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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.
1. An overall understanding of computational social choice, especially decision making in the presence of big datasets. 2. An understanding of strategic behaviour in preference aggregation. 3. A basic knowledge of proof concepts in computational complexity. 4. The capability of designing algorithms and mechanisms for decision-making purposes in the presence of big data sets. 5. Acquired the capacity to work independently and manage their time in order to meet course deadlines.
Subject to approval of the Head of Department of Mathematics and Statistics.
Students must attend one activity from each section.
General information for students
Domestic fee $952.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics