TY - JOUR
T1 - Model-based simulation of legal policies
T2 - framework, tool support, and validation
AU - Soltana, Ghanem
AU - Sannier, Nicolas
AU - Sabetzadeh, Mehrdad
AU - Briand, Lionel C.
N1 - Publisher Copyright:
© 2016, Springer-Verlag Berlin Heidelberg.
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
KW - Legal policies
KW - Model-driven code generation
KW - Probabilistic data generation
KW - Simulation
KW - UML profiles
UR - http://www.scopus.com/inward/record.url?scp=84977070444&partnerID=8YFLogxK
U2 - 10.1007/s10270-016-0542-0
DO - 10.1007/s10270-016-0542-0
M3 - Article
AN - SCOPUS:84977070444
SN - 1619-1366
VL - 17
SP - 851
EP - 883
JO - Software and Systems Modeling
JF - Software and Systems Modeling
IS - 3
ER -