Jason Hartline writes:
Please share the following announcement:
*** Online
Self-study on Mechanism Design and Approximation ***
To enroll: go to
course page on Piazza and enroll as a student.
Synopsis. This course is a self-study course based on the
manuscript "Mechanism Design and Approximation" which is based on a
graduate course that has been developed at Northwestern over the past five
years. Over the fall quarter we will work through roughly one chapter per week.
The week will start with students reading and discussing the material of the
chapter and it will conclude with students working together to solve and write
up solutions to the chapter exercises.
The textbook is in final draft and your comments and suggestions will
help improve the book for future students.
Excerpt from Chapter 1: Our world is an interconnected
collection of economic and computational systems. Within such a system,
individuals optimize their actions to achieve their own, perhaps selfish,
goals; and the system combines these actions with its basic laws to produce an
outcome. Some of these systems perform well, e.g., the national residency
matching program which assigns medical students to residency programs in
hospitals, e.g., auctions for online advertising on Internet search engines;
and some of these systems perform poorly, e.g., financial markets during the
2008 meltdown, e.g., gridlocked transportation networks. The success and
failure of these systems depends on the basic laws governing the system.
Financial regulation can prevent disastrous market meltdowns, congestion
protocols can prevent gridlock in transportation networks, and market and
auction design can lead to mechanisms for allocating and exchanging goods or
services that yield higher profits or increased value to society.
This text focuses on a combined computational and
economic theory for the study and design of mechanisms. A central theme will be
the tradeoff between optimality and other desirable properties such as
simplicity, robustness, computational tractability, and practicality. This
tradeoff will be quantified by a theory of approximation which measures the
loss of performance of a simple, robust, and practical approximation mechanism
in comparison to the complicated and delicate optimal mechanism. The theory
provided does not necessarily suggest mechanisms that should be deployed in
practice, instead, it pinpoints salient features of good mechanisms that should
be a starting point for the practitioner.
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