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

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Chapter 7Development and Calibration of a Large-Scale Agent-Based Model of Individual Tax Reporting Compliance

Kim M. Bloomquist

7.1 Introduction

Since the publication of the groundbreaking theoretical work by Allingham and Sandmo (1972) and Srinivasan (1973), much has been learned about the determinants of taxpayer compliance.1 Despite these advances, progress has lagged in transforming this knowledge into computational tools that tax officials can use to conduct in silico tests of proposed tax service and enforcement programs prior to implementation on potentially millions of taxpayers. Alm (1999) suggests that the key reason for this lack of progress is the inability of existing analytical (i.e., mathematical) models to incorporate sufficient real-world taxpayer behavior and he goes on to point out that past efforts to introduce greater realism into the standard rational choice model of taxpayer decision-making have tended only to increase the ambiguity of the model's predictions. A similar observation has been made by Janssen and Ostrom (2006) and Axtell (2000) for complex social and ecological systems in general. Increasingly, researchers are concluding that agent-based modeling and simulation (ABMS) is a methodology that is well suited for modeling complex social phenomena, of which taxpayer compliance is a prime example (see Alm, 2010).

This chapter describes the development and calibration of a large-scale ABM that simulates the income tax reporting behavior of a community ...

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