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E-raamat: Agent-based Modeling of Tax Evasion: Theoretical Aspects and Computational Simulations

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The only single-source guide to understanding, using, adapting, and designing state-of-the-art agent-based modelling of tax evasion

A computational method for simulating the behavior of individuals or groups and their effects on an entire system, agent-based modeling has proven itself to be a powerful new tool for detecting tax fraud. While interdisciplinary groups and individuals working in the tax domain have published numerous articles in diverse peer-reviewed journals and have presented their findings at international conferences, until Agent-based Modelling of Tax Evasion there was no authoritative, single-source guide to state-of-the-art agent-based tax evasion modeling techniques and technologies.

Featuring contributions from distinguished experts in the field from around the globe, Agent-Based Modelling of Tax Evasion provides in-depth coverage of an array of field tested agent-based tax evasion models. Models are presented in a unified format so as to enable readers to systematically work their way through the various modeling alternatives available to them. Three main components of each agent-based model are explored in accordance with the Overview, Design Concepts, and Details (ODD) protocol, each section of which contains several sub elements that help to illustrate the model clearly and that assist readers in replicating the modeling results described.

  • Presents models in a unified and structured manner to provide a point of reference for readers interested in agent-based modelling of tax evasion
  • Explores the theoretical aspects and diversity of agent-based modeling through the example of tax evasion
  • Provides an overview of the characteristics of more than thirty agent-based tax evasion frameworks
  • Functions as a solid foundation for lectures and seminars on agent-based modelling of tax evasion

The only comprehensive treatment of agent-based tax evasion models and their applications, this book is an indispensable working resource for practitioners and tax evasion modelers both in the agent-based computational domain and using other methodologies. It is also an excellent pedagogical resource for teaching tax evasion modeling and/or agent-based modeling generally. 

Notes on Contributors xiii
Foreword xxi
Preface xxvii
Part I INTRODUCTION
1 Agent-Based Modeling and Tax Evasion: Theory and Application
3(34)
Sascha Hokamp
Laszlo Gulyas
Matthew Koehler
H. Sanith Wijesinghe
1.1 Introduction
3(1)
1.2 Tax Evasion, Tax Avoidance and Tax Noncompliance
4(1)
1.3 Standard Theories of Tax Evasion
5(5)
1.4 Agent-Based Models
10(1)
1.5 Standard Protocols to Describe Agent-Based Models
11(7)
1.5.1 The Overview, Design Concepts, Details, and Decision-Making Protocol
13(4)
1.5.2 Concluding Remarks on the ODD+D Protocol
17(1)
1.6 Literature Review of Agent-Based Tax Evasion Models
18(9)
1.6.1 Public Goods, Governmental Tasks and Back Auditing
22(3)
7.6.2 Replication, Docking, and Calibration Studies
25(1)
1.6.3 Concluding Remarks on Agent-Based Tax Evasion Models
26(1)
1.7 Outlook: The Structure and Presentation of the Book
27(10)
1.7.1 Part I Introduction
28(1)
7.7.2 Part II Agent-Based Tax Evasion Models
28(3)
References
31(6)
2 How Should One Study Clandestine Activities: Crimes, Tax Fraud, and Other "Dark" Economic Behavior?
37(22)
Aloys L. Prinz
2.1 Introduction
37(1)
2.2 Why Study Clandestine Behavior At All?
38(2)
2.3 Tools for Studying Clandestine Activities
40(2)
2.4 Networks and the Complexity of Clandestine Interactions
42(3)
2.5 Layers of Analysis
45(3)
2.6 Research Tools and Clandestine Activities
48(7)
2.7 Conclusion
55(4)
Acknowledgment
56(1)
References
56(3)
3 Taxpayer's Behavior: From the Laboratory to Agent-Based Simulations
59(32)
Luigi Mittone
Viola L. Saredi
3.1 Tax Compliance: Theory and Evidence
59(3)
3.2 Research on Tax Compliance: A Methodological Analysis
62(6)
3.3 From Human-Subject to Computational-Agent Experiments
68(5)
3.4 An Agent-Based Approach to Taxpayers' Behavior
73(10)
3.4.1 The Macroeconomic Approach
74(3)
3.4.2 The Microeconomic Approach
77(69)
3.4.3 Micro-Level Dynamics for Macro-Level Interactions among Behavioral Types
80(3)
3.5 Conclusions
83(8)
References
84(7)
Part II AGENT-BASED TAX EVASION MODELS
4 Using Agent-Based Modeling to Analyze Tax Compliance and Auditing
91(34)
Nigar Hashimzade
Gareth Myles
4.1 Introduction
91(2)
4.2 Agent-Based Model for Tax Compliance and Audit Research
93(5)
4.2.1 Overview
93(1)
4.2.2 Design Concepts
94(4)
4.2.3 Details
98(1)
4.3 Modeling Individual Compliance
98(8)
4.3.1 Expected Utility
98(3)
4.3.2 Behavioral Models
101(1)
4.3.3 Psychic Costs and Social Customs
102(4)
4.4 Risk-Taking and Income Distribution
106(5)
4.5 Attitudes, Beliefs, and Network Effects
111(4)
4.5.1 Networks and Meetings
113(1)
4.5.2 Formation of Beliefs
113(2)
4.6 Equilibrium with Random and Targeted Audits
115(4)
4.7 Conclusions
119(6)
Acknowledgments
122(1)
References
122(1)
Appendix 4A
123(2)
5 SIMULFIS: A Simulation Tool to Explore Tax Compliance Behavior
125(28)
Torn Llacer
Francisco J. Miguel Quesada
Jose A. Noguera
Eduardo Tapia Tejada
5.1 Introduction
125(1)
5.2 Model Description
126(19)
5.2.1 Purpose
127(1)
5.2.2 Entities, State Variables, and Scales
127(4)
5.2.3 Process Overview and Scheduling
131(1)
5.2.4 Theoretical and Empirical Background
131(1)
5.2.5 Individual Decision Making
132(3)
5.2.6 Learning
135(1)
5.2.7 Individual Sensing
136(1)
5.2.8 Individual Prediction
136(1)
5.2.9 Interaction
137(1)
5.2.10 Collectives
137(1)
5.2.11 Heterogeneity
138(1)
5.2.12 Stochasticity
138(1)
5.2.13 Observation
139(1)
5.2.14 Implementation Details
140(1)
5.2.15 Initialization
140(1)
5.2.76 Input Data
141(1)
5.2.77 Submodels
141(4)
5.3 Some Experimental Results and Conclusions
145(8)
Acknowledgments
148(1)
References
148(5)
6 TAXSIM: A Generative Model to Study the Emerging Levels of Tax Compliance in a Single Market Sector
153(46)
Laszlo Gulyas
Tamas Mahr
Istvan J. Toth
6.1 Introduction
153(2)
6.2 Model Description
155(20)
6.2.7 Overview
155(10)
6.2.2 Design Concepts
165(7)
6.2.3 Observation and Emergence
172(1)
6.2.4 Details
173(2)
6.3 Results
175(19)
6.3.1 Scenarios
175(7)
6.3.2 Sensitivity Analysis
182(8)
6.3.3 Adaptive Audit Strategy
190(2)
6.3.4 Minimum Wage Policies
192(2)
6.4 Conclusions
194(5)
Acknowledgments
196(1)
References
196(3)
7 Development and Calibration of a Large-Scale Agent-Based Model of Individual Tax Reporting Compliance
199(26)
Kim M. Bloomquist
7.1 Introduction
199(12)
7.1.1 Taxpayer Dataset
201(1)
7.7.2 Agents
202(2)
7.1.3 Tax Agency
204(3)
7.7.4 Taxpayer Reporting Behavior
207(2)
7.7.5 Filer Behavioral Response to Tax Audit
209(1)
7.7.6 Model Execution
210(1)
7.2 Model Validation and Calibration
211(3)
7.3 Hypothetical Simulation: Size of the "Gig" Economy and Taxpayer Compliance
214(2)
7.4 Conclusion and Future Research
216(2)
Acknowledgments
216(1)
References
217(1)
Appendix 7A Overview, Design Concepts, and Details (ODD)
218(1)
7A.1 Purpose
218(1)
7A.2 Entities, State Variables, and Scales
218(1)
7A.3 Process Overview and Scheduling
219(1)
7A.4 Design Concepts
219(4)
7A.4.1 Basic Principles
219(1)
7A.4.2 Emergence
220(1)
7A.4.3 Adaptation
220(1)
7A.4.4 Objectives
220(1)
7A.4.5 Learning
220(1)
7A.4.6 Prediction
221(1)
7A.4.7 Sensing
221(1)
7A.4.8 Interaction
221(1)
7A A.9 Stochasticity
221(1)
7A.4.10 Collectives
222(1)
7A.4.11 Observation
222(1)
7A.5 Initialization
223(1)
7A.6 Input Data
223(1)
7A.7 Submodels
224(1)
8 Investigating the Effects of Network Structures in Massive Agent-Based Models of Tax Evasion
225(30)
Matthew Koehler
Shaun Michel
David Slater
Christine Harvey
Amanda Andrei
Kevin Comer
8.1 Introduction
225(1)
8.2 Networks and Scale
226(4)
8.3 The Model
230(11)
8.3.1 Overview
230(2)
8.3.2 Design Concepts
232(5)
8.3.3 Details
237(4)
8.4 The Experiment
241(1)
8.5 Results
241(10)
8.5.1 Impact of Scale
243(3)
8.5.2 Distributing the Model on a Cluster Computer
246(5)
8.6 Conclusion
251(4)
References
251(4)
9 Agent-Based Simulations of Tax Evasion: Dynamics by Lapse of Time, Social Norms, Age Heterogeneity, Subjective Audit Probability, Public Goods Provision, and Pareto-Optimality
255(34)
Sascha Hokamp
Andres M. Cuervo Diaz
9.1 Introduction
255(2)
9.2 The Agent-Based Tax Evasion Model
257(12)
9.2.1 Overview of the Model
257(7)
9.2.2 Design Concepts
264(4)
9.2.3 Details
268(1)
9.3 Scenarios, Simulation Results, and Discussion
269(15)
9.3.1 Age Heterogeneity and Social Norm Updating
269(5)
9.3.2 Public Goods Provision and Pareto-optimality
274(3)
9.3.3 The Allingham-and-Sandmo Approach Reconsidered
277(4)
9.3.4 Calibration and Sensitivity Analysis
281(3)
9.4 Conclusions and Outlook
284(5)
Acknowledgments
285(1)
References
285(2)
Appendix 9A
287(2)
10 Modeling the Co-evolution of Tax Shelters and Audit Priorities
289(26)
Jacob Rosen
Geoffrey Warner
Erik Hemberg
H. Sanith Wijesinghe
Una-May O'Reilly
10.1 Introduction
289(2)
10.2 Overview
291(2)
10.3 Design Concepts
293(6)
10.3.1 Simulation
294(3)
10.3.2 Optimization
297(2)
10.4 Details
299(6)
10.4.1 IBOB
299(3)
10.4.2 Grammar
302(2)
10.4.3 Parameters
304(1)
10.5 Experiments
305(6)
10.5.1 Experiment LimitedAudit: Audit Observables That Do Not Detect IBOB
305(3)
10.5.2 Experiment Effective Audit: Audit Observables That Can Detect IBOB
308(1)
10.5.3 Experiment CoEvolution: Sustained Oscillatory Dynamics Of Fitness Values
308(3)
10.6 Discussion
311(4)
References
314(1)
11 From Spins to Agents: An Econophysics Approach to Tax Evasion
315(22)
Gotz Seibold
11.1 Introduction
315(1)
11.2 The Ising Model
316(4)
11.2.1 Purpose
316(1)
11.2.2 Entities, State Variables, and Scales
316(2)
11.2.3 Process Overview and Scheduling
318(2)
11.3 Application to Tax Evasion
320(4)
11.4 Heterogeneous Agents
324(6)
11.5 Relation to Binary Choice Model
330(3)
11.6 Summary and Outlook
333(4)
References
334(3)
Index 337
Sascha Hokamp, PhD is a member of the Research Unit for Sustainability and Global Change (FNU) and of the Center for Earth System Research and Sustainability (CEN), Universität Hamburg. His research topics include illicit activities (tax evasion and doping in elite sports) and the shadow economy.

László Gulyás, PhD is Assistant Professor at Eötvös Loránd University, Budapest. He is a former Head of Division at AITIA International, Inc. He has been doing research on agent-based modeling and multi-agent systems since 1996.

Matthew Koehler, PhD is the Applied Complexity Sciences Area Lead for US Treasury/Internal Revenue Service, US Commerce, and Social Security Administration Program Division at The MITRE Corporation.

Sanith Wijesinghe, PhD is Chief Engineer of the Model Based Analytics department at The MITRE Corporation.
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