G_Theory / Fall 2016 - Game Theory
- This course is an introduction to game theory. Game theory studies interactions of "agents" whose objectives depend on others actions and not only theirs. It permits to model and understand many real-world strategic interactions, e.g., in economics.
- This course introduces the main concepts of game theory (Nash equilibrium, etc.) and illustrates them with examples from economics, political sciences, computer science, engineering, etc.
- The goal of this course is to present the basics of game theory in sufficient details to enable students to (i) feel the relevance of game theory to understand real world interactions and (ii) apply game theory to their own applications.
- This course is followed by the Network Economics course which introduces more advanced game theory concepts and develops in details applications to economics of the Internet.
NetEcon / Fall 2016 - Network Economics
Economics and incentives consideration govern in large part the development and actual performance of networks and digital services. The objective of this course is to raise awareness of students on these questions and how to solve them. More specifically:
- The course will introduce a number of topics in economic analysis of networks and network-related services (web, security, etc.).
- The basic method used will be game theory. The basics of game theory will be assumed to be known and the course will focus on applications to network economics.
- The main goal is to show how mathematical methods based on game theory are used to analyze economics problems in networks, with a focus on modern research topics in network economics.
- To complement the theoretical notions presented, the course will present practical (software) aspects of implementing new pricing models using as an example the software solution SAP Billing and Revenue Innovation Management (BRIM), thanks to the participation of an external expert from SAP.
Stat / Fall 2016 - Statistical data analysis
- The goal of the course is to provide students with simple and efficient statistical methods to analyze data. Such methods are of crucial importance in many situations as they allow to answer questions such as: 'Is this performance improvement significant?', 'What is the uncertainty on that result?', 'How can I predict a new output of my system based on measurements?', 'Which factors have a significant impact on the performance of my system?', and many more.
- Mathematical analysis underlying the presented methods will be sketched, but the main focus will be on the understanding of the methods and the situations in which they can be used (which method to use, what to expect, etc.).
- The course will present generic methods working for data from any application and not a specific domain of application. Examples will be given in different areas (computer networks, engineering, etc.).