Agent-Based Modeling: A Historical Perspective and a Review of Validation and Verification Efforts

Agent-Based Modeling: A Historical Perspective and a Review of Validation and Verification Efforts

Brian L. Heath (Wright State University, USA) and Raymond R. Hill (Air Force Institute of Technology, USA)
DOI: 10.4018/978-1-60566-774-4.ch003
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Abstract

Models and simulations have been widely used as a means to predict the performance of systems. Agentbased modeling and agent distillations have recently found tremendous success particularly in analyzing ground force employment and doctrine. They have also seen wide use in the social sciences modeling a plethora of real-life scenarios. The use of these models always raises the question of whether the model is correctly encoded (verified) and accurately or faithfully represents the system of interest (validated). The topic of agent-based model verification and validation has received increased interest. This chapter traces the historical roots of agent-based modeling. This review examines the modern influences of systems thinking, cybernetics as well as chaos and complexity on the growth of agent-based modeling. The chapter then examines the philosophical foundations of simulation verification and validation. Simulation verification and validation can be viewed from two quite different perspectives: the simulation philosopher and the simulation practitioner. Personnel from either camp are typically unaware of the other camp’s view of simulation verification and validation. This chapter examines both camps while also providing a survey of the literature and efforts pertaining to the verification and validation of agent-based models. The chapter closes with insights pertaining to agent-based modeling, the verification and validation of agent-based models, and potential directions for future research.
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Insights Into The Emergence Of Agent-Based Modeling

Introduction

Over the years Agent-Based Modeling (ABM) has become a popular tool used to model and understand the many complex, nonlinear systems seen in our world (Ferber, 1999). As a result, many papers geared toward modelers discuss the various aspects and uses of ABM. The topics typically covered include an explanation of ABM, when to use it, how to build it and with what software, how results can be analyzed, research opportunities, and discussions of successful applications of the modeling paradigm. It is also typical to find within these papers brief discussions about the origins of ABM, discussions that tend to emphasize the diverse applications of ABM as well as how some fundamental properties of ABM were discovered. However, these historical discussions often do not go into much depth about the fundamental theories and fields of inquiry that would eventually lead to ABM's emergence. Thus, in this chapter we re-examine some of the scientific developments in computers, complexity, and systems thinking that helped lead to the emergence of ABM, shed new light onto some old theories while connecting them to several key ABM principles of today. This chapter is not a complete account of the field, but does provide a historical perspective into ABM and complexity intended to provide a clearer understanding of the field, show the benefits of understanding the diverse origins of ABM, and hopefully spark further interest into the theories and ideas that laid the foundation for today’s ABM paradigm.

Key Terms in this Chapter

Cybernetics: The science of control and communication in the animal and the machine.

Agent-Based Modeling: A computational model for simulating the actions and interactions of autonomous individuals in a network, with a view to assessing their effects on the system as a whole.

Simulation: The imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviors of a selected physical or abstract system.

Validation: The process of determining whether a simulation model is an accurate representation of a system.

Nonlinearity: In terms of system output refers to the state wherein the system output as a collective is greater than the sum of the individual system component outputs.

Emergence: The process of coherent patterns of behavior arising from the self-organizing aspects of complex systems.

Verification: The act of reviewing, inspecting, testing, etc. to establish and document that a product, service, or system meets the regulatory, standard, or specification requirements.

Cellular Automata: A discrete model consisting of a grip of cells each of which have a finite number of defined states where the state of a cells is a function of the states of neighboring cells and the transition among states is according to some predefined updating rule.

Complex Adaptive Systems: A complex, self-similar collection of interacting agents, acting in parallel and reacting to other agent behaviors within the system.

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