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The University of Southampton

COMP6207 Algorithmic Game Theory

Module Overview

This module: - Introduces the students to the key issues of interaction of multiple self-interested parties (a.k.a. agents) and gives a broad survey of topics at the interface of theoretical computer science and game theory dealing with such interactions. - Provides the theoretical background and practical tools to solve problems arising in settings with self-interested participants, to predict possible behaviour and outcomes, and finally, to design multi-agent systems that would incentivise desirable behaviour. - Introduces the students to the specifics of computational game-theoretic techniques in different application areas, ranging from multi-agent systems, electronic marketplaces and networked computer systems to computational biology and social networks. - Extends and advances the knowledge obtained in other AI modules (in particular, COMP6203 Intelligent Agents).

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The principles of game theory and mechanism design (with and without money)
  • Social choice theory
  • Some applications of game theory and mechanism design
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • As an agent in a setting with self-interested participants: to reason about the opponents’ behaviour and make strategic decisions
  • As a system administrator: to analyse participants’ behaviours and predict likely outcomes
  • As a system designer: to design a system such that participants’ strategic behaviour lead to a desirable outcome
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Identify and compute various game theoretic solution concepts


Basic game theoretic concepts Social choice Mechanism design without money Mechanism design with money Applications

Learning and Teaching

Teaching and learning methods

Teaching methods include: - Directed reading and preperation: including, but not limited to, lecture notes, selected research papers and book chapters, as part of necessary preparation for the lectures. - Lectures: Three hours per week during the teaching weeks, supported by in-class quizzes and class discussion. - Tutorials: One hour per week during the teaching weeks, focusing on the application of the practical aspects of computational game-theoretic techniques and problem solving. - Assessment: There are a few Assignments. Learning activities include: - Work in groups: Reading and discussion groups with a plenary feedback during the tutorials. Participation, while not compulsory, is encouraged. - Individual study: Certain amount of (directed) reading is expected during your private study time. It is strongly recommended that you read around the subject areas covered in the lectures. Refer to the list of background texts and ask the lecturers if you require further resources. - Exercise: Applying theoretical knowledge and practical tools obtained in the lectures to solve example problems provided in the recommended literature and home take assignments.

Preparation for scheduled sessions18
Follow-up work18
Wider reading or practice45
Completion of assessment task11
Total study time150

Resources & Reading list

Y. Shoham, K. Leyton-Brown (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. 


Assessment Strategy

Feedback and student support during module study: - Assignments will be marked and feedback given during the tutorial sessions. - Reading and discussion groups with a plenary feedback during the tutorials. Relationship between the teaching, learning and assessment methods and the planned learning outcomes: - The knowledge, understanding and intellectual skills listed will be taught in lectures. In completing the assignments and examination you will demonstrate your mastery of all the skills listed. - The purpose of the tutorials is for you to master the skills and provide feedback on your understanding of topics not covered by, or are difficult to fully assess in, an assignment.


MethodPercentage contribution
Continuous Assessment 25%
Final Assessment  75%


MethodPercentage contribution
Set Task 100%


MethodPercentage contribution
Set Task 100%

Repeat Information

Repeat type: Internal & External

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