Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation

Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation

Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation

Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation


Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. It represents a new way to understand data and generate informationthat has never been available before--a way for businesses to view the future and to understand and anticipate the likely effects of their decisions on their markets and industries. It thus promises to have far-reaching effects on the way that businesses in many areas use computers to supportpractical decision-making. Managing Business Complexity is the first complete business-oriented agent-based modeling and simulation resource. It has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features andadvantages of ABMS to other people and third, to teach readers how to actually implement ABMS by building agent-based simulations. It is intended to be a complete ABMS resource, accessible to readers who haven't had any previous experience in building agent-based simulations, or any other kinds ofmodels, for that matter. It is also a collection of ABMS business applications resources, all assembled in one place for the first time. In short, Managing Business Complexity addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, andhow specifically to build these powerful agent-based models.


Why this book? The answer is because people need to know about one of the most exciting and practical developments in business simulation and modeling that has occurred since the invention of relational databases. The world is changing in terms of the requirements for solving business problems and the capabilities of information technology and computer modeling that the technical and analytical community is able to bring to bear on these problems. This change in requirements means that the problems confronting business are changing and becoming more complex. The change in capabilities means that problems that have been there all along can now be solved.

This book is designed to do two things: (1) to teach you how to think about agents, and (2) to teach you how to do something with agents by developing agentbased models and simulations. In doing so, this book provides you with a vocabulary for agent-based modeling and simulation that draws from a number of fields that people typically do not connect.

We believe that in the future virtually all computer simulations will be in the form of agent-based simulations. Why is this so? For simulations it makes sense because of the natural way that agent models can represent business issues and the close similarity of agent modeling to the predominant computational paradigm of object-oriented programming. In fact, we believe that in the future many optimization models will be agent-based as well, due to the flexibility of the algorithms applied in agent-based optimization and their applicability to solving real-time optimization problems.

This book is intended for managers, analysts, and software developers in business and government. Those interested in an overview of agent-based modeling should read chapters 1, 3, 4, 5, 7, and 15. Those interested in a more detailed discussion should read all the chapters. Those interested in practicing agent modeling for themselves should read all the chapters and duplicate the spreadsheet models described in chapter 8.

The book is the outgrowth of our agent-based modeling project work for the business and government communities. It has benefited from the agentbased modeling conferences that we have organized and the agent-based modeling courses that we have conducted.

The authors would like to thank the many people who made this book possible. We thank our respective families. In particular, Michael North thanks his sister Cindy, his father John, and his mother Shirley. Charles Macal thanks his wife Kathy. We owe much to our colleagues whom we have interacted with along the way to becoming proficient modelers. In particular, Charles Macal thanks his first simulation teacher, A. Alan B. Pritsker, a genuine modeler’s modeler. We thank all of our friends at Argonne National Laboratory, with special gratitude to Tom Wolsko for his visionary insight, and to our fellow members of the . . .

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