Computational Science of Taxation
Full course description
The aim of this course is that students learn to think interdisciplinary between tax and technology. Computational science is a multidisciplinary field and it aims to understand complex systems by developing computational models and simulations. Computational taxation is to tax law what bioinformatics is to medicine and econometrics is to economics. The focus question of this course is how computational models and methods may help to understand and improve the tax domain and complexity in taxation? This course is not about the simple calculation or accounting of taxes! Although students are not required to have a prior background in advanced mathematics or computer science to be able to successfully follow this course, they should have strong analytical thinking skills and an aptitude for quantitative methods.
The course starts and completes with the broad context of computational science for taxation in weeks 1 and 7. In week 2, students will be introduced to the foundations of computational modelling for taxation. Weeks 3 and 4, will give particular attention to the value added of data science and artificial intelligence methods for computational modelling. Week 6 asks students to investigate how engineering methods and computational models may optimize taxation. Students are so given the required knowledge that they can apply to create an innovative computational taxation solution.
The course includes various live teaching and learning activities (TLAs), which may be complemented by open source online education (e.g. DataCamp for the Classroom). The course comprises both problem-based and project-based learning methods. Due to the fact that students from VU and UvT will attend this course, all live TLAs are planned on only one day in the week (which requires close coordination between universities).
Students who successfully complete this course will able to build bridges between the tax domain and the technology domain. They will have the conceptual knowledge and personal competences to be able to co-create innovative computational tax solutions and work in multi-disciplinary teams of tax lawyers, business & public policy advisors, and computer and data scientists.
Upon successful completion of this course, a student is able to:
- Describe and explain major historical, current and future developments in computer technology and data science and their potential to reinvent the tax domain of the future.
- Describe and explain taxation as a computational model;
- Translate fundamental questions of taxation as computational models;
- Describe, explain and apply the data science process;
- Model a data workflow for a taxation problem;
- Describe and explain how artificial intelligence can improve understanding the complexity of taxation.
- Describe and explain the use of ontologies for tax domain modelling.
- Describe and explain how engineering methods and computational models may be used to optimize taxation.
3. Create and present a proof of concept of a computational taxation solution to peers and experts.
To be announced