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CS929-15 Algorithmic Game Theory
The focus of the module is on algorithmic and computational complexity aspects of game-theoretic models.
To familiarise students with formal methods of strategic interaction, as studied in game theory. One of the aims will be to give a flavour of current research and most recent advances in the field of algorithmic game theory.
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Game models: Strategic form, extensive form, games of incomplete information (eg auctions), succinct representations, market equilibria, network games, co-operative games;
Solution concepts: Nash equilibria, subgame perfection, correlated equilibria, Bayesian equilibria, core and Shapley value;
Quality of equilibria: Price of anarchy, price of stability, fairness;
Finding equilibria: Linear programming algorithms, Lemke-Howson algorithm, finding all equilibria;
Complexity results: Efficient algorithms, NP-completeness of decision problems relating to set of equilibria, PPAD-completeness;
Some parts of the module will be research-led, so some topics will vary from year to year.
By the end of the module, students should be able to:
- Understand a variety of advanced algorithmic techniques and complexity results for computing game-theoretic solution concepts (equilibria).
- Apply solution concepts, algorithms, and complexity results to unseen games that are variants of known examples.
- Understand the state of the art in some areas of algorithmic research, including new developments and open problems.
- Understand the fundamental concepts of non-cooperative and co-operative game theory, in particular standard game models and solution concepts.
Indicative reading list
Osborne and Rubinstein, A Course in Game Theory;
Roughgarden, Selfish Routing and the Price of Anarchy;
Nisan, Roughgarden, Tardos and Vazirani (eds), Algorithmic Game Theory;
Selected research papers.
Subject specific skills
Advanced algorithmic techniques;
|Lectures||30 sessions of 1 hour (20%)|
|Seminars||9 sessions of 1 hour (6%)|
|Private study||111 hours (74%)|
Private study description
private reading and revision
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Students can register for this module without taking any assessment.
Assessment group D1
question sheet 1 - peer assessed.
~Platforms - AEP
Assessment group R1
|On-campus Examination - Resit||100%|
CS929 resit paper
~Platforms - AEP
Feedback on assessment
Written comments and marks.
Students must have studied the material in CS260 or equivalent relevant content.
This module is Optional for:
- Year 1 of TCSA-G5PD Postgraduate Taught Computer Science
- Year 1 of TCSA-G5PA Postgraduate Taught Data Analytics
- Year 1 of TMAA-G1PF Postgraduate Taught Mathematics of Systems