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Learning goal is to be able to recognize and explain network phenomena. Social networks such as Facebook, information networks such as the Web, and institutions such as voting are all IT-enabled. The student will learn:
- how to recognize and explain structural and dynamic phenomena in these networks, such as cascading behavior and power laws, and
- how to model and analyze using graph theory and game theory.
After following this module, the student is able to:
- Recognize these phenomena in practice;
- Apply mathematical models from graph theory, probability, and game theory to describe and analyze them;
- Explain and predict network phenomena in terms of network structure and behavior;
- Operationalize and apply these models to existing network data.
In this study unit of the module Web Science we focus on the topics: game theory and network traffic, auctions and matching markets, and institutions and aggregate behaviour.
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The study unit Games, Auctions and Voting of the module Web Science covers the following topics:
- Game Theory and Network Traffic
We study basic game theoretic models and concepts, such as modeling strategic behaviour in normal or extensive form games, best responses, pure and mixed strategies, Nash and dominant strategy equilibria. These concepts are put to work in a network context, modeling network traffic in terms of normal form games. We specifically analyze best response dynamics, user equilibria in networks, and the effects of lack of central coordination on the social cost, also known as the price of anarchy. Students will be able to understand, model, and formally analyze the effects of strategic behaviour in general, and in the context of network traffic in particular.
- Auctions and Matching Markets
Our goal is to understand how business models on the web, such as Google’s ad auctions, actually work. In order to understand that, we study the basics of auction theory, including in particular first and second price auctions, and the role of game theory in order to understand strategic behaviour in such contexts. As a second step, we study matching markets, the computation of market clearing prices, generalized second price auctions, and the celebrated VCG mechanism for sponsored search markets. Students will thereby learn to model and understand the rationale behind various types of auctions and mechanisms, as a basis for understanding and designing business models for the web.
- Institutions and aggregate behaviour
Our goal is to understand institutions such as voting systems and markets where rules and characteristics and expectations of actors affect their behaviour and consequently determine aggregate behaviour of the set of actors as a whole. In particular, we study voting systems such as elections or televised talent shows, markets with asymmetric information or reputation systems, as well as prediction markets such as horse races or stock trading. Students will learn to model and understand the rationale and design behind various types of institutions in terms of the aggregate behaviour they produce.
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| Bachelor Technical Computer Science |
| | Required materialsBook| David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World. ISBN: 978-0-521-19533-1.
The book can be downloaded from http://www.cs.cornell.edu/home/kleinber/networks-book/. |
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Recommended materials-Instructional modesLecture
| Project unsupervised
| Q&A
| Self study without assistance
| Tutorial
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TestsTheoretical Knowledge
| Practical Knowledge and Skills
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