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Agent-Based Simulation of Tax Reporting Compliance

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dc.contributor.advisor Axtell, Robert L. Bloomquist, Kim Michael
dc.creator Bloomquist, Kim Michael 2012-07-20 2012-09-17T19:29:35Z NO_RESTRICTION en_US 2012-09-17T19:29:35Z 2012-09-17
dc.description.abstract Following the global financial crisis of 2008 many national governments have a renewed urgency to collect taxes not paid by noncompliant taxpayers. However, despite decades of theoretical and applied research progress has lagged on the development of computational tools to help tax administrators devise effective compliance improvement strategies. This study aims to bridge this gap by introducing the Individual Reporting Compliance Model (IRCM), an agent-based computational model that simulates tax reporting compliance in a community of 85,000 individual taxpayers, their employers and tax preparers. The model uses detailed tax return information yet maintains taxpayer anonymity by replacing actual tax returns with cases from the Statistics of Income (SOI) Public Use File [Weber 2004]. After reviewing the theoretical and empirical literature on taxpayer compliance, this study describes the development of the IRCM and demonstrates its capabilities in several simulation experiments.
dc.language.iso en en_US
dc.subject agent-based model en_US
dc.subject tax gap en_US
dc.subject tax compliance en_US
dc.subject internal revenue service en_US
dc.subject social simulation en_US
dc.subject taxpayer reporting behavior en_US
dc.title Agent-Based Simulation of Tax Reporting Compliance en_US
dc.type Dissertation en PhD in Computational Social Science en_US Doctoral en Computational Social Science en George Mason University en

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  • Krasnow Institute for Advanced Study
    Seeking to understand the human mind: how it came to be, how it relates to the electrochemical activities of networks of nerve cells in the brain, how it can be modeled on computers, and how it is a vital component of what we are.

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