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

326 Pages·2007·6.51 MB·English

by  Michael J. North

MANAGING BUSINESS COMPLEXITY
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MANAGING BUSINESS COMPLEXITY
Discovering Strategic Solutions with
Agent-Based Modeling and Simulation
Michael J. North andCharles M. Macal
1
2007
1
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Library of Congress Cataloging-in-Publication Data
North, Michael J. (Michael John), 1969–
Managing business complexity : discovering strategic solutions with
agent-based modeling and simulation / Michael J. North and
Charles M. Macal.
p. cm.
Includes bibliographical references and index.
ISBN-13 978-0-19-517211-9
ISBN 0-19-517211-6
1. Management information systems. 2. Distributed artificial
intelligence. I. Macal, Charles M. II. Title.
T58.6.N67 2006
658.403—dc22 2005015235
9 8 7 6 5 4 3 2 1
Printed in the United States of America
on acid-free paper
To my sister Cindy, my father John, and my mother Shirley
Michael North
To my parents Dorothy and Charlie
Charles Macal
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Preface
Why this book? The answer is because people need to algorithms applied in agent-based optimization and
know about one of the most exciting and practical their applicability to solving real-time optimization
developments in business simulation and modeling problems.
that has occurred since the invention of relational This book is intended for managers, analysts, and
databases. The world is changing in terms of the software developers in business and government.
requirements for solving business problems and the Those interested in an overview of agent-based mod-
capabilitiesof information technology and computer eling should read chapters 1, 3, 4, 5, 7, and 15. Those
modeling that the technical and analytical commu- interested in a more detailed discussion should read
nity is able to bring to bear on these problems. This all the chapters. Those interested in practicing agent
change in requirements means that the problems modeling for themselves should read all the chapters
confronting business are changing and becoming and duplicate the spreadsheet models described in
more complex. The change in capabilities means that chapter 8.
problems that have been there all along can now be The book is the outgrowth of our agent-based
solved. modeling project work for the business and govern-
This book is designed to do two things: (1) to teach ment communities. It has benefited from the agent-
you how to think about agents, and (2) to teach you based modeling conferences that we have organized
how to do something with agents by developing agent- and the agent-based modeling courses that we have
based models and simulations. In doing so, this book conducted.
provides you with a vocabulary for agent-based The authors would like to thank the many people
modeling and simulation that draws from a number of who made this book possible. We thank our respective
fields that people typically do not connect. families. In particular, Michael North thanks his sister
We believe that in the future virtually all computer Cindy, his father John, and his mother Shirley.
simulations will be in the form of agent-based simula- Charles Macal thanks his wife Kathy. We owe much
tions. Why is this so? For simulations it makes sense to our colleagues whom we have interacted with
because of the natural way that agent models can rep- along the way to becoming proficient modelers. In
resent business issues and the close similarity of agent particular, Charles Macal thanks his first simulation
modeling to the predominant computational para- teacher, A. Alan B. Pritsker, a genuine modeler’s mod-
digm of object-oriented programming. In fact, we eler. We thank all of our friends at Argonne National
believe that in the future many optimization models Laboratory, with special gratitude to Tom Wolsko for
will be agent-based as well, due to the flexibility of the his visionary insight, and to our fellow members of the
viii PREFACE
Argonne EMCAS development team: Gale Boyd, Dick of Vienna), Lars-Erik Cederman (ETH Zürich),
Cirillo, Guenter Conzelmann, Vladimir Koritarov, Ali Cinar (Illinois Institute of Technology),
Prakash Thimmapuram, and Tom Veselka. We thank Claudio Cioffi-Revilla (George Mason University),
our collaborators on the Repast development team, Nosh Contractor (University of Illinois, Champaign
including Mark Altaweel, Nick Collier, Tom Howe, Urbana), Harvey Drucker (Argonne National
Miles Parker, David Sallach, Pam Sydelko, Eric Tatara, Laboratory), Thierry Emonet (University of Chicago),
and Richie Vos. We thank the staff of the Santa Fe Josh Epstein (Brookings Institution), Nigel Gilbert
Institute, especially Susan Ballati, Shannon Larsen, and (University of Surrey), Bryan Gross (MPSI Systems
Chris Wood for their support in organizing business Inc.), Laszlo Gulyas (AITIA Inc.), Peter Hedström
complexity modeling courses, and the SFI Business (Nuffield College), Cynthia Hood (Illinois Institute
Network supply chain modeling team, including of Technology), Mark Kimura (MPSI Systems Inc.),
George Danner, Ed MacKerrow, and Owen Michael Macy (Cornell University), John Padgett
Densmore. We thank Averill Law for recent discus- (University of Chicago), Scott Page (University of
sions on the relationship between agent modeling and Michigan), Randy Picker (University of Chicago), Bill
conventional simulation. We also thank the team at Rand (Northwestern University), Bob Rosner (Argonne
Oxford University Press, including editors Martha National Laboratory), Keith Sawyer (Washington
Cooley, Frank Fusco, and John Rauschenberg, man- University in St. Louis), John Sterman (Massachusetts
aging editor Lisa Stallings, and copy-editor Alan Hunt Institute of Technology), Fouad Teymour (Illinois
of Keyword Group Ltd. Institute of Technology), Seth Tisue (Northwestern
We thank the following individuals for many enjoy- University), Uri Wilensky (Northwestern University),
able and enlightening discussions about agent-based and Peyton Young (Johns Hopkins University).
modeling over the years: Rob Axtell (George Mason
University), Steve Bankes (Evolving Logic, Inc.), Roger Michael J. North
Burkhart (Deere & Co.), Kathleen Carley (Carnegie Charles M. Macal
Mellon University), John Casti (Technical University
Contents
Chapter 1: The Challenge, 3 The Cycle of Innovation, 14
Why This Book?, 3 Other Angles on Nondeterminism, 16
The Who, What, Where, When, Why, and Choosing Behaviors to Model, 17
How of Agents, 3
The Spectrum of Model Uses, 18
Why Agent-Based Modeling and Simulation
Discovering That the Whole Is Greater
Is Needed Now, 4
Than the Parts, 22
The Foundation of ABMS, 5
Summary, 22
Why ABMS Is Useful, Usable, and
Note, 23
Used, 6
References, 23
How ABMS Works: An Overview, 8
Chapter 3: Agents Up Close, 24
Incremental Discovery, Design, and
Agents: An Overview, 24
Development, 8
Agent Attributes, 24
How This Book Is Organized, 8
Agent Behaviors, 27
Case Studies, 9
Simple Agents or Proto-Agents, 27
Note, 9
Complex Agents, 31
References, 9
A Market Example, 41
Chapter 2: The ABMS Paradigm, 11
Discover, Design, and Develop, 43
Parts Make the Whole, 11
Notes, 43
The Two Fundamental Types
References, 43
of Models, 11
Nondeterminism, 13