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MANAGING BUSINESS COMPLEXITY This Page Intentionally Left Blank MANAGING BUSINESS COMPLEXITY Discovering Strategic Solutions with Agent-Based Modeling and Simulation MANAGING BUSINESS COMPLEXITY This page intentionally left blank MANAGING BUSINESS COMPLEXITY Discovering Strategic Solutions with Agent-Based Modeling and Simulation Michael J. North and Charles M. Macal 1 2007 1 Oxford University Press, Inc., publishes works that further Oxford University’s objective of excellence in research, scholarship, and education. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Copyright © 2007 by Oxford University Press, Inc. Published by Oxford University Press, Inc. 198 Madison Avenue, New York, New York 10016 www.oup.com Oxford is a registered trademark of Oxford University Press All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission of Oxford University Press. 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 658.4 03—dc22 2005015235 987654321 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 This page intentionally left blank 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- capabilities of 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 Development, 8 Agents: An Overview, 24 How This Book Is Organized, 8 Agent Attributes, 24 Case Studies, 9 Agent Behaviors, 27 Note, 9 Simple Agents or Proto-Agents, 27 References, 9 Complex Agents, 31 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 of Models, 11 References, 43 Nondeterminism, 13 x CONTENTS Chapter 4: The Roots of ABMS, 45 Chapter 7: Office ABMS, 116 ABMS in Context, 45 In the Office, 116 Historical Roots, 45 Progressive Development, 116 Complexity Science, 46 Prototyping, 116 The Diffusion of ABMS, 55 ABMS Environments, 117 Plectics Redux, 56 The Four Model Growth Paths, 117 References, 56 Leveraging Change, 118 Chapter 5: The Role of ABMS, 59 Returning to the Questions, 121 The Big Picture of Modeling and Office ABMS Architectures, 122 Simulation for Business Applications, 60 The Office ABMS Continuum, 123 A Supply Chain Example, 63 Examples, 126 A Survey of Modeling Approaches, 66 Back at the Office, 128 When to Use Agents?, 93 Notes, 128 Blended Modeling Approaches, 93 References, 128 Summary, 94 Chapter 8: How to Do Desktop ABMS, 130 Note, 95 ABMS on the Desktop, 130 References, 95 Agent Spreadsheets, 130 Chapter 6: Discovering Agents and Agent Dedicated ABMS Prototyping Behaviors, 97 Environments, 161 Agents Are What They Do, 97 A Market Example, 171 Social Agents, 97 Summary, 182 Examples of Behavioral Theories, 97 Notes, 182 Agent Diversity, 101 References, 182 Other Agents, 101 Chapter 9: How to Do Participatory Multi-Agent Systems, 101 ABMS, 183 Artificial Intelligence, 102 Live and In Person!, 183 Discovering Agents, 102 The Start of It All, 183 Discovering Agent Behaviors, 102 Strengths and Weaknesses, 184 A Market Example, 112 Developing Strong Minds, 186 Acting Up, 114 Turning Over Rocks, 187 References, 114 Details, Details, Details, 187 CONTENTS xi A Market Example,
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