MA437      Half Unit
Learning Dynamics in Games

This information is for the 2025/26 session.

Course Convenor

Prof Bernhard Von Stengel

Dr Galit Ashkenazi-Golan

Availability

This course is available on the MSc in Mathematics and Computation and MSc in Operations Research & Analytics. This course is available with permission as an outside option to students on other programmes where regulations permit. This course uses controlled access as part of the course selection process.

In case the cap is reached, the lecturers will prioritise access according to interest, background, and fit with study plans such as planned dissertations of participants.

Requisites

Pre-requisites:

Students must have completed MA301 before taking this course.

Course content

Multiagent learning dynamics and their challenges.

Fictitious play, replicator dynamics, projected gradient, Q-learning, learning with neural networks.

Convergence and limit behaviour, reachable types of equilibria (Nash, correlated, coarse correlated), relation to game types such as zero-sum or potential games.

Teaching

20 hours of lectures and 10 hours of seminars in the Winter Term.

The first five seminars will involve exercises and formative presentation rehearsals. The second five seminars will involve exercises and summative presentations.

Formative assessment

Problem sets will be set and marked and discussed in the seminars, where students will also have the opportunity to first rehearse giving presentations. Later in the seminars, each student will give a summatively assessed presentation.

Indicative reading

Reading materials will be provided on the Moodle page of the course.

Assessment

Exam (75%), duration: 120 Minutes in the Spring exam period

Project (25%) in Winter Term Week 6

The presentation will be accompanied by a short written report.

The presentation and report combined account for 25% of the mark.

The written exam accounts for 75%.


Key facts

Department: Mathematics

Course Study Period: Winter Term

Unit value: Half unit

FHEQ Level: Level 7

Keywords: Game Theory, Machine Learning, Artificial Intelligence, Learning Dynamics

Total students 2024/25: Unavailable

Average class size 2024/25: Unavailable

Controlled access 2024/25: No
Guidelines for interpreting course guide information

Course selection videos

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.

Personal development skills

  • Leadership
  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills