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.

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.

An Agent-Based Model (ABM) accounting for tax-morale and loss-aversion was implemented over different network systems with social interactions at the local level to study the phenomenon of tax evasion. This ABM is an innovative model which integrates endogenous characteristics of heterogeneous agents and proposes a more relaxed assumption on the information exchanged between agents as compared to previous social models. The current study gives an insight on the possibility that choosing specific network structures may yield to more realistic outcomes. Moreover, this ABM manages to replicate both individual and aggregate results from previous experimental and computational models of tax evasion. A clearcut novelty might be the non-linear channel through which the network centrality enhances a positive effect on the aggregated level of tax compliance. There is a large area of action for public policy makers to further research the presented results about how audit rates, fines and tax morale non-linearly increase income disclosure, whereas tax rates have a non-linear negative impact on tax compliance.

"Swarm, a standard set of program libraries, allows users to construct simulations where a collection of heterogeneous independent agents or elements interact through discrete events. This volume offers the first extensive tutorial to the use of these software libraries developed at the Santa Fe Institute as part of the ongoing research into complexity."--BOOK JACKET.

We develop a model where heterogeneous agents maximize their individual utility based on (after tax) income and on the level of public expenditure (as in Cowell, Gordon, 1988). Agents are different in risk aversion and in the relative preference for public expenditure with respect to personal income. In each period, an agent can optimally conceal some income based on conjectures on the perceived probability of being subject to audits, the perceived level of public expenditure and the perceived amount of tax paid by other individuals. As far as the agent-based model is concerned, we assume that the Government sets the tax rate and the penalties, uses all the revenue to finance public expenditure (with no inefficiency) and fights evasion by controlling a (random) fraction of agents. We show that, through computational experiments based on micro-simulations, stable configurations of tax rates and public expenditure endogenously form in this case as well. In such equilibrium-like situations we find: a positive relationship between the tax rate and evasion still arises; tax compliance mainly depends on the distribution of personal features like risk-aversion and the degree of preference for public expenditure; an endogenous level of tax evasion that is almost not affected by reasonable rates of control. A proper choice of the tax rate results instead in voluntary partial compliance; the enforcement of higher compliance rates requires unrealistic and costly large-scale audits.

We present a design and model for Simulating Co-Evolution of Tax and Evasion (SCOTE). The system performs agent based modeling of the tax ecosystem and searches for tax evasion strategies using a variant of a Genetic Algorithm with a grammar. Current methodologies and tools to detect, discover or recognize tax evasion are not sufficient. In recent years the tax gap, the aggregate sum of the difference between the tax owed in principle and tax paid in practice was calculated to exceed 450 billion dollars. Numerous tax evasion schemes have surfaced that perform seemingly legal transactions but once observed closely their sole purpose is to reduce tax liability. Moreover, these schemes are evolving with time. Whenever a scheme is detected and eliminated by fixing a loop hole in the tax code, others emerge to replace it and currently there is no systematic way to predict the emergence of these schemes. SCOTE allows us to encode tax evasion strategies into a searchable representation. SCOTE has three major components namely the Genetic Algorithm library(GA), the interpreter and the Parser. The GA encodes transaction plans into an integer representation and performs search over the transaction plans to find a scheme that produces the maximum tax gap. The Parser performs grammatical mapping of list of integers to a transaction plan.The interpreter models the tax ecosystem into a graph where the entities such as taxpayer and partnerships are nodes and the transactions between entities are the edges. Each entity has a portfolio of assets and the values of the assets are updated after a transaction. The interpreter runs a transaction plan generated by GA on the graph to produce the tax gap. We ran two experiments using two of the known tax evasion schemes namely "Son of Boss" and "iBOB" and we were able to detect the two schemes using SCOTE.

This book presents the latest findings on network theory and agent-based modeling of economic and financial phenomena. In this context, the economy is depicted as a complex system consisting of heterogeneous agents that interact through evolving networks; the aggregate behavior of the economy arises out of billions of small-scale interactions that take place via countless economic agents. The book focuses on analytical modeling, and on the econometric and statistical analysis of the properties emerging from microscopic interactions. In particular, it highlights the latest empirical and theoretical advances, helping readers understand economic and financial networks, as well as new work on modeling behavior using rich, agent-based frameworks. Innovatively, the book combines observational and theoretical insights in the form of networks and agent-based models, both of which have proved to be extremely valuable in understanding non-linear and evolving complex systems. Given its scope, the book will capture the interest of graduate students and researchers from various disciplines (e.g. economics, computer science, physics, and applied mathematics) whose work involves the domain of complexity theory.