Economics of Data (313-0-20)

Instructors

Annie Liang

Meeting Info

Tech Institute Lecture Room 5: Tues, Thurs 2:00PM - 3:20PM

Overview of class

Predictive algorithms and big data are increasingly being used by firms and policymakers to guide high-stakes decisions, with a range of ethical, social, and policy implications. This course covers theoretical frameworks for thinking through those implications. The course is split into four parts, which model these issues at different scales. The first part of the course starts with the individual decision maker. We cover foundational theories regarding what information is and how it is used in decision problems. The second part of the course considers acquisition of information by a decision-maker and consequences for learning. The third part of the course considers the interaction between an agent and an algorithm. We cover topics regarding strategic data disclosure and manipulation. The final part of the course considers broader social implications of algorithm design, with an emphasis on recent topics regarding fairness in algorithm decision-making. The objective of the course is not to provide any "answers" regarding the questions raised, but rather to equip students with tools and frameworks that they can use to develop their own analyses of emerging social issues related to big data and algorithms.

Enrollment Requirements

Enrollment Requirements: Pre-requisite: Students must have taken ECON 310-1 or MMSS 211-1 and ECON 281 or ECON 381-1 or MATH 386-1 or IEMS 304 or STAT 350 to successfully enroll in this course.

Associated Classes

DIS - Tech Institute Lecture Room 5: Fri 1:00PM - 1:50PM