Incentives in Computer Science
Learn to design software and systems interacting with multiple self-interested participants with case studies on BitTorrent, Google, NYSE, eBay, and Bitcoin.
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Weekly Effort
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Course Description
- Explore 21st-century computer science applications such as real-time online advertising auctions, blockchain protocols, and more, focusing on the interaction with multiple self-interested participants.
- Learn the vocabulary and modeling tools required to reason about complex design problems involving various modern technological systems.
- Analyze case studies on key topics like the BitTorrent protocol for peer-to-peer file distribution, Google's sponsored search auctions, high-frequency trading on the NYSE, and eBay's reputation system.
- Understand the underlying principles of the Bitcoin protocol and how it fits within the broader context of modern computer science applications.
What You Will Learn
By the end of this course, learners will be able to:
Acquire the vocabulary and modeling tools necessary to reason effectively about various design problems in modern technology.
Delve into detailed case studies covering subjects such as the BitTorrent protocol for peer-to-peer file distribution, Google's sponsored search auctions, high-frequency trading on the NYSE, and eBay's reputation system.
Gain valuable insights into the intricacies of these systems by exploring real-world examples, enhancing their understanding of how different technologies interact and function.
Develop the skills needed to analyze and tackle similar design challenges in the future, equipping themselves with practical knowledge to address 21st-century computer science applications.
Course Outline
Module 1: Markets, everywhere
Module 2: The prisoner's dilemma
Module 3: Asymmetric information
Module 4: Auctions
Module 5: Participatory budgeting
Module 6: Bitcoin
Instructors
Tim Roughgarden is a Professor in the Computer Science Department at Columbia University. Prior to joining Columbia, he spent 15 years on the computer science faculty at Stanford, following a Ph.D. at Cornell and a postdoc at UC Berkeley.
He works on the boundary of computer science and economics, and on the design, analysis, applications, and limitations of algorithms.
Please note that there are no instructors or course assistants actively monitoring this course.