Tutorial on Agent-Based Modeling and Simulation PART 2: How to Model with Agents

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of interacting autonomous agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to do research. Some have gone so far as to contend that ABMS is a new way of doing science. Computational advances make possible a growing number of agent-based applications across many fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling the growth and decline of ancient civilizations to modeling the complexities of the human immune system, and many more. This tutorial describes the foundations of ABMS, identifies ABMS toolkits and development methods illustrated through a supply chain example, and provides thoughts on the appropriate contexts for ABMS versus conventional modeling techniques

[1]  R. Axelrod,et al.  The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration , 1998 .

[2]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  John R. Koza,et al.  Hidden Order: How Adaptation Builds Complexity. , 1995, Artificial Life.

[4]  R. Axelrod Reviews book & software , 2022 .

[5]  John H. Holland,et al.  Hidden Order: How Adaptation Builds Complexity , 1995 .

[6]  W. Arthur,et al.  The Economy as an Evolving Complex System II , 1988 .

[7]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[8]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[9]  Kathleen M. Carley,et al.  Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers , 2004 .

[10]  J. Casti Would-Be Worlds: How Simulation Is Changing the Frontiers of Science , 1996 .

[11]  Charles M. Macal,et al.  Escaping the Accidents of History: An Overview of Artificial Life Modeling with Repast , 2005 .

[12]  Chung-Yuan Huang,et al.  Simulating SARS: Small-World Epidemiological Modeling and Public Health Policy Assessments , 2004, J. Artif. Soc. Soc. Simul..

[13]  Decision,et al.  Evaluating the potential impact of transmission constraints on the operation of a competitive electricity market in Illinois. , 2006 .

[14]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[15]  Nicolas Lhuillier,et al.  FOUNDATION FOR INTELLIGENT PHYSICAL AGENTS , 2003 .

[16]  A. Colman,et al.  The complexity of cooperation: Agent-based models of competition and collaboration , 1998, Complex..

[17]  Leigh Tesfatsion,et al.  Agent-Based Computational Economics: Growing Economies From the Bottom Up , 2002, Artificial Life.

[18]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[19]  Joao Antonio Pereira,et al.  Linked: The new science of networks , 2002 .

[20]  Robert L. Axtell,et al.  WHY AGENTS? ON THE VARIED MOTIVATIONS FOR AGENT COMPUTING IN THE SOCIAL SCIENCES , 2000 .

[21]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[22]  G. Nigel Gilbert,et al.  Simulation for the social scientist , 1999 .

[23]  Charles M. Macal,et al.  Introduction: The Simulation of Social Agents , 2001 .

[24]  Michael Wooldridge,et al.  Agent-based software engineering , 1997, IEE Proc. Softw. Eng..

[25]  Steven O. Kimbrough,et al.  On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt , 2002 .

[26]  Charles M. Macal,et al.  Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation , 2007 .

[27]  Ivar Jacobson,et al.  The Unified Modeling Language User Guide , 1998, J. Database Manag..

[28]  Timothy A Kohler,et al.  Simulating ancient societies. , 2005, Scientific American.

[29]  T. Schelling Micromotives and Macrobehavior , 1978 .

[30]  Nelson Minar,et al.  The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations , 1996 .

[31]  V.S. Koritarov Real-world market representation with agents , 2004, IEEE Power and Energy Magazine.

[32]  Blake LeBaron,et al.  Short-memory traders and their impact on group learning in financial markets , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[33]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[34]  Robert Tobias,et al.  Evaluation of free Java-libraries for social-scientific agent based simulation , 2004, J. Artif. Soc. Soc. Simul..