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

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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.

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Notes

  1. This profile is not to be confused with the profile that we present in Sect. 5 for extending domain models with probabilistic information.

  2. We note that the traversal strategy of Alg. 1, discussed in Sect. 6.4, ensures that associations that have an end with a cardinality of 1, 0..1, 1..n or \(1..*\) will satisfy the multiplicity constraints. We further note that the satisfaction of many-to-many multiplicity constraints is a given. Nevertheless, our data generator interprets many-to-many multiplicity constraints as m-to-n ones, with m and n chosen either randomly or by the «multiplicity» stereotype.

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Acknowledgments

We thank members of Luxembourg’s Inland Revenue Office (ACD) and National Centre for Information Technologies (CTIE), particularly T. Prommenschenkel, L. Balmer, and M. Blau for sharing their valuable time and insights with us. Financial support was provided by CTIE and FNR under Grants FNR/P10/03 and FNR9242479.

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Authors and Affiliations

  1. SnT Centre for Security, Reliability and Trust, University of Luxembourg, Luxembourg, Luxembourg

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

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  1. Ghanem Soltana
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  2. Nicolas Sannier
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  3. Mehrdad Sabetzadeh
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  4. Lionel C. Briand
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Corresponding author

Correspondence to Ghanem Soltana.

Additional information

Communicated by Prof. Jordi Cabot and Prof. Alexander Egyed.

Appendix

Appendix

1.1 Transformation of legal policies to Java: detailed algorithm

The core algorithm for transforming legal policies to simulation code, named PMToJava, is presented in Alg. 4. This algorithm takes as input a Policy Model, PM, an \(\textsf {element} \in \mathcal {PM}\), the set of \(\mathcal {PM}\)’s inputs, \(inputs\_all\), and the set of inputs that have been already declared, \(inputs\_declared\). Note that, we assume that \(\mathcal {PM}\) uses only deterministic decisions as a modeling restriction (since loops are handled using expansion regions). The transformation is based on a set of predefined patterns. These patterns are detailed in 12.2. The transformation process is recursive and mimics a depth-first traversal of the underlying graph of \(\mathcal {PM}\). There are four main parts to this process: (1) pattern recognition (Alg. 4, L. 4–7); (2) input declarations (Alg. 4, L. 8–14); (3) transformation of all elements other than decisions (Alg. 4, L. 15–28). Within this class of elements, additional processing is necessary for expansion regions to propagate their output if they have any (Alg. 4, L. 25–27); and (4) transformation of decision nodes (Alg. 4, L. 29–36).

Main (Alg. 5) initializes the transformation’s variables and makes the first call to PMToJava over the input policy model \(\mathcal {PM}\). During its execution, PMToJava makes calls to Algs. 4-8. retrieveDependentInputs (Alg. 6) retrieves all input parameters that need to be declared before transforming a given pattern. getInitialNode (Alg. 7) returns the initial node of an Activity or an ExpansionRegion. getFlows (Alg. 8) returns, based on the current pattern being processed, the outgoing flows targeting the next elements that should be visited. operatorsToJava (Alg. 9) transforms the equality operators as well as the conditional operators from OCL to Java. recognizePattern (Alg. 10) identifies and creates the appropriate pattern that matches the shape formed by: the visited element, its stereotype, its container (Activity or ExpansionRegion) and its neighborhood (adjacent elements).

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1.2 Patterns for transforming legal policies to Java simulation code

Table 4 shows the patterns used by our transformation algorithm PMToJava (Alg. 4 of Appendix 12.1) for generating the appropriate Java simulation code (L. 19–20 of Alg. 4). The first column of Table 4 provides the name, the generic shape, and a brief description of each pattern. A pattern can be Elementary, composed of a single UML element, or Aggregated, composed of several UML elements. The second column shows the resulting Java fragments for each pattern. These fragments correspond to the opening and closing expressions, respectively, denoted in PMToJava by \(st_1\) and \(st_2\) (L. 19–20 of Alg. 4). The opening and closing expressions delimit the beginning and the end of a branch or a loop, respectively. Patterns that do not result in the creation of a branch or loop do not require a closing expression.

1.3 Consistency constraints of the profile for expressing the probabilistic characteristics of the simulation population

Table 5 shows the consistency constraints that check the sound application of our profile (the profile for probabilistic information) on a given domain model. The first column provides a description of the consistency constraints alongside their OCL expressions. The second column lists the stereotypes over which the consistency constraints apply.

Table 4 Patterns for transforming legal policies to Java simulation code
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Table 5 Consistency constraints (profile for express probabilistic characteristics)
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Soltana, G., Sannier, N., Sabetzadeh, M. et al. Model-based simulation of legal policies: framework, tool support, and validation. Softw Syst Model 17, 851–883 (2018). https://doi.org/10.1007/s10270-016-0542-0

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