Course 2019-2020 a.y.

30419 - COMPUTATIONAL MICROECONOMICS - MODULE 2 (MECHANISM DESIGN)

Department of Economics

Course taught in English
Go to class group/s: 25
BEMACS (8 credits - II sem. - OB  |  SECS-P/01)
Course Director:
NENAD KOS

Classes: 25 (II sem.)
Instructors:
Class 25: NENAD KOS


Suggested background knowledge

To feel comfortable in this course, students should be familiar with basic game theory concepts.

Mission & Content Summary

MISSION

The course provides an introduction to the theory of incentives and contracts which has become an indispensable part of economics since its inaugural steps in the 1960s. Students are introduced to fundamental tools of theory accompanied with applications and examples varying from spectrum auctions to college admissions. The course aims at developing abstract strategic thinking needed for understanding many economic environments and situations.

CONTENT SUMMARY

  • Adverse Selection.
  • Market for lemons.
  • Second degree price discrimination.
  • Delegation.
  • Audit mechanisms and costly state verification.
  • Ex ante contracting.
  • Limited liability.
  • Insurance markets.
  • Regulation.
  • Uuctions.
  • Signaling Models.
  • Moral Hazard.
  • Matching.

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Identify an incomplete information environment.
  • Distinguish between moral hazard and adverse selection.
  • Define a game that models the interaction under consideration.
  • Illustrate a contract that alleviates the underlying conflict of interest.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Derive or compute equilibria of economic mechanisms.
  • Design optimal selling mechanisms in various environments.
  • Construct a contract that aligns incentives.
  • Identify whether a matching algorithm is stable.
  • Propose a matching algorithm that satisfies certain criteria.
  • Critically asses a strategic interaction and model it.

Teaching methods

  • Face-to-face lectures
  • Exercises (exercises, database, software etc.)

DETAILS

In addition to face-to-face lectures, the students are given exercises to solve. Those exercises allow students to apply the analytical tools learned during the course.


Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
   x x

ATTENDING AND NOT ATTENDING STUDENTS

The students’ assessment is based on written exam(s).

  • The written exam(s) consists of exercises and open questions aimed at assessing students’ ability to apply the analytical tools illustrated during the course.
  • Students can take a partial written exam and the final written exam at the end of the course. In this case, the weight is 50% for the partial exam and 50% for the end of term exam. Alternatively, students can take a written exam that accounts for 100% of the final grade.

Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

A textbook has not been chosen yet but are available on the online platform before the start of the course.

Last change 31/05/2019 08:56