Tax Compliance and Public Goods Provision -- An Agent-based Econophysics Approach

We calculate the dynamics of tax evasion within a multi-agent econophysics model which is adopted from the theory of magnetism and previously has been shown to capture the main characteristics from agent-based based models which build on the standard Allingham and Sandmo approach. In particular, we implement a feedback of public goods provision on the decision-making of selfish agents which aim to pursue their self interest. Our results imply that such a feedback enhances the moral attitude of selfish agents thus reducing the percentage of tax evasion. Two parameters govern the behavior of selfish agents, (i) the rate of adaption to changes in public goods provision and (ii) the threshold of perception of public goods provision. Furtheron we analyze the tax evasion dynamics for different agent co mpositions and under the feedback of public goods provision. We conclude that policymakers may enhance tax compliance behavior via the threshold of perception by means of targeted public relations.Comment: 28 pages, 3 figures, accepted for publication in the Central European Journal of Economic Modelling and Econometric

Income tax evasion dynamics: Evidence from an agent-based econophysics model

We analyze income tax evasion dynamics in a standard model of statistical mechanics, the Ising model of ferromagnetism. However, in contrast to previous research, we use an inhomogeneous multi-dimensional Ising model where the local degrees of freedom (agents) are subject to a specific social temperature and coupled to external fields which govern their social behavior. This new modeling frame allows for analyzing large societies of four different and interacting agent types. As a second novelty, our model may reproduce results from agent-based models that incorporate standard Allingham and Sandmo tax evasion features as well as results from existing two-dimensional Ising based tax evasion models. We then use our model for analyzing income tax evasion dynamics under different enforcement scenarios and point to some policy implications. --tax evasion,tax compliance,Ising Model,econophysics,numerical simulation

Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives

Model Replication in the Context of Agent-Based Simulation: Lessons Learnt from Two Case Studies

This paper examines model replication in the context of agent-based simulation through two case studies. Replication of a computational model and validation of its results is an essential tool for scientific researchers, but it is rarely used by modelers. In our work we address the question of validating and verifying simulations in general, and summarize our experience in approaching different models through replication with different motivations. Two models are discussed in details. The first one is an agent-based spatial adaptation of a numerical model, while the second experiment addresses the exact replication of an existing economic model

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

Supply Chain Based Solution to Prevent Fuel Tax Evasion: Proof of Concept Final Report

The goal of this research was to provide a proof-of-concept (POC) system for preventing non-taxable (non-highway diesel use) or low-taxable (jet fuel) petrochemical products from being blended with taxable fuel products and preventing taxable fuel products from cross-jurisdiction evasion. The research worked to fill the need to validate the legitimacy of individual loads, offloads, and movements by integrating and validating, on a near-real-time basis, information from global positioning system (GPS), valve sensors, level sensors, and fuel-marker sensors

Panoptic dual-use management: preventing deliberate pandemics in an age of synthetic biology and artificial intelligence

Powerful new technologies can have profound global security implications. In this thesis, I investigate how advances in synthetic biology and artificial intelligence could have dual-use potential and enable the deliberate release of pandemic pathogens. I review risks from synthetic biology based on case studies on wildlife virus discovery, viral engineering for vaccine design, and viral engineering for gene therapy. For assessing impacts of artificial intelligence, I consider large language models and biodesign tools. I find that related advances can create new methods to engineer pathogens and make such capabilities increasingly accessible to non-specialists. These risks are not well captured by existing risk mitigation measures. I argue that the management of dual-use virological research is currently defined by oversight of individual research projects. This is effective for addressing high-risk research but fails to address risks from a more diffuse set of research and technologies with dual-use potential. To help mitigate these risks, I introduce the idea of panoptic dual-use management. Inspired by methodologies to reduce carbon emissions, panoptic dual-use management involves treating associated dual-use risks as negative externalities and creating appropriate incentives so they are accounted for in decisions between projects. I explore ways in which such incentives could be created for various stakeholders. For instance, funding bodies could use dual-use risks as a tiebreaker between projects on the brink of getting funded, a practice which would incentivise researchers to preferentially propose projects with lower dual-use risks. To realise this proposal, I sketch out a framework for assigning tiered dual-use scores to virological research. I conclude by highlighting the importance of combining different dual-use management approaches across stakeholders and geographies to establish an effective complex of overlapping mitigation regimes