Model-based simulation of legal policies: framework, tool support, and validation

Abstract

Simulation of legal policies 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. Legal policy simulation is currently implemented using a combination of spreadsheets and software code. Such a direct implementation poses a validation challenge. In particular, legal experts often lack the necessary software background to review complex spreadsheets and code. Consequently, these experts currently have no reliable means to check the correctness of simulations against the requirements envisaged by the law. A further challenge is that representative data for simulation may be unavailable, thus necessitating a data generator. A hard-coded generator is difficult to build and validate. We develop a framework for legal policy simulation that is aimed at addressing the challenges above. 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.

Synthetic data generation for statistical testing

“…To illustrate, we note three among several other logical anomalies that we observed when using our previous approach [12] for generating test cases based on a data profile of citizens' records: children who were older than their parents, individuals who were married before being born, and individuals who were classified as widower without ever having been married. Without the ability to enforce logical constraints to avoid such anomalies, the generated data is unsuitable for statistical testing and estimating reliability.…”

“…This is despite the fact that many data-centric systems, e.g., public administration and financial systems, are subject to reliability requirements and thus statistical testing. Recently, we proposed a statistical data profile and a heuristic algorithm for generating representative synthetic data [12]. Although motivated by microeconomic simulation [13] rather than software testing, our previous approach provides a useful basis for generating data that can be used for statistical testing.…”

“…In this section, we briefly describe our previous data generation approach [12]. We leverage this approach for (1) expressing the desired statistical characteristics of data, and (2) generating initial data samples which we process further to achieve not only representativeness but logical validity as well.…”

“…For generating a data sample, we previously proposed a heuristic technique that is aimed exclusively at representativeness [12]. This technique traverses the elements of the data schema and instantiates them according to the prescribed probabilities.…”

“…The contributions of this paper are as follows: 1) We develop a model-based test data generator that can simultaneously satisfy statistical representativeness and logical validity requirements over complex, interdependent data. The desired statistical characteristics are expressed using our previously-developed probabilistic UML annotations [12]. Validity constraints are expressed using UML's constraint language, OCL [15].…”