AØKK08201U  Mechanism design

Volume 2017/2018
Education

MSc programme in Economics – elective course

The course is an admission requirement for the 5+3 PhD Programme in Economics.

 

The PhD Programme in Economics at the Department of Economics - elective course with resarch module (PhD students must contact the study administration and the lecturer in order to write the research assignment)

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.

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 which covers information design.

The content is subject to minor changes.

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

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.

  • apply the envelope theorem and the skill to derive optimal Bayesian mechanisms in well behaved settings .

 

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.

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.

(subject to minor changes and additionsregarding information design)

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 21 (except holidays).

The overall schema for the Master can be seen at
https:/​/​intranet.ku.dk/​ECONOMICS_MA/​COURSES/​COURSECATALOGUE-F18/​Pages/​default.aspx

Timetable and venue:
To see the time and location of lectures please press the link/links under "Se skema" (See schedule) at the right side of this page (E means Autumn, F means Spring).

You can find the similar information partly in English at
https:/​/​skema.ku.dk/​ku1718/​uk/​module.htm
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-F18; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”
Credit
7,5 ECTS
Type of assessment
Written examination, 7 days
individual take home exam. It is not allowed to collaborate on the assignment with anyone. The exam assignment is given in English and must be answered in English.
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Exam registration requirements

A one week take-home assignment (that can be done in groups) must be passed in order to be admitted to the final exam. The plagiarism rules must be complied and please be aware of the rules for co-writing assignemnts.

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Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
The course can be selected for external assessment.
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Exam period

The take-home exam takes place from June 15, 2018 at 10 a.m. to June 22 at 10 a.m.

 

For enrolled students more information about examination, rules, exam schedule etc. is available at the intranet for master students (UK) and master students (DK).

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Re-exam

The take-home reexam takes place from August 27, 2018 at 10 a.m. to September 3 at 10 a.m.

 

If only a few students have registered for the written re-exam, the reexam might change to an oral exam including the date, time and venue for the exam, which will be informed  by the Examination Office.

 

For enrolled students more information about reexamination, rules, schedule etc. is available at the intranet for master students (UK) and master students (DK).

Criteria for exam assesment

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

To receive the top grade, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.

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