The Strategy Hypercube: Exploring Strategy Space Using Agent-Based Models, Lecture Notes in Computer Science 2927:182-192
Book review to be published in The Journal of Artificial Societies and Social Simulation (JASSS)
Taylor, Simon J. E. (Ed.) (2014) Agent-Based Modeling and Simulation, OR Society and Palgrave Macmillan: Basingstoke
Agent-Based Modeling and Simulation is the first in the Operational Research Society’s OR Essentials series. OR Essentials brings together multidisicpinary 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 convers 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.
Agent-Based Models of a Banking Network as an Example of a Turbulent Environment: The Deliberate vs Emergent Strategy Debate Revisited is a paper that I wrote that uses an agent-based model to simulate the strategic positioning decisions of firms within a competitive environment. It links firm strategy (particularly banking strategy) to the competitive environment: where should firms locate to gain most customers given that all firms are trying to achieve the same objective, that of maximizing market share / revenue / profits? It is an interesting question, and one that is very suited to modelling the dynamics of an industry using agent-based models.
“We discuss the notion of complexity as applied to firms and corporations. We introduce the background to complex adaptive systems, and discuss whether this presents an appropriate model or metaphor to be used within management science. We consider whether a corporation should be thought of as a complex system, and conclude that a firm within an industry can be defined as a complex system within a complex system.Whether we can say that the use of complexity research will fundamentally improve firm performance will depend on the effect on success derived from its application.”
This paper with François Collet of ESADE and Daniela Lup of the LSE analyzes the emergence of the strategic management filed showing the benefits of network brokerage are stronger during the early phase of development and diminish over time.
Through exposure to heterogeneous sources of knowledge, actors who broker between unconnected contacts are more likely to generate valuable output. We contribute to the theory of social capital of brokerage by considering the impact of field maturity. Using longitudinal data from the field of strategic management we find that the benefits of network brokerage are stronger during the early stages of field development and diminish as the field matures. The results of our study call for further research on the interplay between network structures and processes of field emergence.
This paper, published with colleagues from Warwick University and Cambridge University: Petra Vertes, Ruth Nicol, Sandra Chapman, Nicholas Watkins, and Edward Bullmore was the result of inter-disciplinary work funded by the EPSRC – the Engineering and Physical Sciences Research Council (Grant number EP/H02395X/1). We investigated the similarities in the network structure financial markets and brain networks.
Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties.
Agent-Based Modeling Toolkits: NetLogo, RePast, and Swarm (2005, Academy of Mangement Learning and Education 4(4), 525–527) sets out a comparison of three widely used agent-based modeling toolkits: RePast, NetLogo, and Swarm. It shows the differences between the toolkits, setting out the advantages, disadvantages, and limitations of each software toolkit.
Agent-Based Models to Manage the Complex is a book chapter in Managing Organizational Complexity: Philosophy, Theory and Application: Volume 1 (ISCE Book Series – Managing the Complex) is an introduction to the use of agent-based models in management. It demonstrates the use of models in Repast, an agent-based modeling toolkit, and links this to complexity science concepts of emergent systems.
Behavioral Operational Research: Theory, Methodology and Practice (Martin Kunc, Jonathan Malpass, Leroy Wright, Eds.) was published by Palgrave Macmillan in September 2016. My chapter on Agent-Based Modeling and Behavioral Operational Research shows the great potential of using agent-based simulation within BOR, showing how example models can be applied to the field. More details of the chapter can be found on the Palgrave Macmillan site here (DOI:10.1057/978-1-137-53551-1_7).
Please click here for the chapter in pdf format.
Updated September 2016 with full text.
The Dynamics of Strategy is a book published by Oxford University Press (Robertson and Caldart 2010) and combines natural science models with strategic management. Chapters on networks, agent-based modelling, and dynamic economic models are used to show how strategy can be treated analytically.