Tutorial simulation agent based modeling

Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds.

TUTORIAL ON AGENT-BASED MODELING AND SIMULATION PART 2: HOW TO MODEL WITH AGENTS Charles M. Macal Michael J. North Center for Complex Adaptive Agent Systems Simulation (CAS2) Decision & Information Sciences Division Argonne National Laboratory Argonne,IL 60439, U.S.A.

ABSTRACT 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 doingscience. 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. Thistutorial 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 INTRODUCTION

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Used: How ABMS is being used to solve practical problems.

This tutorial is organized into two parts. The first part is a tutorial on howto think about ABMS. The background on ABMS and its motivating principles are described to illustrate its main concepts and to indicate the state-of-the-art. The second part is a tutorial on how to do ABMS. Practical applications of ABMS are described, ABMS toolkits are presented, and ABMS development approaches are discussed. Several ABMS examples are demonstrated throughout the tutorial. 2 HOW TOTHINK ABOUT ABMS

2.1 What Is An Agent? Although there is no universal agreement on the precise definition of the term “agent,” definitions tend to agree on more points than they disagree. Some modelers consider any type of independent component (software, model, individual, etc.) to be an agent (Bonabeau 2001); an independent component’s behavior can range from primitive reactive decisionrules to complex adaptive artificial intelligence (AI). Others insist that a component’s behavior must be adaptive in order for it to be considered an agent; the agent label is reserved for components that can in some sense learn from their environments and change their behaviors in response. Casti (1997) argues that agents should contain both base-level rules for behavior as well as a higher-levelset of “rules to change the rules.” The baselevel rules provide responses to the environment while the “rules to change the rules” provide adaptation. Jennings (2000) provides a computer science view of agency emphasizing the essential characteristic of autonomous behavior. The fundamental feature of an agent is the capability of the component to make independent decisions. This requires agents tobe active rather than purely passive.

Agent-based Modeling and Simulation (ABMS) is a new modeling paradigm and is one of the most exciting practical developments in modeling since the invention of relational databases (North and Macal, in press). ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electroniclaboratories to support their research. The goals of this tutorial are to show how ABMS is: • • Useful: Why ABMS is a good and even better modeling approach in many cases, Usable: How we are progressively advancing to usable ABMS systems, with better software development environments and more application experiences, and

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Macal and North From a practical...