Academics / Courses / DescriptionsCOMP_SCI 496: Data Economics
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Prerequisites
PhD in CS, or PhD Econ, or PhD IEMS or PhD CS+LS or PhD TSB or Kellogg PhD MECS or Permission of Instructor.Description
As data transforms science and society, understanding the economics of data is of utmost importance. Collecting data is costly and must be correctly incentivized. Possessing data gives market power which can be leveraged through sharing policies. In turn, sharing data has fairness and privacy implications. The value of data for decision problems depends on its quality. And using data to shape strategies can impact outcomes in unexpected ways, requiring careful market design to mitigate bad outcomes. The topics of the course will be drawn from recent and classic literature pertaining to data economics, including data elicitation, information design, differential privacy, fairness, calibration, social learning, and learning in games.
- Coursework: Bi-weekly problem sets. Final exam.
- This course fulfills the Technical Elective area.
COURSE COORDINATORS: Prof. Jason Hartline
COURSE INSTRUCTOR: Prof. Hartline