6.853  Topics in Algorithmic Game Theory: Algorithmic Game Theory and Data Science

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6.853  Topics in Algorithmic Game Theory: Algorithmic Game Theory and Data Science

Spring 2019

Instructors: Konstantinos Daskalakis, Vasilis Syrgkanis

TAs: Govind Lalgudi Ramnarayan, Devendra Anil Shelar

Lecture:  TR2.30-4  (54-100)
Office hours:  T 4:30-6  (26-210)      

Information: 

How do you setup an auction to optimize revenue? How do you price cloud resources to optimize efficiency? More broadly, how do you solve an optimization problem when your inputs are supplied by strategic agents with a keen interest on your solution, and who may therefore misreport their inputs to manipulate the result? How do you inform all these answers when you have access to data?

This class, situated at the intersection of Algorithms, Game Theory and Machine Learning, will present an analytical and computational framework to approach optimization and inference problems of this form. In doing so, we will also look at the foundations of Game Theory, Learning Theory and Econometrics.

Prerequisites:

Intro to algorithms or Intro to optimization(6.006/6.046 or equivalent), intro to probability (6.042/6.041 or equivalent)

Piazza link: https://piazza.com/class/jrr81wuvjxg2hv

Course webpage:: http://vsyrgkanis.com/6853sp19/

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