MA434      Half Unit
Algorithmic Game Theory

This information is for the 2021/22 session.

Teacher responsible

Dr Galit Ashkenazi-Golan


This course is available on the MSc in Applicable Mathematics and MSc in Operations Research & Analytics. This course is available with permission as an outside option to students on other programmes where regulations permit.


Students must have completed Algorithms and Computation (MA407) or have taken an equivalent course that provides basic knowledge in the analysis of algorithms. No prior knowledge about Game Theory is required. 

Course content

The last 15-20 years have witnessed a lively interaction between computer science and economics. Many problems central to computer science – from resource allocation problems in large networks to online advertising – fundamentally involve the interaction of multiple self-interested parties. Game theory and mechanism design offer a host of models and definitions to reason about such problems. But the flow of ideas also travels in the opposite direction, as research in computer science has complemented the traditional economics literature in several ways. For example, computer science offers a focus on and a language to discuss computational complexity, has popularised the use of approximation guarantees in situations where exact solutions are unrealistic or unknowable, and proposes several alternatives to Bayesian- or average-case analysis that emphasise robust solutions to economic design problems.

This course gives an overview over the key ideas and developments of this young research field. The focus is on the various new techniques and methods that have been developed, and the new insights that they yield.

Topics covered:

  • Complexity of equilibria: hardness of computing pure Nash equilibria, poly-time algorithm for correlated equilibria
  • Best response dynamics and no-regret learning: existence and speed of convergence
  • Tools for bounding the inefficiency of equilibria: price of anarchy, price of stability, the smoothness framework
  • Algorithmic mechanism design: the VCG mechanism and its computational complexity, characterization of truthful mechanisms and techniques for obtaining truthful poly-time approximation mechanisms
  • Tools for the design and analysis of simple, robust, non-truthful mechanisms
  • Posted price mechanisms and prophet inequalities


This course is delivered through a combination of classes and lectures totaling a minimum of 30 hours across Lent Term. This year, some or all of this teaching will be delivered through a combination of virtual classes and lectures delivered as online videos.

Formative coursework

Students will be expected to produce 10 problem sets in the LT.

Written answers to set problems will be expected on a weekly basis.

Indicative reading

  1. Noam Nisan, Tim Roughgarden, Eva Tardos, Vijaj V. Vazirani. Algorithmic Game Theory. Cambridge University Press. September 2007.
  2. Tim Roughgarden. Twenty Lectures on Algorithmic Game Theory. Cambridge University Press. August 2016.
  3. David C. Parkes and Sven Seuken. Introduction to Economics and Computation: A Design Approach. Cambridge University Press. June 2019.


Exam (100%, duration: 2 hours) in the summer exam period.

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.

Important information in response to COVID-19

Please note that during 2021/22 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the differing needs of students in attendance on campus and those who might be studying online. For example, this may involve changes to the mode of teaching delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.

Key facts

Department: Mathematics

Total students 2020/21: 12

Average class size 2020/21: 12

Controlled access 2020/21: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills