Economics, AI, and Optimization

Course Info

  • Instructor: Christian Kroer
  • Time: Mondays & Wednesdays 1:10-2:25pm
  • Location: 233 Mudd
  • Office hours: Wednesday 2:25-3:30pm (or anytime; but email me first in that case)

Course Summary

Economics, AI, and Optimization is an interdisciplinary course that will cover selected topics at the intersection of economics, operations research, and computer science. A recurring theme in the course will be how economic solution concepts are enabled at scale via AI and optimization methods. We will describe several successful practical applications, including how to:

  • Make a poker AI
  • Fairly allocate course seats to students, food to food banks, etc
  • Protect wildlife or airports
  • Conduct large-scale auctions for spectrum or Internet ads

Course Structure

The course will be lecture-based, with Christian Kroer giving the lectures. At the end of the course there will be a few lectures of project presentations by students.

Readings will consist of a mixture of textbooks and course notes, which will be uploaded after lectures.

Students will complete a project, which may be done individually or in groups of 2-3 students.

Grading will be as follows:

  • 50% final project write-up
  • 20% homework (there will only be 1-2 homeworks)
  • 15% Final project presentation
  • 10% Participation
  • 5% Project proposal

Outline

A rough outline is as follows:

  • Intro to game theory and market design
  • Nash equilibrium
    • Zero-sum games, minimax theorem
    • First-order methods/Online convex optimization/regret minimization in games
    • Deep learning for solving games at scale
    • How the above is used in making superhuman poker AIs
  • Security games
    • Stackelberg equilibrium
    • Basic Stackelberg security game model
    • Mixed-integer programming, deep learning for scaling up
    • Applications to airport, wildlife, power grid security
  • Market design
    • Fisher markets and market equilibrium
    • Optimization methods for computing market equilibria
    • Machine learning methods for large markets
    • fair division, course allocation
    • Internet ad auctions
    • Spectrum auctions

We will also cover some subset of the following:

  • Matching markets
  • Data science in multiagent systems

Textbooks

The primary book is:

Additionally, we may use some sections of the following books. They are also recommended for supplementary reading:

Schedule

#
Date
Topic Reading Lecture notes
1 1 / 22 Introduction AGT Ch 1 Lecture note 1.pdf
2 1 / 27 Introduction to game theory AGT Ch 1, Hazan Ch 1 Lecture note 2.pdf
3 1 / 29 Hedge, Online convex optimization Hazan Ch 1, Ch 5.0-5.4 Lecture note 3.pdf
4 2 / 3 Online Mirror Descent Orabona Ch. 6.0-6.4 See previous note
5 2 / 5 OMD convergence, Minimax theorem Orabona Ch. 6.0-6.4 See previous note
6 2 / 10 Blackwell approachability, regret matching Farina blog Lecture note 4.pdf
7 2 / 12 From Regret to Nash Lecture note 5.pdf
8 2 / 17 Extensive-Form Games, DGFs AGT Ch 3.7 - 3.11 Lecture note 6.pdf
9 2 / 19 Counterfactual Regret Minimization See previous note
10 2 / 24 Subgame solving, deep learning BS18 Sections 1 and 2 (preferably whole paper), BSA18 Section 2 (preferably whole paper), DeepStack sections “DeepStack” and “Deep Counterfactual Value Networks” Lecture note 7.pdf
11 2 / 26 Stackelberg Equilibrium, Security Games Lecture note 8.pdf
12 3 / 2 Stackelberg wrap-up, Intro to Fair Division HCSC Ch 11 Lecture note 9.pdf
13 3 / 4 Eisenberg-Gale convex program AGT Ch 5 & 6 See previous note
14 3 / 11 Dominant-Resource Fairness Lecture note 10.pdf
15 3 / 30 Intro to Auctions AGT Ch 9 & 10 Lecture note 11.pdf
16 4 / 1 Auctions with Budgets - Second Price Multiplicative Pacing Equilibria in Auction Markets Lecture note 12.pdf
17 4 / 6 Auctions with Budgets - First Price Pacing Equilibrium in First-Price Auction Markets Lecture note 12.pdf
18 4 / 8 Auctions with Budgets - Dynamics Learning in Repeated Auctions Lecture note 13.pdf
19 4 / 13 Large-Scale Market equilibrium Lecture note 14
20 4 / 15 Large-Scale Market equilibrium See previous note
21 4 / 20 Large-Scale Market equilibrium See previous note
22 4 / 22 Indivisible Goods - Fair Division For trying fair division: http://www.spliddit.org/ Lecture note 15
23 4 / 27 Indivisible Goods - Fair Division Algorithms See previous note
24 4 / 29 Indivisible Goods - A-CEEI See previous note
25 5 / 4 Indivisible Goods - A-CEEI See previous note

Below is a list of related courses at other schools.

Professor Title Year School
John P. Dickerson Applied Mechanism Design for Social Good 2018 UMD
Fei Fang Artificial Intelligence Methods for Social Good 2018 CMU
Yiling Chen Topics at the Interface between Computer Science and Economics 2016 Harvard
Vincent Conitzer Computational Microeconomics: Game Theory, Social Choice, and Mechanism Design 2016 Duke
Sanmay Das Multi-Agent Systems 2016 Wash U
Ariel Procaccia Truth, Justice, and Algorithms 2016 CMU
Milind Tambe Security and Game Theory 2016 USC
Constantinos Daskalakis Games, Decision, and Computation 2015 MIT
Tuomas Sandholm Foundations of Electronic Marketplaces 2015 CMU
Tim Roughgarden Algorithmic Game Theory 2013 Stanford
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Christian Kroer
Assistant Professor