AØKK08201U  Mechanism design

Volume 2013/2014
Education
MSc in Economics
Content

Usually economists are interested in how individuals and organizations behave within their environment. For example, we ask how many hours an individual will work if he faces a certain tax system (the tax system and his employment possibilities are then the environment) or we ask how many units a firm will produce given the market environment (i.e. given the number of competitors, the mode of competition and the demand). Mechanism design goes one step further and asks: What kind of environment should a "designer" create if he wants to achieve a certain goal. For example, mechanism design asks: How should a government that is concerned about its citizens welfare design the tax schedule? In the second example, mechanism design could answer the question: How should a welfare maximizing planner organize a market?

Mechanism design is, therefore, an approach that can be used and has been used in many subfields of economics.

You can find a more thorough description of mechanism design on:

http://www.tseconomist.com/1/post/2013/01/-mechanism-design-theory-takuro-yamashita.html

 This course consists of three parts.

The first part (based on chapter 23 in MasColell/Whinston/Greene) introduces the students to the classic results and methods of mechanism design. After some introductory examples, dominant strategy mechanism design is treated: This covers the revelation mechanism and continues with the Gibbard-Satterthwaite theorem and the Groves-Clarke mechanism. We show that the designer can only achieve his objectives with the Groves-Clarke mechanism if he is willing to pay/receive own money to/from the players. As this is not always realistic, we turn from dominant strategy to Bayesian mechanism design to see whether we can get around this problem of "budget balance". The expected externality mechanism gives a positive answer to this question if players can be forced to participate in the mechanism.

We talk about some technical questions concerning incentive compatibility and use these new tools to establish the famous Myerson-Satterthwaite theorem which says that fully efficient trade cannot be achieved by any mechanism if players can opt not to participate. This naturally leads to the question which mechanism is most efficient. We study this question of optimal Bayesian mechanisms in several settings including bargaining, pricing, regulation and auctions.

 The second part applies and extends the concepts of the first part. The material is based mainly on published papers and small excerpts from other textbooks. We apply the standard model to market design in order to answer questions about innovation. Also the optimal design of an income tax scheme is discussed. We analyze how optimal mechanisms are affected if the setup differs from the classical mechanism design setup. Some of the following deviations from the standard setup are considered: Agents exert externalities on each other (e.g. if Pakistan sells nuclear weapons to North Korea, US security is affected), agents' information is correlated (e.g. if a government sells drilling rights either all companies will value the right highly if there is a lot of oil and not so highly if there is none), agents' information is multi-dimensional (e.g. buyers value design as well as functionality), agents can search for more information before signing a contract.

 The third part deals with recent, applied work of economists on so called matching markets. The classic Gale-Shapley algorithm is introduced. We then use this tool (and some related tools like the top cycle algorithm) to think about the design of the following markets: Matching students to schools/universities and organizing kidney donations. If there is time, we might cover another market like the market for physicians or power markets.

 Students are expected to do some assigned reading and smaller exercises between classes.

Learning Outcome

At the end of this course, students can apply the classical tools of mechanism design. Students can explain the advantages and disadvantages of dominant vs. Bayesian mechanism design and the limitations to both approaches. Students understand the logic behind the revelation principle, the Clarke-Groves mechanism, the expected externality mechanism, the envelope theorem and monotonicity condition as well as the Myerson-Satterthwaite theorem. Students can derive optimal Bayesian mechanisms in well behaved settings and apply matching algorithms to fully specified problems. Very good students can analyze a given matching mechanism, find and illustrate its weaknesses and suggest alternatives based on mechanisms treated in the course.  Students can read, summarize, compare and comment on research papers that use the techniques covered in the course.

 

Reading list:

Mas-Colell, Andreu, Michael Dennis Whinston, and Jerry R. Green. Microeconomic theory. New York: Oxford University Press, 1995. only chapter 23

the list of papers and textbook excerpts is tentative: some papers will probably be skipped while few other papers might be assigned during the course. Most papers do not have to be read completely and precise instructions (which pages) will be given in the course.

Boone, Jan, and Jacob Goeree. "Optimal market design." (2010); working paper

Salanie, Bernard. The economics of taxation. MIT press, 2003. only ch. 4.1-4.2

Diamond, Peter A. "Optimal income taxation: an example with a U-shaped pattern of optimal marginal tax rates." The American Economic Review (1998): 83-95.

Moldovanu, Benny, and Aner Sela. "The optimal allocation of prizes in contests." The American Economic Review (2001): 542-558.

Jehiel, Philippe, Benny Moldovanu, and Ennio Stacchetti. "How (not) to sell nuclear weapons." The American Economic Review (1996): 814-829.

Cremer, Jacques, and Richard P. McLean. "Full extraction of the surplus in Bayesian and dominant strategy auctions." Econometrica (1988): 1247-1257 

Martimort, David, and Lars Stole. "Market participation in delegated and intrinsic common‐agency games." The Rand Journal of Economics 40.1 (2009): 78-102.

Bolton, Patrick, and Mathias Dewatripont. Contract theory. The MIT Press, 2005. only ch. 6.1

Cremer, Jacques, and Fahad Khalil. "Gathering information before signing a contract." The American Economic Review (1992): 566-578.

Gale, David, and Lloyd S. Shapley. "College admissions and the stability of marriage." The American Mathematical Monthly 69.1 (1962): 9-15.

Roth, Alvin E. "The economist as engineer: Game theory, experimentation, and computation as tools for design economics." Econometrica 70.4 (2002): 1341-1378.

Roth, Alvin E. "What Have We Learned from Market Design?." The Economic Journal 118.527 (2008): 285-310.

Roth, Alvin E., Tayfun Sönmez, and M. Utku Ünver. "Kidney exchange." The Quarterly Journal of Economics 119.2 (2004): 457-488.

Abdulkadiroğlu, Atila, and Tayfun Sönmez. "School choice: A mechanism design approach." The American Economic Review (2003): 729-747.

Active knowledge of the material in Microeconomics C and the compulsory Math courses of the Bachelor is required. It is not required but helpful to take (or to have taken) one or more related courses like “Contract Theory and the Economics of Organization”, “Auctions” and “Game Theory”.
3 hours of lectures per week for 14 weeks.
Credit
7,5 ECTS
Type of assessment
Written examination, 49 hours
Type of assessment: The exam is a one week take home assignment at the end of the course. In order to be allowed to take the exam, students have to pass a midterm assignment which is also a one week take home assignment.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
100 % censurship
Exam period
Will be updated before the start of the semester
Re-exam
Same as ordinary. But if only a few students have registered for the re-exam, the exam might change to an oral exams with a synopsis to be handed in. This means that the examination date also will change.
Criteria for exam assesment
The Student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
  • Category
  • Hours
  • Lectures
  • 42
  • Preparation
  • 115
  • Exam
  • 49
  • Total
  • 206