CE322-6-AU-CO:
Algorithmic Game Theory

The details
2019/20
Computer Science and Electronic Engineering (School of)
Colchester Campus
Autumn
Undergraduate: Level 6
Current
Thursday 03 October 2019
Saturday 14 December 2019
15
04 October 2018

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

(none)

Module description

This module prepares students to understand and design the kinds of systems that are coming to define modern life, such as Amazon, Uber, eBay, etc. These companies need analysts who can decide which objectives to maximize, what information and choices to offer, what rules to set, and so on. These questions require a broad understanding of topics at the interface of theoretical computer science and economics. This module will take a computational and algorithmic approach to designing and analyzing such systems. Students will explore the interaction between self-interested agents and strategic scenarios through the lens of Algorithmic Game Theory and Mechanism Design.

Module aims

Algorithmic game theory lies at the exciting intersection of CS and Economics and is an area of expertise that is in great demand within the UK Finance Industry, forming one of the primary recruiting sectors for graduates from Computer Science. The aim of this module is to introduce this subject to students using research-led teaching that uses expertise from staff within the Centre for Computational Finance and Economic Agents (CCFEA) at the University of Essex. This model also provides an excellent opportunity for students to explore continuing their studies in a postgraduate CCFEA degree programme.

Module learning outcomes

By the end of this module, students will:

1. Be able to model various situations as strategic games
2. Understand and use computational and algorithmic aspects of game theory
3. Appreciate incentive structures used in certain situations
4. Carry out a model of a realistic scenario, perform evaluations and draw conclusions from the model evaluation.

Module information

Syllabus

Game Theory Basics:
* Mixed Strategies, Expected Payoffs, and Nash Equilibrium.
* 2-Player Zero-Sum Games, and The Minimax Theorem, Introduction to Linear
Programming
* Selfish Network Routing, Congestion Games, and the Price of Anarchy

Auctions and Mechanism Design Basics;
* Auctions as games, Bayesian games, and Vickrey auctions
* Matching Markets, unit-demand auctions, and VCG
* Revenue maximizing auctions, and Simple auctions
* Combinatorial auctions
* Online auctions, sponsored search auctions
* Mechanism design without money

Algorithms and complexity theory basics
* learning and computing Nash and market equilibria
* NP-Completeness, PLS-Completeness, PPAD-Completeness

Current research challenges in algorithmic game theory.

Learning and teaching methods

No information available.

Bibliography

This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework Lab Script Solutions 15%
Coursework Modelling Report 13/12/2019 100%

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Maria Kyropoulou

 

Availability
No
No
No

External examiner

No external examiner information available for this module.
Resources
Available via Moodle
Of 50 hours, 34 (68%) hours available to students:
16 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information

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