Agent-based Modeling of Tax Evasion. Theoretical Aspects and Computational Simulations. Wiley Series in Computational and Quantitative Social Science

  • ID: 4290450
  • Book
  • 376 Pages
  • John Wiley and Sons Ltd
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The only single–source guide to understanding, using, adapting, and designing state–of–the–art agent–based modeling of tax evasion

A computational method for simulating the behaviour 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 presented their findings at international conferences, until Agent–based Modeling of Tax Evasion there has been 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 Modeling 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 modeling 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 modeling 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.

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Notes on Contributors xiii

Foreword xxi

Preface xxvii

Part I INTRODUCTION

1 Agent–Based Modeling and Tax Evasion: Theory and Application 3Sascha Hokamp, László Gulyás, Matthew Koehler and H. Sanith Wijesinghe

1.1 Introduction 3

1.2 Tax Evasion, Tax Avoidance and Tax Noncompliance 4

1.3 Standard Theories of Tax Evasion 5

1.4 Agent–Based Models 10

1.5 Standard Protocols to Describe Agent–Based Models 11

1.5.1 The Overview, Design Concepts, Details, and Decision–Making Protocol 13

1.5.2 Concluding Remarks on the ODD+D Protocol 17

1.6 Literature Review of Agent–Based Tax Evasion Models 18

1.6.1 Public Goods, Governmental Tasks and Back Auditing 22

1.6.2 Replication, Docking, and Calibration Studies 25

1.6.3 Concluding Remarks on Agent–Based Tax Evasion Models 26

1.7 Outlook: The Structure and Presentation of the Book 27

1.7.1 Part I Introduction 28

1.7.2 Part II Agent–Based Tax Evasion Models 28

References 31

2 How Should One Study Clandestine Activities: Crimes, Tax Fraud, and Other Dark Economic Behavior? 37Aloys L. Prinz

2.1 Introduction 37

2.2 Why Study Clandestine Behavior At All? 38

2.3 Tools for Studying Clandestine Activities 40

2.4 Networks and the Complexity of Clandestine Interactions 42

2.5 Layers of Analysis 45

2.6 Research Tools and Clandestine Activities 48

2.7 Conclusion 55

Acknowledgment 56

References 56

3 Taxpayer s Behavior: From the Laboratory to Agent–Based Simulations 59Luigi Mittone and Viola L. Saredi

3.1 Tax Compliance: Theory and Evidence 59

3.2 Research on Tax Compliance: A Methodological Analysis 62

3.3 From Human–Subject to Computational–Agent Experiments 68

3.4 An Agent–Based Approach to Taxpayers Behavior 73

3.4.1 The Macroeconomic Approach 74

3.4.2 The Microeconomic Approach 77

3.4.3 Micro–Level Dynamics for Macro–Level Interactions among Behavioral Types 80

3.5 Conclusions 83

References 84

Part II AGENT–BASED TAX EVASION MODELS

4 Using Agent–Based Modeling to Analyze Tax Compliance and Auditing 91Nigar Hashimzade and Gareth Myles

4.1 Introduction 91

4.2 Agent–Based Model for Tax Compliance and Audit Research 93

4.2.1 Overview 93

4.2.2 Design Concepts 94

4.2.3 Details 98

4.3 Modeling Individual Compliance 98

4.3.1 Expected Utility 98

4.3.2 Behavioral Models 101

4.3.3 Psychic Costs and Social Customs 102

4.4 Risk–Taking and Income Distribution 106

4.5 Attitudes, Beliefs, and Network Effects 111

4.5.1 Networks and Meetings 113

4.5.2 Formation of Beliefs 113

4.6 Equilibrium with Random and Targeted Audits 115

4.7 Conclusions 119

Acknowledgments 122

References 122

Appendix 4A 123

5 SIMULFIS: A Simulation Tool to Explore Tax Compliance Behavior 125Toni Llacer, Francisco J. Miguel Quesada, José A. Noguera and Eduardo Tapia Tejada

5.1 Introduction 125

5.2 Model Description 126

5.2.1 Purpose 127

5.2.2 Entities, State Variables, and Scales 127

5.2.3 Process Overview and Scheduling 131

5.2.4 Theoretical and Empirical Background 131

5.2.5 Individual Decision Making 132

5.2.6 Learning 135

5.2.7 Individual Sensing 136

5.2.8 Individual Prediction 136

5.2.9 Interaction 137

5.2.10 Collectives 137

5.2.11 Heterogeneity 138

5.2.12 Stochasticity 138

5.2.13 Observation 139

5.2.14 Implementation Details 140

5.2.15 Initialization 140

5.2.16 Input Data 141

5.2.17 Submodels 141

5.3 Some Experimental Results and Conclusions 145

Acknowledgments 148

References 148

6 TAXSIM: A Generative Model to Study the Emerging Levels of Tax Compliance in a Single Market Sector 153László Gulyás, Tamás Máhr and István J. Tóth6.1 Introduction 153

6.2 Model Description 155

6.2.1 Overview 155

6.2.2 Design Concepts 165

6.2.3 Observation and Emergence 172

6.2.4 Details 173

6.3 Results 175

6.3.1 Scenarios 175

6.3.2 Sensitivity Analysis 182

6.3.3 Adaptive Audit Strategy 190

6.3.4 Minimum Wage Policies 192

6.4 Conclusions 194

Acknowledgments 196

References 196

7 Development and Calibration of a Large–Scale Agent–Based Model of Individual Tax Reporting Compliance 199Kim M. Bloomquist

7.1 Introduction 199

7.1.1 Taxpayer Dataset 201

7.1.2 Agents 202

7.1.3 Tax Agency 204

7.1.4 Taxpayer Reporting Behavior 207

7.1.5 Filer Behavioral Response to Tax Audit 209

7.1.6 Model Execution 210

7.2 Model Validation and Calibration 211

7.3 Hypothetical Simulation: Size of the Gig Economy and Taxpayer Compliance 214

7.4 Conclusion and Future Research 216

Acknowledgments 216

References 217

Appendix 7A: Overview, Design Concepts, and Details (ODD) 218

7A.1 Purpose 218

7A.2 Entities, State Variables, and Scales 218

7A.3 Process Overview and Scheduling 219

7A.4 Design Concepts 219

7A.4.1 Basic Principles 219

7A.4.2 Emergence 220

7A.4.3 Adaptation 220

7A.4.4 Objectives 220

7A.4.5 Learning 220

7A.4.6 Prediction 221

7A.4.7 Sensing 221

7A.4.8 Interaction 221

7A.4.9 Stochasticity 221

7A.4.10 Collectives 222

7A.4.11 Observation 222

7A.5 Initialization 223

7A.6 Input Data 223

7A.7 Submodels 224

8 Investigating the Effects of Network Structures in Massive Agent–Based Models of Tax Evasion 225Matthew Koehler, Shaun Michel, David Slater, Christine Harvey, Amanda Andrei and Kevin Comer

8.1 Introduction 225

8.2 Networks and Scale 226

8.3 The Model 230

8.3.1 Overview 230

8.3.2 Design Concepts 232

8.3.3 Details 237

8.4 The Experiment 241

8.5 Results 241

8.5.1 Impact of Scale 243

8.5.2 Distributing the Model on a Cluster Computer 246

8.6 Conclusion 251

References 251

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 255Sascha Hokamp and Andrés M. Cuervo Díaz

9.1 Introduction 255

9.2 The Agent–Based Tax Evasion Model 257

9.2.1 Overview of the Model 257

9.2.2 Design Concepts 264

9.2.3 Details 268

9.3 Scenarios, Simulation Results, and Discussion 269

9.3.1 Age Heterogeneity and Social Norm Updating 269

9.3.2 Public Goods Provision and Pareto–optimality 274

9.3.3 The Allingham–and–Sandmo Approach Reconsidered 277

9.3.4 Calibration and Sensitivity Analysis 281

9.4 Conclusions and Outlook 284

Acknowledgments 285

References 285

Appendix 9A 287

10 Modeling the Co–evolution of Tax Shelters and Audit Priorities 289Jacob Rosen, Geoffrey Warner, Erik Hemberg, H. Sanith Wijesinghe and Una–May O Reilly

10.1 Introduction 289

10.2 Overview 291

10.3 Design Concepts 293

10.3.1 Simulation 294

10.3.2 Optimization 297

10.4 Details 299

10.4.1 IBOB 299

10.4.2 Grammar 302

10.4.3 Parameters 304

10.5 Experiments 305

10.5.1 Experiment LimitedAudit: Audit Observables That Do Not Detect IBOB 305

10.5.2 Experiment EffectiveAudit: Audit Observables That Can Detect IBOB 308

10.5.3 Experiment CoEvolution: Sustained Oscillatory Dynamics Of Fitness Values 308

10.6 Discussion 311

References 314

11 From Spins to Agents: An Econophysics Approach to Tax Evasion 315Götz Seibold

11.1 Introduction 315

11.2 The Ising Model 316

11.2.1 Purpose 316

11.2.2 Entities, State Variables, and Scales 316

11.2.3 Process Overview and Scheduling 318

11.3 Application to Tax Evasion 320

11.4 Heterogeneous Agents 324

11.5 Relation to Binary Choice Model 330

11.6 Summary and Outlook 333

References 334

Index 337

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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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