COMPSCI 323 (and 590.08, and ECON 336): Computational Microeconomics, Spring 2022

LSRC B101 (or, except for exams, zoom; zoom link and other materials available on Ed Discussion, please let me know if you do not have access to Ed Discussion).
MW 10:15-11:30am, and recitations F 10:15-11:30am (all times US Eastern). We may occasionally use the recitation slot for lectures due to scheduling constraints.
First class: Wednesday, January 5, 2022 (zoom only for as long as the university requires).
Instructor: Vincent Conitzer. (Please call me Vince.)
TAs: Chad Kalil, Alex Whitefield (graduate); Albert Sun, David (Ruoyu) Wu, Joe (Yuncong) Zuo (undergraduate).

COVID plans: the situation continues to change rapidly so we may have to adapt; the plan for now is for this course to be taught in person. Unless conditions significantly change for the better, we plan to be strict about wearing well-fitting high-quality masks over nose and mouth in class -- please let us know if you need help obtaining a good mask. We will also seek to enable and not penalize remote participation (though synchronous participation is still encouraged), except the plan for now is to conduct exams in the classroom only (no remote option). Please be ready to bring devices to class to participate in activities such as polls.

In recent years, there has been a surge of interaction between computer scientists and economists. This interaction is driven both by necessity and opportunity. On the one hand, as computer systems become more interconnected, multiple parties must interact in the same environment and compete for scarce resources, which necessarily introduces economic phenomena. On the other hand, in the past, economic mechanisms (such as auctions and exchanges) have been designed to require very limited computing and communication resources, as they would otherwise be impractical. These days, computation and communication pose much less of a constraint, which presents an opportunity to create highly efficient, computationally intensive mechanisms.

In the first part of the course, we will study the design of expressive marketplaces. In such marketplaces, participant can express nontrivial valuations over outcomes: for example, a participant may express that a complete travel package to Las Vegas including a flight, hotel reservation, and entertainment is worth $700 to her, but any incomplete package is worth $0. This can greatly increase market efficiency, but clearing the market (deciding on the final outcome) becomes computationally hard. We will cover techniques for solving these problems.

In the second part of the course, we will study game theory. Game theory studies how to act optimally in strategic settings where each party's utility (happiness) depends on the actions of other parties. We will cover such definitions of optimality as well as techniques for computing optimal actions. We will study applications including bidding in auctions, building computer poker players, and security.

In the third part of the course, we will draw on the first two parts and study how to design market mechanisms that are optimal when we take into account that agents will behave strategically (game-theoretically). Again, we will cover techniques for computing the optimal mechanisms.

Students should be comfortable with probability. Background in computer science and/or economics will be helpful but neither is required; the goal is to bring together students from different backgrounds. The formal requirement is at least one of the following: Computer Science 230, 200-level Mathematics course, or 200-level Statistical Science course. But you should know or be able to quickly pick up some basic probability. Generally, the course will probably not be enjoyable for students who dislike mathematics.

We will use parts of a book by Shoham and Leyton-Brown (SLB), Multiagent Systems. A free electronic copy is available at that link though the printed version is very reasonably priced as well.
There will be additional readings for individual classes. The slides for the course are also part of the reading.

Grading (tentative and subject to change)
Participation: 5%
Programming and written assignments: 35%
Midterm: 20%
Final: 40%

Rules for assignments: You may discuss homework assignments with at most one other person. However, you may not simply copy down the other person's solution (or any part thereof). Each person should do her/his own writeup, at which point you should derive the solution yourself. This also implies that you cannot copy any code (linear programs etc.) from each other. Copying code is considered a serious form of cheating, and there are ways of detecting copied code. If you have trouble with the programming assignments, just ask for help. On your writeup, you must acknowledge your partner (if any) and any other sources you used; if you worked on your own and used no other sources, state this explicitly.
We will not plan the course down to the individual lecture. Dates will be added as the course progresses. Topics are given below (a topic need not take exactly one lecture to complete and we may not cover all topics).

Date Topic Materials
1/5,1/7 Course at a glance. Slides: ppt, pdf.
Homework 0 out.
Optional: CACM overview article.
Part 0: Basic techniques from computer science.
1/7 - 1/19 Linear programming. (Mixed) integer linear programming. Slides: ppt, pdf.
Example files: painting.lp, knapsack.lp, knapsack_simple.mod, knapsack.mod, cell.lp, cell.mod, hotdog.mod, sudoku.mod.
SLB Appendices A, B.
Programming assignment 1 out.
Guide to the modeling language. Here are also lecture notes I wrote those for a course on linear and integer programming; if you want to learn more about these topics there may be some useful resources on that course's website.
1/14 (bonus lecture) Computational problems. Algorithms. Runtime of algorithms. Easy and hard problems. Slides: ppt, pdf.
Sorting algorithms spreadsheet.
Example files: set_cover.mod, set_cover2.mod, matching.mod.
Optional: CACM article on P vs. NP.
Part 1: Expressive marketplaces.
1/24-2/2 Single-item auctions. Combinatorial auctions. Bidding languages. Winner determination problem. Variants (reverse auctions, exchanges). Slides: ppt, pdf.
Note: we are not going in the same order as the book on these topics. The book does mechanism design before getting into auctions. I'm pointing out the chapters that are associated with each topic, but for reading purposes you may prefer following the order of the book for the next few lectures, reading mechanism design (Ch. 10) before auctions (Ch. 11), and single-item auctions and their analysis before combinatorial auctions.
SLB 11.3.1-11.3.4, 11.4.1.
Optional: 11.2, 11.3.5, Conitzer chapter on auctions, Lehmann et al. chapter on winner determination, Sandholm chapter on optimal winner determination.
2/2-2/9 Expressive financial securities. Slides: ppt, pdf.
SLB 10.4.2.
Programming assignment 2 out. Partial solution to graph winner determination problem, for first problem.
Optional: Paper 1, paper 2, paper 3.
Article about Predictalot.
2/9-2/16 Barter exchanges/matching markets. Kidney exchange. Slides: ppt, pdf.
Paper (you do not need to understand all the details about constraint and column generation).
2/16-2/28 Voting and social choice. Homework 3 out.
Slides: ppt, pdf.
SLB Chapter 9 (9.5 is optional).
Optional (if you really like this): chapter on computational social choice.
A website aiming to do liquid democracy (presented without endorsement; seems legitimate as far as I can tell but I don't know the people involved, use your own judgment if you consider signing up). (Actually, seems to have changed to just a demo?)
Part 2: Game theory.
2/28-3/2; Sleeping Beauty on 3/14 Risk neutrality and risk aversion. Expected utility theory. Slides: ppt, pdf.
SLB Section 3.1.
Sleeping Beauty slides: ppt, pdf.
3/2 - 3/30 Games in normal form. Dominance and iterated dominance. Computing dominated strategies. Minimax strategies. Computing minimax strategies. Nash equilibrium. Computing Nash equilibria. Homework 4 out.
Slides: ppt, pdf.
SLB 3.2, 3.4.3, 4.5; 3.3.1-3.3.3, 3.4.1, 4.1, 4.2.1, 4.2.3, 4.2.4, 4.4.
Optional (including the papers): 3.3.4, 4.2.2; 3.4.5, 4.6. Paper on computing dominated strategies. (You can skip the part on Bayesian games.) Paper on computing Nash equilibria. (You only need to read the part concerning 2-player games.) Paper on computing special kinds of Nash equilibria. (You can skip everything from Bayesian games on.)
3/16 Midterm review. Practice midterm.
More practice questions: ppt, pdf.
3/21 Midterm.
3/30-4/4 Games in extensive form. Backwards induction. Subgame perfect equilibrium. Imperfect information. Equilibrium refinements. Computing equilibria. Poker. Slides: ppt, pdf.
SLB 5.1 (alpha-beta is optional), 5.2.1, 5.2.2.
Optional (including the paper): 5.2.3. Paper on finding optimal strategies to commit to.
Part 3: Mechanism design.
4/6- Bayesian games. Auctions revisited. Slides: ppt, pdf.
SLB 6.3, 11.1.1-11.1.8.
Optional: 11.1.9, 11.1.10.
Incentive compatibility. Individual rationality. Revelation principle. Clarke mechanism. Groves mechanisms. Slides: ppt, pdf.
SLB 10.1-10.4.
Optional: rest of chapter 10.
Article on the Swoopo auction.
4/18, 4/20 Final review. Practice final 1.
Practice final 2.