Abstract

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.