Market design at Iowa State: course invites remote access

 Bertan Turhan writes to tell me about the market design course he is offering this semester, to which he invites remote participation (for access you can email him at bertan@iastate.edu ).

He writes:

"I wanted to inform you about a second-year PhD course I am teaching this semester with great guest lecturers. It is in person and taken by both Econ, OR, and CS students at ISU, but I also live broadcast it so that others can benefit. ... The course is mainly based on the Online and Matching-Based Market Design (Echenique, Immorlica, and Vazirani). "

Here is the syllabus and other readings.

Topics in Market Design: Econ 287/365: Winter quarter, Itai Ashlagi

Itai Ashlagi will be teaching Econ 287 this quarter, on topics in market design.  It's highly recommended.

He writes that the syllabus below is very tentative, and will depend in part on how many of the enrolled students took Econ 285 (Ostrovsky and Roth) in the Fall (back in 2023:-)

Topics in Market Design 2024, Itai Ashlagi

Market design is a field that links the rules of the of the marketplace to understand frictions, externalities and more generally economic outcomes. The course will provide theoretical foundations on assignment and matching mechanisms as well as mechanism design. There will be emphasis on theories at the intersection of economics, CS and operations as well as applications that arise in labor markets, organ allocation, platforms.

The class will further expose students to timely market design challenges and will we will host a few guest lectures. The class offers an opportunity to begin a research project. Students will reading critique papers, present papers and write a final paper.

Lectures: Monday 10:30am-1:20pm Shriram 052

Course requirements: (i) reading and writing critiques about papers, (ii), presenting papers in class, and (iii) a term paper.

Instructor: Itai Ashlagi. iashlagi@stanford.edu

Some potential papers for presenting:

Equity and Efficiency in Dynamic Matching: Extreme Waitlist Policies, Nikzad and Strack.

Eliminating Waste in Cadaveric Organ Allocation, Shi and Yin

Pick-an-object mechanisms, Bo and Hakimov

Monopoly without a monopolist, Huberman, Leshno and Moallemi

The College Portfolio Problem, Ali and Shorrer

Equal Pay for Similar Work, Passaro, Kojima, and Pakzad-Hurson

Auctions with Withdrawal Rights: A Foundation for Uniform Price, Haberman and Jagadessan.

Optimal matchmaking strategy in two-sided marketplaces, Shi

Practical algorithms and experimentally validated incentives for equilibrium-based fair division (ACEEI),

Budish, Gao, Othman, Rubinstein

Congestion pricing, carpooling, and commuter welfare, Ostrovsky and Schwarz

Artificial intelligence and auction design, Banchio and Skrzypacz

Selling to a no-regret buyer, Braverman et al.

Dynamic matching in overloaded waiting lists, Leshno

The regulation of queue size by levying tolls, Naor

Optimal search for the best alternative, Weitzman

Whether or not to open Pandora’s box, Doval

Descending price optimally coordinates search, Kleinberg, Waggoner, Weyl

Market Failure in Kidney Exchange? Nikhil Agarwal, Itai Ashlagi, Eduardo Azevedo, Clayton Featherstone and Omer Karaduman

Choice Screen Auctions, Michael Ostrovsky

Incentive Compatibility of Large Centralized Matching Markets, Lee

Tentative schedule:

Week 1: Two-sided matching, stability and large markets.

Week 2: One-sided matching, duality, optimization and constraints.

Week 3: Multi-item auctions, auction design, revenue equivalence, optimal auctions, interdependent

valuations.

Week 4: Congestion, dynamic matching.

Week 5: Waitlists, search and learning.

Week 6: Foundations of mechanism design.

Week 7: Robustness in implementation

Weeks 8-10: Projects

We will host several guest lectures. Presentations of papers will take place throughout the course.

Background references

1. List of (mostly applied) papers are given in a separate document.

2. Books

Roth, Alvin E.and Marilda A. Oliveira Sotomayor, Two-sided matching: A study in game-theoretic modeling and analysis. No. 18. Cambridge University Press, 1992.

Vijay Krishna, Auction Theory, 2010.

Tilman Borgers, An Introduction to Mechanism Design by Tilman Borgers.

Milgrom, Paul, Putting Auction Theory to Work, 2004.

3. Papers

(a) Introduction

Roth, Alvin E. The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics. Econometrica, 70(4), 2002. 1341-1378.

Klemperer, Paul, What Really Matters in Auction Design?, Journal of Economic Perspectives, 16(1): 169-189, 2002.

Weitzman, Martin, Is the Price System or Rationing More Effective in Getting a Commodity to Those Who Need it Most?, The Bell Journal of Economics, 8, 517-524, 1977.

(b) Stable matching and assignment

Gale, David and Lloyd Shapley, College Admissions and the Stability of Marriage, American Mathematical Monthly, 69: 9-15,1962.

Roth and Sotomayor, Chapters 2-5.

Hylland, Aanund, and Richard Zeckhauser. The efficient allocation of individuals to positions, The Journal of Political Economy, 293-314,1979.

Roth, Alvin E., The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory. Journal of Political Economy, 92: 991-1016, 1984.

Kojimam, Fuhito and Parag A. Pathak. Incentives and stability in large two-sided matching markets. American Economic Review, 99:608-627, 2009

Abdulkadiroglu, Atila and Tayfun Sonmez. School choice: A mechanism design approach. American Economic Review, 93:729-747, 2003.

Abdulkadiroglu, Atila , Parag A. Pathak, and Alvin E. Roth. The New York City high school match. American Economic Review, 95:364-367, 2005.

Ashlagi, Itai, Yash Kanoria, and Jacob D. Leshno. Unbalanced random matching markets: The stark effect of competition, Journal of Political Economy,

Ashlagi, Itai and Peng Shi. Optimal allocation without money: An engineering approach. Management Science, 2015.

Peng Shi and Nick Arnosti. Design of Lotteries and Waitlists for Affordable Housing Allocation, Management Science, 2019.

Peng Shi, Assortment Planning in School Choice, 2019.

Ashlagi, Itai, and Afshin Nikzad. What matters in tie-breaking rules? how competition guides design, 2015.

(c) Auctions and revenue equivalence

Myerson, Roger Auction Design, Mathematics of Operations Research, 1981.

Milgrom, Paul. Putting Auction Theory to Work. Chapter 2-3.

W. Vickrey, Counterspeculation, auctions, and competitive sealed tenders, The Journal of Finance, 16(1) 8–37, 1961.

R. Myerson, Optimal auction design, Mathematics of Operations research, 1981.

J. Bulow and J. Roberts, The simple economics of optimal auctions, Journal of Political Economy, 1989.

J. Bulow and P. Klemperer, Auctions vs negotiations, American Economic Review, 1996.

P.R. McAfee and J. McMillan, Auctions and bidding, Journal of Economic Literature 1987.

P. Milgrom and R. Weber, A theory of auctions and competitive bidding, Econometrica, 1982.

Roth, A. E. and A. Ockenfels, Late-Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon.” American Economic Review, 92(4): 1093-1103, 2002.

(d) Mechanism design

Vickrey, William (1961): Counterspeculation, Auctions and Competitive Sealed Tenders. Journal of Finance, 16(1): 8-37.

Ausubel, Larry and Paul Milgrom, The Lovely but Lonely Vickrey Auction. in Cramton et. al Combinatorial Auctions, 2005.

J.C. Rochet, A necessary and sufficient condition for rationalizability in a quasi-linear context”, 1987.

K. Roberts, The characterization of implementable choice rules”, 1979.

F. Gul and E. Stacchetti, Walrasian equilibrium with gross substitutes, Journal of Economic Theory, 1999.

I. Ashlagi, M. Braverman, A,. Hassidim and D. Monderer, Monotonicity and implementability, Econometrica, 2011.

(e) Dynamic mechanism design and dynamic pricing

G. Gallego and G. Van Ryzin, Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management science, 40(8), 999-1020, 1994.

S. Board and A. Skrzypacz, Revenue management with forward-looking buyers, Unpublished manuscript, Stanford University,2010.

A. Gershkov, B. Moldovanu, P. Strack, Revenue Maximizing Mechanisms with Strategic Customers and Unknown, Markovian Demand

D. Bergemann and J. Valimaki, The dynamic pivot mechanism, Econometrica, 2010.

A. Gershkov and B. Moldovanu, Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach, 168-198, 2009.

F. Gul, H. Sonnenschein, R. Wilson, Foundations of dynamic monopoly and the Coase conjecture, J. of Economic Theory, 1986.

D. Besanko and W. L. Whinston, Optimal price skimming by a monopolist facing rational consumers, Management Science, 1990.

(f) Dynamic matching

Itai Ashlagi and Alvin E. Roth. New challenges in multihospital kidney exchange. American Economic Review, 102:354-359, 2012

Nikhil Agarwal, Itai Ashlagi, Eduardo Azevedo, Clayton Featherston and Omer Karaduman. Market Failure in Kidney Exchange, 2018.

Anderson, R., Ashlagi, I., Gamarnik, D. and Kanoria, Efficient Dynamic Barter Exchange, Operations Research, 2015.

Mohammad Akbarpour, Shengwu Li, and Shayan Oveis Gharan. Dynamic matching market design. JPE, 2019.

Baccara, Mariagiovanna, SangMok Lee, and Leeat Yariv, Optimal dynamic matching, 2015.

Jacob Leshno, Dynamic Matching in Overloaded Waiting Lists, 2017.

Two courses on matching and market design in Stanford's MS&E department, by Ashlagi and Saberi (first meeting is today)

Itai Ashlagi and  Amin Saberi are offering courses on matching theory and market design this quarter. First meetings are today, in the morning and in the afternoon:

MS&E 230: Market design for engineers

Itai Ashlagi  T-Thu 9:45-11:15

Course description:  Marketplaces use algorithms and sets of rules in order to allocate resources among self-interested agents, who often hold necessary information. This course will provide key principles in engineering a marketplace, to identify relation between the rules in the marketplace and market failures, and how to redesign them to achieve desirable outcomes.  The course provides foundations of resource allocation systems building on game theoretic analysis.  The course explores economic and algorithmic tools from matching, mechanism design, auction theory and information design. Cases of existing and future marketplaces will be discussed, including ride-sharing systems, school choice programs, online dating, online advertising and organ allocation.

Some applied questions include: How should we assign students to schools? How should we match doctors to residency programs? Shall we price roads and the impact of tolls? How should we allocate affordable housing and arrange waiting lists? How should we incentivize hospitals to collaborate on kidney exchanges? How should we allocate food to food banks? How should reduce organ discards?  How can we assist in migration?  Why is some marketplace decentralized (college admissions) and others are more controlled? How should platforms set incentives? What incentives and frictions arise in blockchains? 

 **********

MS&E 319: Matching Theory
Amin Saberi  T Th 01:30p-03:00p

The theory of matching with its roots in the work of mathematical giants like Euler and Kirchhoff has played a central and catalytic role in combinatorial optimization for decades. More recently, the growth of online marketplaces for allocating advertisements, rides, or other goods and services has led to new interest and progress in this area.  

The course starts with classic results characterizing matchings in bipartite and general graphs and explores connections with algebraic graph theory and discrete probability. Those results are complemented with models and algorithms developed for modern applications in market design, online advertising, and ride-sharing.

Topics include: 

Matching, determinant, and Pfaffian
Matching and polynomial identity testing
Isolating lemma and matrix inversion, matching in RNC

Combinatorial and polyhedral characterizations 
The assignment problem and its dual, primal-dual, and auction algorithms
Tutte’s theorem, Edmond’s LP, and the Blossom algorithm

The Gallai-Edmonds decomposition, Berge-Tutte formula, and applications in Nash bargaining

The stable marriage problem
Gale-Shapley theorem, incentive and fairness issues
LP characterization, counting stable matchings

Matching in dynamic environments
Online matching under various arrival models
Applications in ride-sharing and online advertising

 

Computation of matching  

Combinatorial vs continuous algorithms, near-linear time algorithms

Matchings in sub-linear time, streaming computational models

Sparsifiers and stochastic matching 

Counting matchings  
The Van der Waerden conjecture, Bregman-Minc’s inequality
Deterministic approximations, counting matchings in planar graphs
Markov chain Monte Carlo algorithms, sequential importance sampling
The Ising model, applications, and basic properties

***********

 

 

Course on Matching, in Barcelona, by Péter Biró and David Manlove, April 27-28

If you are a Ph.D. student interested in matching, here's a two day course in Barcelona, by experts in the field.

V SEIO Course on Game Theory   

The University of Barcelona, the BEAT Research Institute, and the Game Theory and Assignment Markets Research Group are delighted to host the V SEIO Course on Game Theory on April 27 and 28, 2022.

The course is targeted at PhD students and early career researchers working in areas related to game theory. Besides covering a very active research topic, it is also an opportunity to meet with other researchers working in similar areas. 

The two-day course will cover algorithmic and game theoretic aspects of matching markets. Participants are welcome to present their game theory related research during a poster session.

The course will be delivered by Péter Biró (Head of  the Mechanism Design Group at KRTK) and David Manlove (Professor of Algorithms and Complexity at the University of Glasgow).

Registration deadline:  April 1, 2022  Registration is free, please fill in the on-line form to register.

Day 1 (April 27)

 09:00-09:30 Registration and welcome session

09:30-10:20 Stable Marriage and Hospitals / Residents problems: classical results

10:20-10:50 Coffee break

10:50-11:40 Decentralised matching markets, path-to-stability results

11:50-12:40 Hospitals / Residents problem: extensions (ties, couples, lower quotas)

12:40-14:10 Lunch

14:10-15:00 Hungarian university admissions: matching with contracts, choice functions, cutoff

stability

15:00-15:30 Coffee break

15:30-16:20 Housing markets: exchange of indivisible goods

16:30-17:20 Respecting improvement property for housing markets

Day 2 (April 28)

 09:00-09:50 House Allocation problem: Pareto optimal, popular and profile-based optimal matchings

10:00-10:50 School choice and constrained welfare-maximizing solutions

10:50-11:50 Coffee break and poster session

11:50-12:40 Stable roommate problems

12:40-14:10 Lunch

14:10-15:00 Matching with payments, auctions

15:00-15:30 Coffee break

15:30-16:20 Kidney Exchange

16:30-17:20 Generalized matching games, international kidney exchange

Market design course for health policy and medical students, at Stanford, taught by Alex Chan and Kurt Sweat

 Starting tomorrow, a short course in market design:

BIOS 203, Fall 2021: Market Design and Field Experiments for Health Policy and Medicine 

Primary Instructor: Alex Chan chanalex@stanford.edu | Office Hours: By appointment

Secondary Instructor: Kurt Sweat kurtsw@stanford.edu | Office Hours: By appointment

Description. Market design is an emerging field in economics, engineering and computer science about how to organize systems to allocate scarce resources. In this course, we study (1) the theory and practice of market design in healthcare and medicine, and (2) methods to evaluate the impact of such designs. Students will be provided with the necessary tools to diagnose the problems in markets and allocation mechanisms that render them inefficient, and subsequently develop a working toolbox to remedy failed markets and finetune new market and policy designs.

With a practical orientation in mind, we will learn how to construct rules for allocating resources or to structure successful marketplaces through successive examples in healthcare and medicine: medical residency matching, kidney exchange, allocation of scarce medical resources like COVID vaccine and tests, medical equipment procurement, online marketplace for doctors, and, if time permits, reward system for biopharmaceutical innovation. Guest lectures by practicing market designers and C-suite healthcare executives (CEO, CFO) would feature in the course as well.

An important goal of the class is to introduce you to the critical ingredients to a successful design: a solid understanding of institutions, grasps of economic theory, and well-designed experiments and implementation. In the final sessions, students will also learn how to design and deploy one of the most powerful tools in practical market design: A/B testing or randomized field experiments. These techniques are widely used by tech companies like UBER, Amazon, eBay, and others to improve their marketplaces.

At the end of the course, students should have acquired the necessary knowledge to become an avid consumer and user, and potentially a producer, of the market design and field experimental literature (recognized by 4 recent Nobel Prizes in Economics: 2007/2012/2019/2020).

Time & Location.

● Tue, Thu 6:30 PM - 8:00 PM (beginning November 16, 2021) at Encina Commons Room 119

Course Webpage. ● https://canvas.stanford.edu/courses/145148

Schedule and Readings

(* required readings, others are optional)

Session 1. Market design and Marketplaces – November 16

1. * Roth, A. E. (2007). The art of designing markets. harvard business review, 85(10), 118.

2. Kominers, S. D., Teytelboym, A., & Crawford, V. P. (2017). An invitation to market design. Oxford Review of Economic Policy, 33(4), 541-571.

3. Roth, A. E. (2002). The economist as engineer: Game theory, experimentation, and computation as tools for design economics. Econometrica, 70(4), 1341-1378

Session 2. Matching Markets: Medical Residents and the NRMP – November 18

1. * Chapter 1 in Gura, E. Y., & Maschler, M. (2008). Insights into game theory: an alternative mathematical experience. Cambridge University Press.

2. * Fisher, C. E. (2009). Manipulation and the Match. JAMA, 302(12), 1266-1267.

3. * National Resident Matching Program. (2021). Feasibility of an Early Match NRMP Position Statement

4. Roth, A. E., & Peranson, E. (1997). The effects of the change in the NRMP matching algorithm. JAMA, 278(9), 729-732.

5. Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9-15.

Session 3. Kidney Exchange and Organ Allocation – November 30

1. * Wallis, C. B., Samy, K. P., Roth, A. E., & Rees, M. A. (2011). Kidney paired donation. Nephrology Dialysis Transplantation, 26(7), 2091-2099.

2. * Chapter 3 in Roth, A. E. (2015). Who gets what—and why: The new economics of matchmaking and market design. Houghton Mifflin Harcourt.

3. Gentry, S. E., Montgomery, R. A., & Segev, D. L. (2011). Kidney paired donation: fundamentals, limitations, and expansions. American journal of kidney diseases, 57(1), 144-151.

4. Salman, S., Gurev, S., Arsalan, M., Dar, F., & Chan, A. Liver  Exchange: A Pathway to Increase Access to Transplantation.

5. Sweat, K. R. Redesigning waitlists with manipulable priority: improving the heart transplant waitlist.

6. Agarwal, N., Ashlagi, I., Somaini, P., & Waldinger, D. (2018). Dynamic incentives in waitlist mechanisms. AEA Papers & Proceedings, 108, 341-347.

Session 4. 1 st Half: Repugnance as a Constraint on Markets – December 2

1. * Roth, A. E. (2007). Repugnance as a Constraint on Markets. Journal of Economic perspectives, 21(3), 37-58.

2. * Minerva, F., Savulescu, J., & Singer, P. (2019). The ethics of the Global Kidney Exchange programme. The Lancet, 394(10210), 1775-1778.

3. Chapter 11 in Roth, A. E. (2015). Who gets what—and why: The new economics of matchmaking and market design. Houghton Mifflin Harcourt.

2 nd Half: Market Design and Allocation during COVID-19 – December 2

1. * Emanuel, E. J., Persad, G., Upshur, R., Thome, B., Parker, M., Glickman, A., ... & Phillips, J. P. (2020). New England Journal of Medicine. Fair allocation of scarce medical resources in the time of Covid-19.

2. Piscitello, G. M., Kapania, E. M., Miller, W. D., Rojas, J. C., Siegler, M., & Parker, W. F. (2020). Variation in ventilator allocation guidelines by US state during the coronavirus disease 2019 pandemic: a systematic review. JAMA network open, 3(6), e201

3. Schmidt, H., Pathak, P., Sönmez, T., & Ünver, M. U. (2020). Covid-19: how to prioritize worse-off populations in allocating safe and effective vaccines. British Medical Journal, 371.

4. Schmidt, H., Pathak, P. A., Williams, M. A., Sonmez, T., Ünver, M. U., & Gostin, L. O. (2020). Rationing safe and effective COVID-19 vaccines: allocating to states proportionate to population may undermine commitments to mitigating health disparities. Ava

5. Neimark, J. (2020). What is the best strategy to deploy a COVID-19 vaccine. Smithsonian Magazine.

Session 5. 1 st Half: Auction Design and Procurement in Medicine – December 7

1. * The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel. (2020). Improvements to auction theory and inventions of new auction formats. Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 20

2. * Song, Z., Cutler, D. M., & Chernew, M. E. (2012). Potential consequences of reforming Medicare into a competitive bidding system. Jama, 308(5), 459-460.

3. Newman, D., Barrette, E., & McGraves-Lloyd, K. (2017). Medicare competitive bidding program realized price savings for durable medical equipment purchases. Health Affairs, 36(8), 1367-1375.

4. Cramton, P., Ellermeyer, S., & Katzman, B. (2015). Designed to fail: The Medicare auction for durable medical equipment. Economic Inquiry, 53(1), 469-485.

5. Ji, Y. (2019). The Impact of Competitive Bidding in Health Care: The Case of Medicare Durable Medical Equipment.

6. Thaler, R. H. (1988). Anomalies: The winner's curse. Journal of economic perspectives, 2(1), 191-202.

7. Chapter 2 in Haeringer, G. (2018). Market design: auctions and matching. MIT Press.

2 nd Half: (GUEST LECTURE) Ralph Weber, CEO, MediBid Inc. on “The Online Marketplace for Medicine” – December 7

Session 6. A/B Testing and Field Experiments to Test Designs – December 9

1. * Chapters 1, 4 in List, John. (2021). A Course in Experimental Economics (unpublished textbook, access on course website)

2. * Gallo, A. (2017). A refresher on A/B testing. Harvard Business Review, 2-6.

3. Chan, A. (2021). Customer Discrimination and Quality Signals – A Field Experiment with Healthcare Shoppers.

4. Kessler, J. B., Low, C., & Sullivan, C. D. (2019). Incentivized resume rating: Eliciting employer preferences without deception. American Economic Review, 109(11), 3713-44.

5. Chapters 3, 5, 6, 7, 8 in List, John. (2021). A Course in Experimental Economics (unpublished textbook, access on course website)

6. The Committee for the Prize in Economic Sciences in Memory of Alfred Nobel. (2019). Understanding development and poverty alleviation. Scientific Background on the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2019.

Bonus Session (optional). (GUEST LECTURE) Donald Lung, CFO, Antengene on “Designing Markets to Access Biopharmaceutical Intellectual Property Across Regulatory Regimes – the Case of China” – Date TBD

Bonus Session (optional). (GUEST LECTURE) TBD – Date TBD

Two market design courses at Harvard this semester, by Kominers, and by Akbarpour and Li

If you are at Harvard this semester, you have two market design classes available:

Mohammad Akbarpour (visiting for the quarter from Stanford) and Shengwu Li will be teaching
SIMPLICITY & COMPLEXITY IN ECONOMICS
Fall 2019 T/Th 9 :00-10:15, ECON 2071

and Scott Kominers will again be teaching
ECON 2099, "Market Design"  

Gabriel Weintraub's class on online marketplaces at Stanford GSB (winter quarter)

Gabriel Weintraub writes:

I am sharing this information in case some of you are interested on this new half-quarter PhD course I will be teaching this coming Winter:

OIT 648: Empirics of Online Markets
In this course we cover current research on the empirics of platforms and online marketplaces. We will study diverse topics relevant to the design of these markets such as search and matching, review and reputation systems, demand estimation, and pricing. We will do so in the context of different application domains such as rentals, sharing, e-commerce, labor markets, and advertising. The course will be eclectic in terms of approaches, using reduced-form and structural econometrics, machine learning, and experimentation. The course will mostly consist of recent papers presented by the instructor, guests, and students. Some background knowledge required to understand current work will be provided as needed.

The course will meet on the following Mondays between 3:30 and 6:20PM: 

- Mon. Feb 4

- Mon. Feb. 11

- Mon. Feb. 25
- Mon, Mar 4
- Mon. Mar 11
- Fri. Mar. 15

Market design class at Harvard taught by Scott Kominers

If you are a Harvard student, check out Scott Kominer's Fall market design class.
Here's the course outline.

Market Design

---

Economics 2099 -- Harvard University -- Fall 2018 

Description:

This course explores the theory and practice of market design. Key topics include auctions, labor market matching, school choice programs, online markets, organ exchange systems, financial market design, and matching with contracts. The first half of the course will introduce market design and its technology; subsequent weeks will discuss recent papers alongside their classical antecedents.

Information on Logistics, Requirements, and Readings:

See the course syllabus (posted August 29, 2018).

Enrollment Application:

https://tinyurl.com/2099app/.

Assignment Deadlines:

A short proposal summary/plan will be due on October 17, 2018. The final proposal will be due on December 10, 2018 (the last day of Reading Period).

Schedule:

Date	Topic	Guest(s)
September 4, 2018	Introduction/Overview
September 11, 2018	The Market Designer's Toolbox
September 18, 2018	Food Supply, Scrip Systems, and Pseudo-Markets	Erica Moszkowski
September 25, 2018	School Choice
October 2, 2018	Generalized Matching	Ravi Jagadeesan
October 9, 2018	Markets for Intellectual Property
October 16, 2018	Auction Theory	Shengwu Li
October 23, 2018	The US Spectrum Incentive Auction
October 30, 2018	Organ Allocation
November 6, 2018	Finance, Cryptocurrency, and Blockchain
November 13, 2018	Inequality and Urban Issues	Edward L. Glaeser
November 20, 2018	New Horizons	Zoë Cullen, Andrey Fradkin David Parkes, Utku Ünver, Kate Vredenburgh
November 27, 2018	Refugees, Immigration, and Economic Development	Benjamin Roth
December 4, 2018	Student Talks/Course Wrap

Internal Harvard Website:

https://canvas.harvard.edu/courses/43959/.

Office Hours Calendar:

https://2099-officehours.youcanbook.me/.

Market design comes to Harvard Law School

Here's a piece from the Harvard Law Bulletin that caught my eye:
Holger Spamann brings new thinking to the structure of his class and casebook 

He's teaching "a corporate finance course divided into four different modules, any of which students can opt out of depending on their knowledge level.

"A student who comes in with a great deal of experience in the field will be able to skip the initial module on basic valuation. Subsequent modules cover diversification and market efficiency, capital structure, and then finally auctions and market design. Students who want to only dip their feet can opt out of later modules.

"Spamann, who also earned a master’s and Ph.D. in economics from Harvard and practiced briefly as an M&A attorney, says his background in economics informs his approach to corporate finance and how he teaches the subject."
********

Spamann and Guhan Subramanian also have a casebook for sale: here's the Amazon link.

Corporations: Casebook Paperback – September 2, 2017

by Holger Spamann (Author), Guhan Subramanian (Author)

Scott Kominers had a big market design class at Harvard this year

Scott may eventually educate a high percentage of market designers: here's a picture of his class this year.

Matching and more: Continuing Education at the AEA meetings in Philadelphia

2018 Continuing Education, January 7-9, 2018, Sheraton Philadelphia Downtown

The AEA's 2018 Continuing Education Program will be held at the Sheraton Philadelphia Downtown on January 7-9, 2018, immediately following the close of ASSA. Participants can choose from three concurrent programs. Registration now open. (Alternatively, download PDF Registration form.)

Matching Market Design

Atila Abdulkadiroglu (Duke University)

Atila Abdulkadiroglu joined the Department of Economics at Duke University in the Fall of 2006. He taught at Northwestern University and Columbia University before coming to Duke. He received his PhD in Economics at the University of Rochester. His research has led to the design and implementation of better admissions policies in school choice programs in the US, He has consulted several school districts in redesigning student assignment systems, including Boston (MA), Chicago (Il), Denver (CO), New Orleans (LA), New York City (NY). His current research also focuses on economics of education. He is a recipient of an Alfred P. Sloan Research Fellowship and a National Science Foundation CAREER award. Abdulkadiroglu serves as an Editor-in-Chief of Review of Economic Design. He serves on the board of The Institute for Innovation in Public School Choice.

Nikhil Agarwal (MIT)

Nikhil Agarwal is the Castle Krob Career Development Assistant Professor of Economics at Massachusetts Institute of Technology, where he has been teaching since 2014. He completed his PhD in Economics at Harvard University in 2013, and was a Postdoctoral Associate at the Cowles Foundation for Research in Economics at Yale University. Agarwal specializes in the empirical study of matching markets. He has developed tools that have been applied to labor markets, education markets and organ allocation systems.

Parag Pathak (MIT)

Parag A. Pathak is the Jane Berkowitz Carlton and Dennis William Carlton Professor of Microeconomics at MIT, found­ing co-director of the NBER Working Group on Market Design, and founder of MIT's School Effectiveness and Inequality Initiative (SEII), a laboratory focused on education, human capital, and the income distribution.  Pathak has helped to design the Boston, Chicago, Denver, Newark, New Orleans, New York, and Washington DC school choice systems.   His work on mar­ket design and edu­ca­tion was garnered numerous recognitions including a Presidential Early Career Award for Scientists and Engineers and the 2016 Social Choice and Welfare prize.  He has also authored leading studies on charter schools, high school reform, selective education, and school vouchers.  Pathak is a Fellow of the Econometric Society, and has served on the editorial boards of Econometrica, American Economic Review, and the Journal of Political Economy.

---

Machine Learning and Econometrics

Susan Athey (Stanford University)

Susan Athey is the Economics of Technology Professor at Stanford Graduate School of Business.  She received her bachelor’s degree from Duke University and her PhD from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. Her current research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning.  She has worked on several application areas, including timber auctions, internet search, online advertising, the news media, and virtual currency. As one of the first “tech economists,” she served as consulting chief economist for Microsoft Corporation for six years, and now serves on the boards of Expedia, Rover, and Ripple.  She also serves as a long-term advisor to the British Columbia Ministry of Forests, helping architect and implement their auction-based pricing system.

Guido Imbens (Stanford University)

Guido Imbens is Professor of Economics at the Stanford Graduate School of Business. After graduating from Brown University Guido taught at Harvard University, UCLA, and UC Berkeley. He joined the GSB in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences. Guido Imbens has taught in the continuing education program previously in 2009 and 2012.

---

DSGE Models and the Role of Finance

Lawrence Christiano (Northwestern University)

Larry Christiano's research has been focused primarily on the problem of determining how the government's monetary and fiscal instruments ought to respond to shocks over the business cycle. This research has two parts: one involves formulating and estimating an empirically plausible model of the macroeconomy, and the second involves developing economic concepts and computational methods for determining optimal policy in an equilibrium model. He is a Fellow of the Econometric Society, and a research associate of the National Bureau of Economic Research.

Thomas Philippon (New York University)

Thomas Philippon is Professor of Finance at New York University - Stern School of Business. Philippon was named one of the “top 25 economists under 45” by the IMF in 2014, he won the 2013 Bernácer Prize for Best European Economist under 40, the 2010 Michael Brennan & BlackRock Award, the 2009 Prize for Best Young French Economist, and the 2008 Brattle Prize for the best paper in Corporate Finance. He was elected Global Economic Fellow in 2009 by the Kiel Institute for the World Economy. He has studied various topics in finance and macroeconomics: financial distress, systemic risk, government interventions during financial crises, asset markets and corporate investment. Recently his work has focused on the evolution of the financial system and on the Eurozone crisis. He currently serves on the Monetary Policy Advisory Panel of the NY Federal Reserve Bank, and as a board member and director of the scientific committee of the French prudential regulator (ACPR). He was the senior economic advisor to the French finance minister in 2012-2013. Philippon graduated from Ecole Polytechnique, received a PhD in Economics from MIT, and joined New York University in 2003.

Scott Kominers' Market design course at Harvard

http://scottkom.com/courses/Market-Design_2017-2018/index.html

Date	Topic	Guest(s)
September 5, 2017	Introduction/Overview
September 12, 2017	The Market Designer's Toolbox
September 19, 2017	Food Supply, Scrip Systems, and Pseudo-Markets
September 26, 2017	School Choice	Parag Pathak
October 3, 2017	Signaling in Matching Markets
October 10, 2017	Internet Markets	Ben Edelman, Andrey Fradkin
October 17, 2017	Auction Theory	Shengwu Li
October 24, 2017	The US Spectrum Incentive Auction
October 31, 2017	Organ Allocation	Carmen Wang
November 7, 2017	Refugees, Immigration, and Economic Development	Ilya Vidrin
November 14, 2017	Markets for Intellectual Property
November 21, 2017	New Horizons	Ravi Jagadeesan, David Parkes, Ben Roth, Utku Ünver
November 28, 2017	Student Talks/Course Wrap

Internal Harvard Website:

https://canvas.harvard.edu/courses/29888/.

Market design at Harvard Business School

Here's the announcement of a new HBS course on market design:

Making Markets

Course Number 1764

Professor Thomas R. Eisenmann
Associate Professor Scott Duke Kominers
Spring; Q3Q4; 3 credits
24 sessions
Paper

Markets are everywhere - and where they’re not, you can build them!

Career Focus

Over the past twenty years, entrepreneurs have created and captured enormous value by launching new marketplaces. Examples include Airbnb, Alibaba, ClassPass, Craigslist, eBay, eHarmony, Etsy, Gerson Lehrman Group, Google, IEX Group, Lending Club, Kickstarter, OpenTable, Rakuten, Uber, Upwork, and many more.

Making Markets (M²) is intended for students who want to manage in marketplace environments and remedy market failures by building new platforms and marketplaces from scratch or by redesigning existing ones - or who want to advise or invest in entrepreneurs who pursue such opportunities.

Educational Objectives

Students will learn how to identify market failures and determine when those failures create opportunities to launch or redesign marketplaces.

First, we will explore how markets function and what makes them fail. Next, we will examine how effective marketplace design-or redesign-can address market failures and improve efficiency, liquidity, and fairness. Then, we will take the entrepreneur’s perspective, studying the key barriers to organizing new marketplaces and devising strategies for overcoming them. Along the way, we will pay special attention to settings in which marketplaces create more value for transaction partners than relying only on unmediated exchanges. As we will see, marketplace design can often “square the circle,” solving seemingly intractable problems simply by reducing transaction costs or barriers to entry.

Case contexts will range from ultra-local (e.g., the HBS EC course lottery) to truly global (e.g., container shipping); will examine private and public/social enterprise settings; will profile both online and offline marketplaces; and will span all stages of marketplace launch and development.

Course Content

Through case studies, simulations, and the occasional interactive lecture, M² will examine the design, launch, and management of marketplaces and marketplace platforms. Core lessons include:

• The Structure and Purposes of Markets: Markets create value by enabling parties to execute mutually beneficial transactions - exchanging goods, say, or sharing ideas. They are everywhere that transacting parties face incentives - from classic contexts like financial or product markets to dating, recruiting, and the sharing economy.
  Some markets are completely unstructured, but most are subject to at least some rules that shape participation. In this course, we will focus in particular on markets that are organized through marketplaces that combine rules for participation with infrastructure to facilitate interactions and transactions.
• Common Sources of Market Failure: In many markets, institutional frictions combine with incentives to produce suboptimal outcomes - socially wasteful transactions occur, or productive ones do not. When such market failures occur, entrepreneurial opportunities arise: reshaping the market to improve efficiency creates value that can be captured(!).
  To understand how to fix markets, however, we must first understand how and why market failures occur. The course will classify different types of market failures, and highlight entrepreneurial responses to each.
• Strategies for Launching and Managing Marketplaces: When launching a marketplace or other market intervention, it is essential to mobilize a critical mass of market participants so that there is enough liquidity for valuable transactions to occur. Once running, a marketplace must maintain balance between its supply and demand sides, or else participants may leave to transact elsewhere. Yet at the same time, marketplaces must avoid crowding that makes it hard for participants to find high-value transaction partners.
  The course will provide strategies for promoting participation and trust in marketplaces, especially early on. Then, we will learn techniques for growing marketplaces, and combating the problems that marketplaces face at scale, such as congestion, “unraveling” (e.g., when recruiters pressure candidates with early and exploding offers), and the risk of disintermediation.
• Types of Marketplace Mechanisms: Markets work in many different ways. Some compel participants to seek out their own transaction partners; others use centralized transaction discovery and execution systems like auctions and recommendation algorithms. The mechanisms that a marketplace uses to identify and process transactions can be the difference between success and failure.
  Choosing among marketplace mechanisms requires careful attention to market participants’ needs and transaction attributes. The course will provide guidelines for adopting mechanisms best suited for different market contexts.

Incentives in Computer Science--Tim Roughgarden

There was a time when only economists worried about incentives, but as this great looking computer science course by Tim Roughgarden shows, that time is long past...

CS 269I: Incentives in Computer Science

Instructor:

• Tim Roughgarden (Office hours (note new time): Mondays 12:15-1:15 PM, Gates 474. Email: tim@cs.stanford.edu.)

Prerequisites: Mathematical maturity at the level of undergraduate algorithms (CS161). Programming maturity at the level of 106B/X.
Course Description: Many 21st-century computer science applications require the design of software or systems that interact with multiple self-interested participants. This course will provide students with the vocabulary and modeling tools to reason about such design problems. Emphasis will be on understanding basic economic and game theoretic concepts that are relevant across many application domains, and on case studies that demonstrate how to apply these concepts to real-world design problems. Topics include auction and contest design, equilibrium analysis, cryptocurrencies, design of networks and network protocols, matching markets, reputation systems, and social choice. Possible case studies include BGP routing, Bitcoin, eBay's reputation system, Facebook's advertising mechanism, Mechanical Turk, and dynamic pricing in Uber/Lyft.
General references: Twenty Lectures on Algorithmic Game Theory, Cambridge University Press, 2016. See also the Amazon page.

• This textbook is based on the course CS364A. The overlap with 269I will be roughly 20-25%. Though if you enjoy this course, you're likely to also enjoy many of the topics in this book.

The following collection is older and targeted more to researchers than to students, but is still useful for several topics.

• Algorithmic Game Theory, Cambridge University Press, 2007. Read the entire book online by clicking here (look under the "Resources" tab).

We will also draw on the following books for some of the lectures.

• Parkes and Seuken, Economics and Computation (draft), 2016. (Stanford access only.)
• Easley and Kleinberg, Networks, Crowds, and Markets, 2010.

Lecture notes

• Lecture 1 (The Draw/College Admissions) [beta version]
• Lecture 2 (Stable Matching) [beta version]
• Lecture 3 (Strategic Voting) [beta version]
• Lecture 4 (Voting Rules as MLE/Knapsack Voting) [beta version]
• Lecture 5 (Incentives in P2P Networks) [beta version]
• Lecture 6 (Incentivizing Participation) [beta version]
• Lecture 7 (Selfish Routing) [beta version]
• Lecture 8 (Incentives in BGP Routing) [beta version]
• Lecture 9 (Incentives in Bitcoin) [beta version]
• Lecture 10 (Incentives in Crowdsourcing) [beta version]
• Lecture 11 (slides) (Incentives in Societal Networks) [guest lecture by Balaji Prabhakar]
• Lecture 12 (Reputation Systems) [beta version]
• Lecture 13 (Auction Basics) [beta version]
• Lecture 14 (Sponsored Search) [beta version]
• Lecture 15 (VCG Mechanism) [beta version]
• Lecture 16 (Revenue-Optimal Auctions) [beta version]
• Lecture 17 (Incentiving Forecasts and Feedback) [beta version]
• Lecture 18 (Prediction Markets) [beta version]
• Lecture 19 (Time-Inconsistent Planning) [beta version]
• Lecture 20 (Fair Division) [beta version]

Coursework

• Exercise Sets (50%): Exercise sets will be handed out on Wednesdays and will be due one week later. You can work in pairs, if you wish.
  • Here's a LaTeX template that you can use to type up solutions. Here and here are good introductions to LaTeX.
  • Week 1 Exercises
  • Week 2 Exercises
  • Week 3 Exercises
  • Week 4 Exercises
  • No exercises for Week 5!
  • No exercises for Week 6!
  • Week 7 Exercises
  • Week 8 Exercises
  • Week 9 Exercises
• Final Project (50%): For details and some example topics, see here.

Tentative Syllabus (will likely change)

• Week 1: Introduction to incentives through killer examples.
• Week 2: Social choice (voting, Arrow's impossibility theorem, etc.).
• Week 3: Incentives in peer-to-peer and social networks (e.g., incentives in BitTorrent).
• Week 4: Incentives in communication networks (routing, flow control, etc.).
• Week 5: Incentives in cryptocurrencies (like Bitcoin).
• Week 6: Reputation systems. Incentives in crowdsourcing.
• Week 7: Basic auction theory (eBay, sponsored search auctions).
• Week 8: Advanced auction theory and mechanism design (Facebook advertising auctions, contest design).
• Week 9: Scoring rules and prediction markets.
• Week 10: Lessons from behavioral economics (i.e., how do people make decisions, anyway?).

Detailed Lecture Schedule

• Lecture 1 (Mon Sept 26): The incentives of the Draw, past and present. Pareto optimality and strategyproofness. College admissions. One-sided vs. two-sided markets. The National Resident Matching Program (NRMP). Supplementary reading:
  • Check out the changes made to the Draw in 1970, as reported by the Stanford Daily. The other articles make for a fascinating time capsule, as well.
  • The original Gale-Shapley paper (1962). It's very easy to read!
• Lecture 2 (Wed Sept 28): Stable matchings. Properties of the deferred acceptance (Gale-Shapley) mechanism. Could college admissions go through a centralized clearinghouse? Supplementary reading:
  • Biró, Student Admissions in Hungary as Gale and Shapley Envisaged, 2008.
  • Section 2 of Lecture 10 from CS364A (or from the optional textbook).
  • The paper that compares the evolution of different resident-hospital matching algorithms in the U.K. (Roth, 1991).
  • Citation for the 2012 Nobel Prize (Roth and Shapley).
• Lecture 3 (Mon Oct 3): Participatory democracy. Strategic voting. Spoilers and the 2000 US election. Majority, plurality, ranked-choice voting, Borda counts. Gibbard-Satterthwaite and the impossibility of reasonable strategyproof voting rules. Arrow's Impossibility Theorem. Compromises, single-peaked preferences, and the median voting rule. Supplementary reading and resources:
  • Participatory budgeting in general and at Stanford.
  • The rank aggregation problem.
  • Reasonably short proofs of the Gibbard-Satterthwaite and Arrow impossibility theorems are here (see Sections 1.2.3 and 1.2.4).
  • Chapter 23 of the Easley/Kleinberg book (see general references).
• Lecture 4 (Wed Oct 5): Subjective vs. objective interpretations of voting rules. Metaphor: linear regression as the maximum likelihood solution with normally distributed errors. Marquis de Condorcet and majority rule as a maximum likelihood estimator. The Kemeny-Young rule. Knapsack voting and its properties. Supplementary reading and resources:
  • The dramatic life of Marquis de Condorcet.
  • See Pnyx for an implementation of the Kemeny rule.
  • Knapsack voting, by Goel/Krishnaswamy/Sakshuwong (2014).
  • Section 15.2 of the Parkes/Suen book (see general references).
• Lecture 5 (Mon Oct 10): Incentives in peer-to-peer (P2P) networks. History lesson: Napster, Gnutella, etc. Free riding on Gnutella. Prisoner's Dilemma. Repeated Prisoner's Dilemma: the grim trigger and Tit-for-Tat stategies. Tit-for-tat in the BitTorrent reference client. Strategic clients (BitThief and BitTyrant). Supplementary reading:
  • Chapter 5 of the Parkes/Seuken book draft, above.
  • Free Riding on Gnutella (Adar/Huberman, 2000)
  • The Axelrod tournaments: The Evolution of Cooperation (Axelrod/Hamilton, 1981)
  • Incentives Build Robustness in BitTorrent (Cohen, 2003) describes the motivation for the original design.
  • BitThief (Locher/Moor/Schmid/Wattenhofer, 2006)
  • BitTyrant (Piatek/Isdal/Anderson/Krishnamurthy/Venkataramani, 2007)
• Lecture 6 (Wed Oct 12): Coordination games. Technology adoption and network cascades. Individual vs. collective preferences in public good problems. Case study: badge design in Stack Overflow, Coursera, etc. Supplementary reading:
  • See CS 224W for much more on network cascades.
  • Steering User Behavior with Badges (Anderson/Huttenlocher/Kleinberg/Leskovec, 2013)
  • Engaging with Massive Online Courses (same authors, 2014)
• Lecture 7 (Mon Oct 17): Selfish routing and network over-provisioning. Braess's paradox and Pigou's example. The price of anarchy. Modest over-provisioning guarantees near-optimal routing.
  • Demonstration of Braess's paradox.
  • How Bad Is Selfish Routing? (Roughgarden/Tardos, 2000)
  • The Price of Anarchy is Independent of the Network Topology (Roughgarden, 2002)
  • See also Lectures 11 (Sections 1-3) and 12 (Section 1) from CS364A (or from the optional textbook).
• Lecture 8 (Wed Oct 19): The Border Gateway Protocol for Internet routing. Stable routings: non-uniqueness and non-existence. Dispute wheels and the convergence of BGP to a unique solution. Incentive issues. Incentive-compatability with path verification. Supplementary reading:
  • The Stable Paths Problem and Interdomain Routing (Griffin/Shepherd/Wilfong, 2002)
  • Stable Internet Routing Without Global Coordination (Gao/Rexford, 2001)
  • Interdomain Routing and Games (Levin/Schapira/Zohar, 2008)
  • A Survey of Interdomain Routing Policies (Gill/Schapira/Goldberg, 2013)
  • Latest draft of the BGPsec protocol (2016)
• Lecture 9 (Mon Oct 24): Incentives in Bitcoin mining. Transactions and the Bitcoin blockchain protocol. Forks. Incentive issues: the 51% attack, the double-spend attack, and selfish mining. Supplementary reading:
  • General reference: the book Bitcoin and Cryptocurrency Technologies. (Narayanan/Bonneau/Felten/Miller/Goldfeder, 2016)
  • See also Section 22.3 of the Parkes/Suen book (see general references).
  • Majority is not Enough: Bitcoin Mining is Vulnerable (Eyal/Gün Sirer, 2014)
• Lecture 10 (Wed Oct 26): Incentives in crowdsourcing. Bitcoin in a regime with high transaction fees. The DARPA Network Challenge and incentivizing recruitment. Sybil attacks and possible solutions. The "Wisdom of the Crowd": fact or fiction? Herding behavior and information cascades. Supplementary reading:
  • On the Instability of Bitcoin Without the Block Reward (Carlsten/Kalodner/Weinberg/Narayanan, 2016)
  • Time-Critical Social Mobilization (Pickard et al., 2011) describes the MIT group's winning strategy in the DARPA Network Challenge.
  • Less happy times in the 2011 DARPA Shredder Challenge.
  • On Bitcoin and Red Balloons (Babaioff/Dobzinski/Oren/Zohar, 2012)
  • Chapter 16 of the Easley/Kleinberg book (see general references).
• Lecture 11 (Mon Oct 31): Incentives in societal networks (guest lecture by Balaji Prabhakar). "Nudges" for changing behavior. Case studies in Bangalore, Singapore, and at Stanford.
  • Balaji's slides
  • An Incentive Mechanism for Decongesting the Roads: A Pilot Program in Bangalore (Merugu/Prabhakar/Rama, 2009)
  • INSINC: A Platform for Managing Peak Demand in Public Transit (Pluntke/Prabhakar, 2013)
• Lecture 12 (Wed Nov 2): Adverse selection, moral hazard, and reputation systems. The market for lemons. Analogs in health insurance, the labor market, and online platforms. Moral hazard. Reputational effects in the n-person Prisoner's Dilemma. Whitewashing and the pay-your-dues strategy. Sybil attacks. Case study: the evolution of eBay's reputation system. Supplementary reading:
  • Chapter 27 of the AGT book (see general references).
  • Chapter 20 of the Parkes/Suen book (see general references).
  • Sections 22.5-22.9 of the Easley/Kleinberg book (see general references).
  • The Social Cost of Cheap Pseudonyms (Friedman/Resnick, 2001)
  • The evolution of the eBay reputation system: The Limits of Reputation in Platform Markets: An Empirical Analysis and Field Experiment(Nosko/Tadelis, 2015)
• Lecture 13 (Mon Nov 7): Auction design basics. How would you bid in a first-price auction? The Vickrey auction and truthfulness. Welfare maximization. Introduction to sponsored search auctions.
  • This lecture is based on Lecture 2 from CS364A (or from the optional textbook).
  • Optional: The Economics of Internet Search (Varian, 2006)
• Lecture 14 (Wed Nov 9): The theory of first-price auctions. Externalities. VCG: a truthful sponsored search auction. GSP vs. VCG. Supplementary reading:
  • See Section 2.3 of Hartline's book for more about the Bayes-Nash equilibria of first-price auctions.
  • Sponsored search: see AGT book, Sections 28.1-28.3.1 (see general references).
  • Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords (Edelman/Ostrovsky/Schwarz, 2007)
  • Position Auctions, (Varian, 2007)
  • See also this CS364B course for tons of subtopics and references on sponsored search auctions (circa late 2007).
• Lecture 15 (Mon Nov 14): Revenue equivalence of the GSP and VCG sponsored search auctions. VCG in AdSense and Facebook. The general VCG mechanism and its truthfulness. Practical issues with VCG. Supplementary materials:
  • The VCG Auction in Theory and Practice (Varian/Harris, 2014)
  • On How Machine Learning and Auction Theory Power Facebook Advertising, talk by Eric Sodomka (2015).
• Lecture 16 (Wed Nov 16): Revenue maximization. Bayesian optimal auctions. Monopoly prices. Optimality of Vickrey with a monopoly price reserve. Case study: reserve prices in Yahoo! keyword auctions. Prior-independent auctions and the Bulow-Klemperer theorem. Further reading:
  • For more details and proofs, see Lectures 5 and 6 from CS364A (or the optional textbook).
  • Ostrovsky/Schwarz, Reserve Prices in Internet Advertising Auctions: A Field Experiment, 2009.
• Lecture 17 (Mon Nov 28): Strictly proper scoring rules. Incentivizing honest opinions. Output agreement. Peer prediction. Further reading:
  • Section 27.4 of the AGT book (see general references).
  • Chapter 17 of the Parkes/Suen book (see general references).
• Lecture 18 (Wed Nov 30): Prediction markets. The Iowa Electronic Markets and continuous double auctions. The Policy Analysis Market and the Wisdom of Crowds. Market scoring rules and automated market-makers. Further reading:
  • Chapter 26 of the AGT book (see general references).
  • Chapter 18 of the Parkes/Suen book (see general references).
  • The Policy Analysis Market: A Thwarted Experiment in the Use of Prediction Markets for Public Policy (Hanson, 2007)
• Lecture 19 (Mon Dec 5): Behavioral economics. Time-inconsistent planning: procrastination, choice reduction, and undue obedience. Upper and lower bounds on cost ratios. Naive vs. sophisticated agents. Further reading:
  • Procrastination and Obedience (Akerlof, 1991)
  • Time-Inconsistent Planning (Kleinberg/Oren, 2014)
  • For tighter bounds, see Computational Issues in Time-Inconsistent Planning (Tang/Teng/Wang/Xiao/Xu, 2015)
  • Planning Problems for Sophisticated Agents with Present Bias (Kleinberg/Oren/Raghavan, 2016)
• Lecture 20 (Wed Dec 7): Fair division. The cut and choose protocol and envy-freeness. The Selfridge-Conway envy-free protocol for 3 players. Recent advances for 4 or more players. The rent division problem, and the maxmin envy-free solution. Further reading:
  • Quanta article about recent advances in envy-free cake-cutting (Klarreich, 2016)
  • A Discrete and Bounded Envy-free Cake Cutting Protocol for Any Number of Agents (Aziz/Mackenzie, 2016)
  • Which Is the Fairest (Rent Division) of Them All?, (Gal/Mash/Procaccia/Zick, 2016)
  • Fair division in practice: spliddit