Mechanism design

Course content

This is a mathematically oriented course.

Mechanism design deals with institutions in environments with asymmetric information. While most of economics delas with the question how players act within a given environment, mechanism design asks: What kind of environment should a "designer" create if he wants to achieve a certain goal and which goals can realistically be achieved? For example: How should a government that is concerned about its citizens welfare design the tax schedule? How should a welfare maximizing planner organize a market? How to organize a revenue maximizing auction?

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

Depending on time and interest the course will have 2 or 3 parts.

The first part (based on chapter 23 in MasColell/Whinston/Greene) introduces the students to the classic results and methods of mechanism design: dominant strategy mechanism design  (revelation mechanism, 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 waste money. As this is not efficient, we turn from dominant strategy to Bayesian mechanism design an cover the expected externality mechanism, Bayesian incentive compatibility and establish the famous Myerson-Satterthwaite theorem which implies that fully efficient mechanisms do not exist in many economically relevant settings. 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 analyze how optimal mechanisms are affected if the setup differs from the classical mechanism design setup, e.g. 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) and discuss ”robust mechanism design” (what can we achieve if we cannot predict the beliefs of players?) and its applications.

If time permits, the course will have a third part. There are two possible topics for this part: Either using the mechanism design approach to analyze the question of efficient property rights and ownership. Alternatively, we go through recent, applied work of economists on so called matching markets (Gale-Shapley algorithm and top trading cycle algorithm) which deals with questions like matching students to schools/universities.

The content is subject to minor changes.

Learning outcome

At the end of this course, students can apply the classical tools of mechanism design and should be able to:

Knowledge:

  • know the material covered in the course; in particular knowledge of the logic behind the revelation principle, the Clarke-Groves mechanism, the expected externality mechanism, the monotonicity condition, the Myerson-Satterthwaite theorem, the Cremer-McLean mechanism and the agenda of robust mechanism design.

 

Skill:

  • analyze a given mechanism, find and illustrate its weaknesses and suggest alternatives based on the material treated in the course;
  • read, summarize, compare and comment on research papers that use the techniques covered in the course.

 

Competence:

  • explain the advantages and disadvantages of dominant vs. Bayesian mechanism design and the limitations to both approaches;
  • relate the different concepts and ideas covered in the course;
  • analyze new economic problems with mechanism design tools.

 

Mathematical superpower:

  • apply the envelope theorem and the skill to derive optimal Bayesian mechanisms in well behaved settings and apply matching algorithms to fully specified problems.

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

Tilman Börgers. An Introduction to the Theory of Mechanism Design, New York: Oxford University Press, 2015. only chapters 6.4 and 10

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.

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.

Milgrom, Paul and Segal, Ilya. "Envelope Theorems for Arbitrary Choice Sets."Econometrica 70.2 (2002): 583--601

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

Bergemann, Dirk, and Stephen Morris. "Robust mechanism design." Econometrica 73.6 (2005): 1771-1813.

Segal, Ilya, and Michael D. Whinston "Robust predictions for bilateral contracting with externalities." Econometrica 71.3 (2003): 757-791.

Segal,  Ilya, and Michael D. Whinston. ”Property rights.” Handbook of organizational economics (2012)

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

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

It is strongly recommended that Micro III has been followed prior to taking Mechanism Design. Mastering the material from the mathematics courses in the Bachelor program is very helpful.

Schedule:
3 hours lectures a week from week 6 to 20 (except holidays).


Timetable and venue:
The schedule for the semester spring 2018 will be available no later than 7th of November 2017

ECTS
7,5 ECTS
Type of assessment
Written examination, 7 days
take home exam. The exam assignment is given in English and must be answered in English.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
if chosen by the Head of Studies.
Criteria for exam assessment

Students are assessed on the extent to which they master the learning outcome for the course.

To receive the top grade, the student must be able to demonstrate in an excellent manner that he or she has acquired and can make use of the knowledge, skills and competencies listed in the learning outcomes.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 42
  • Preparation
  • 115
  • Exam
  • 49
  • English
  • 206