A model-based framework for probabilistic simulation of legal policies

Legal policy simulation is an important decision-support tool in domains such as taxation. The primary goal of legal policy simulation is predicting how changes in the law affect measures of interest, e.g., revenue. Currently, legal policies are simulated via a combination of spreadsheets and software code. This poses a validation challenge both due to complexity reasons and due to legal experts lacking the expertise to understand software code. A further challenge is that representative data for simulation may be unavailable, thus necessitating a data generator. We develop a framework for legal policy simulation that is aimed at addressing these challenges. The framework uses models for specifying both legal policies and the probabilistic characteristics of the underlying population. We devise an automated algorithm for simulation data generation. We evaluate our framework through a case study on Luxembourg's Tax Law.