DATA474-19S2 (C) Semester Two 2019

Special Topic: Computational Social Choice

15 points
15 Jul 2019 - 10 Nov 2019


This course provides a thorough introduction to both classical and computational social choice. Social choice theory is the study of mechanisms for collective decision making, such as voting rules or protocols for fair division. Computational social choice addresses problems at the interface of social choice theory with computer science, it uses concepts from social choice theory in the presence of big datasets. This course will introduce some of the fundamental concepts in social choice theory and how they are used in today's data science. The topics covered include material in voting theory, preference aggregation, judgment aggregation, and fair division.

Learning Outcomes

  • On successful completion of this course, students will have:
  • An overall understanding of computational social choice, especially decision making in the presence of big datasets.
  • An understanding of strategic behaviour in preference aggregation.
  • A basic knowledge of proof concepts in computational complexity.
  • The capability of designing algorithms and mechanisms for decision-making purposes in the presence of big data sets.  
  • Acquired the capacity to work independently and manage their time in order to meet course deadlines.
    • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Critically competent in a core academic discipline of their award

      Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.

      Employable, innovative and enterprising

      Students will develop key skills and attributes sought by employers that can be used in a range of applications.

      Globally aware

      Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.


Subject to approval of the Head of Department of Mathematics and Statistics.

Timetable 2019

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Wednesday 10:00 - 12:00 Jack Erskine 441 15 Jul - 25 Aug
9 Sep - 20 Oct

Course Coordinator / Lecturer

Gabor Erdelyi


- F. Brandt, V. Conitzer, U. Endriss, J. Lang, and A. Procaccia editors, Handbook of Computational Social Choice.  Cambridge University Press, 2016
- J. Rothe editor, Economics and Computation.  Springer, 2015

Indicative Fees

Domestic fee $1,002.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All DATA474 Occurrences

  • DATA474-19S2 (C) Semester Two 2019