COMPGW02 - Web Economics
This database contains 2016-17 versions of the syllabuses. For current versions please see here.
Note: Whilst every effort is made to keep the syllabus and assessment records correct, the precise details must be checked with the lecturer(s).
|Code||COMPGW02 (Also taught as: COMPM041)|
|Prerequisites||Normally offered only to students in computer science related programmes because basic programming skills are required. Basic understanding of probability and statistics and proficient in java programming, as demonstrated by a least one programing project in the past is required.|
|Taught By||Emine Yilmaz (50%) Jun Wang (50%)|
|Aims||The course is intended to provide an introduction of the computing systems and their economics for the production, distribution, and consumption of (digital) goods and services over the Internet and web. While the basic economic principles are covered to understand the business aspects of web-based services, the course is primarily focused on the computational and statistical methods for implementing, improving and optimizing the internet-based businesses, including algorithmic mechanism design, online auctions, user behavior targeting, dynamic pricing, crowdsourcing and social media mining. Practical applications such as Google’s online advertising, Ebay’s online auction, and Amazon’s cloud computing will also be covered and discussed.|
|Learning Outcomes||The students are expected to master both the theoretical and practical aspects of web economics. More specifically, the student will: |
- Incentives: Game theory, and Auction theory
- Business Models in the Internet:
- auction and bidding (the Ebay Model, swoopo, and b2c and b2b auctions (alibaba)
- Subscription (Compulsory license, dropbox premier model, spotify, apple icloud, pay per use).
- Online retailing (Amazon, Apple Apps).
- Computational advertising
- Vickrey auction and the second price auction
- Search-based advertising, Contextual advertising and Behaviour targeting, Demand-side platform and Real-time bidding, Ad exchange and futures and options
- Computing as a service/utility
- Social media mining
Management and optimization
- Dynamical pricing models (air-tickets) and Yield management and scheduling (online advertising)
- Attention economics and Personalization and Long tail
- Prediction markets and their accuracy
- Topic modelling
- Human computing and Social computing systems
- Crowdsourcing and Amazon Mechanical Turk (MTurk) and Collective intelligence
- System design (ESP game, reCAPTCHA etc)
Method of Delivery
Lectures. We will also have guest lecturers from the relevant industries in order to get an understanding of the real world applications of the material covered. A website or/and moodle webpage will be created for the course and the course materials such as lecture notes, sample codes, will be shared. By using moodle, students will also be able to discuss relevant ideas and have questions answered by the lecturer.
The module has the following assessment components:
- Written examination (2.5 hours, 70%)
- Coursework (30%)
To pass this module, students must:
- Obtain an overall pass mark of 50% for all components combined.
Noam Nisan (Editor), Tim Roughgarden (Editor), Eva Tardos (Editor), Vijay V. Vazirani (Editor), Algorithmic Game Theory, Cambridge University, 2007.
David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010
R. Preston McAfee, Introduction to Economic Analysis www.mcafee.cc/Introecon/IEA.pdf
Nir Vulkan, The Economics of e-Commerce, Princeton University Press, 2003
Carl Shapiro, Hal R. Varian, Information rules: a strategic guide to the network economy, 1999