A model-based framework for probabilistic simulation of legal policies
作者： Ghanem Soltana , Nicolas Sannier , Mehrdad Sabetzadeh , Lionel C. Briand
DOI: 10.1109/MODELS.2015.7338237
关键词:
摘要: Legal policy simulation is an important decision-support tool in domains such as taxation. The primary goal of legal predicting how changes the law affect measures interest, e.g., revenue. Currently, policies are simulated via a combination spreadsheets and software code. This poses validation challenge both due to complexity reasons experts lacking expertise understand A further that representative data for may be unavailable, thus necessitating generator. We develop framework aimed at addressing these challenges. uses models specifying probabilistic characteristics underlying population. devise automated algorithm generation. evaluate our through case study on Luxembourg's Tax Law.
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