ chapters pertaining to different components of the agent-based modelling paradigm. Finally, Chapters 11–14 cover topics related to modelling and simulation in general but tailored to agent-based modelling specifically. Chapter 15 is a concluding chapter. The authors take a much broader definition of agentbased than might be found in any other book on agents and agent-based modelling. As such they cover a greater breadth of topics as compared to these other texts. Their target audience is not the accomplished agent-based modeller, even though there is a wealth of worthwhile information in the book for that modeller. As stated, the target audience is ‘managers, analysts, and software developers in business and government’. To this, one should add, ‘or anyone interested in learning about agents or to those just getting started in research using agent models’. The caveat to this is that the book has sufficient technical depth to require some level of technical competence to comprehend the coverage and exploit the knowledge the book imparts. The first five chapters introduce the reader to agents and how agent-based models apply. This coverage includes defining the agent-based model paradigm (Chapter 2), defining agents (Chapter 3), providing some history of agent modelling (Chapter 4) and where agent-based modelling fits (Chapter 5). While overall they present quite a complete background, the Chapter 5 coverage gets overly ambitious in that it tries to lay out the complete spectrum of modelling approaches. Although not really in line with the focus of the book, this full-spectrum coverage does help the newcomer to agent-based modelling better distinguish among modelling approaches. Further, this broad review of modelling approaches provides useful background information for the remainder of the book. Chapters 6–10 provide the ‘how-to’ portion of the book. Chapter 6 is one of the best chapters in the text and a wonderful addition to the agent-based modelling literature. While capturing agent behaviours, and their interactions, is a crucial part of the agent-based modelling methodology, rarely do texts provide a how-to associated with this knowledge engineering component. North and Macal correct Journal of Simulation (2010) 4, 211–212 r 2010 Operational Research Society Ltd. All rights reserved. 1747-7778/10