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Agent-based Modeling of Tax Evasion by Sanith Wijesinghe, Matthew Koehler, Laszlo Gulyas, Sascha Hokamp

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Chapter 8Investigating the Effects of Network Structures in Massive Agent-Based Models of Tax Evasion

Matthew Koehler, Shaun Michel, David Slater, Christine Harvey, Amanda Andrei and Kevin Comer

8.1 Introduction

With the ongoing increase in computing power, agent-based models have become a preferred tool of choice for the study of complex adaptive systems, especially those systems in which humans are a nontrivial part. Agent-based models are an appropriate choice for these types of systems as they allow the modeler to express the system more naturally, using a logical rule-based approach, rather than with closed form equations that require strong assumptions to be made about the said system (Axtell, 2000b; Epstein, 2006). This is particularly the case when the system is made up of a large (but not infinite) number of discrete, adaptive agents (i.e., humans) that may change, adapt, or coordinate their behaviors over time, recognized as organized complexity (Weaver, 1948). These types of systems currently stymie closed form analysis as well as statistical approximation (Weaver, 1948). Under these circumstances the most efficient way to understand the temporal dynamics of the system is to simulate it (Buss et al., 1990).

With respect to the analysis of a tax system, agent-based modeling (ABM) may be particularly useful. By representing the system as a collection of interacting individuals, displaying bounded rationality, each embedded within a space and social network, one can ...

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