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

Ghanem Soltana, Nicolas Sannier, Mehrdad Sabetzadeh, Lionel C. Briand

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)851-883
Number of pages33
JournalSoftware and Systems Modeling
Volume17
Issue number3
DOIs
Publication statusPublished - 1 Jul 2018
Externally publishedYes

Keywords

  • Legal policies
  • Model-driven code generation
  • Probabilistic data generation
  • Simulation
  • UML profiles

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