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EECS 510
EECS 395/495  Algorithmic Mechanism Design
Spring 2010
Reference Text: Nisan, Roughgarden, Tardos, and Vazirani, Algorithmic Game Theory, Cambridge, 2007.
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Lectures: Tuesday and Thursday, 10:3011:50, Jacobs 166.
Instructor: Jason D. Hartline.
Announcement and Discussion Board: amd10 [at] googlegroups.com
Overview. This course combines game theory and
economics with algorithms. Algorithms studies at simple processes for
finding optimal or near optimal solutions to complicated optimization
problems. The output of an algorithm is often an allocation of
resources, e.g., which edges in a graph are in the shortest path or
which tasks can be scheduled on a computer server. Game theory and
economics study the outcome of systems of selfish agents, each
optimizing their own objective. Algorithmic mechanism design combines
the two fields and looks to find simple processes that result in good
allocations of resources even when the input to these processes are
provided by selfish agents who may try to game the system to get a
more favorable outcome for themselves. These settings of selfish
agents are especially relevant in computer networks such as the
Internet. Unfortunately, optimal mechanisms are almost never simple
enough to be implemented in practice, therefore, this course develops
techniques for designing and analyzing simple mechanisms that
approximate the optimal ones. From a computer science perspective,
this course can be views as adding game theory to standard settings
for approximation algorithms. From an economics perspective, this
course can be viewed as adding approximation to standard settings in
auction theory and mechanism design. Examples will be taken from
eBay, Internet routing, Internet broadcast, FCC spectrum auction, and
Internet advertising.
Prerequisites. Discrete math, probability, or
statistics, e.g., EECS
310 (Mathematical Foundations of Computer Science). Algorithms, e.g.,
EECS
336 (Introduction to Algorithms), is
recommended but not necessary.
Lecture Notes and Homework.
Course work. Students taking this course for credit
are required to complete light problemsolving homework assignments,
attend 5 research seminars on game theory (preferrably mechanism
design), write a one paragraph summary of each (focus on the main
results). Seminars can be found on the
Theory
Seminar calendar. In the second half of the course, the students
should work on and complete a project. Project ideas include (a)
writing a survey paper that combines and summarizes the results of
several related papers, (b) combining the models of two unrelated
papers and solving a research question related to the new model, (c)
using auction theory to simplify and better explain the results of a
CS paper that is not well grounded in auction theory. All homeworks
and projects may be done in pairs. Each student in the paper is
expected to contribute to every problem. There will be weekly reading
assignments from research papers and surveys and students are expected
to contribute to in class and online discussion of these
papers.
Grading.
50% homeworks (including minireports on 5 game theory seminars),
40% project, and
10% classroom participation.
Schedule.
 Week 1: Mechanism Design Overview
 Topics: Mechanism Design, Approximation, Vickrey auction, Vickrey with reserve, path auctions, singleminded combinatorial auctions, computation in MD, approximation in MD, surplus, profit.
 Reading: [1]
 Week 2: Equilibrium
 Topics: BayesNash equilibirum, dominant strategy equilibrium, singledimensional agents, revenue equivalence, revelation principle, incentive compatibility.
 Homework: [hw 1]
 Week 3: Computational Tractibility
 Topics: singledimensional mechanism design, VickreyClarkeGroves (VCG) mechanism, singleminded combinatorial auctions, LehmannO'CallahanShoham (LOS) mechanism, approximation mechanisms.
 Reading: [2] [3] [4] [5]
 Week 4: Computational Tractibility (cont)
 Topics: BIC blackbox reduction, computing payments.
 Reading: [6] [7] [8] [9]
 Week 5: Bayesian Profit Maximization
 Topics: Myerson auction, virtual values, ironing.
 Reading: [10]
 Week 6: Bayesian Profit Approximation
 Topics: Vickrey with reserve price approximation, prophet inequalities.
 Reading: [11]
 Week 7: Priorfree Profit Approximation
 Topics: BulowKlemperer, single sample approximation, priorfree benchmarks, digital goods, random sampling auction, profit extraction.
 Reading: [12] [13] [14]
 Week 9: Online Mechanisms
 Topics: online learning, multiarmed bandit, expert algorithm.
 Reading: [15] [16]
 Week 8: Frugality (priorfree profit approx without downwardclosed)
 Topics: spanning tree auctions, path auctions.
 Reading: [17] [18] [19]
 Week 10: Multidimensional Settings
 Topics: VCG, random sampling auction, sequential posted pricing.
 Reading: [13] [20]
Reading.
 2007 Nobel Prize in Economics [overview.pdf] [details.pdf]
 Nisan, Ronen, "Algorithmic Mechanism Design", 2001. [ps]
 Lehmann, O'Callaghan, and Shoham, "Truth Revelation in Approximately Efficient Combinatorial Auctions", 2002. [link]
 Briest, Krysta, Vocking, "Approximation techniques for utilitarian mechanism design", 2005.
[link]
 Babaioff, Lavi, Pavlov, "Singlevalue combinatorial auctions and algorithmic implementation in undominated strategies", 2009.
[link]
 Hartline, Lucier, "Bayesian Algorithmic Mechanism Design", 2010. [pdf]
 Babaioff, Blumrosen, Naor, Shapira, "Informational overhead of incentive compatibility", 2008.
[link]
 Babaioff, Sharma, Slivkins, "Characterizing Truthful MultiArmed Bandit Mechanisms", 2009.
[link]
 Babaioff, Kleinberg, Slivkins, "Truthful Mechanisms with Implicit Payment Computation", 2010.
[link]
 Myerson, "Optimal Auction Design", 1981. [link]
 Hartline, Roughgarden, "Simple versus Optimal Mechanisms", 2009. [pdf]
 Dhangwatnotai, Roughgarden, Yan, "Revenue Maximization with a Single Sample", 2010.
[pdf]
 Hartline, Karlin, "Chapter 13: Profit Maximization in Mechanism Design", in Algorithmic Game Theory. 2007.
[pdf]
 Hartline, Roughgarden, "Optimal Mechanism Design and Money Burning", 2008. [link]
 Parkes, "Chapter 16: Online Mechanisms", in Algorithmic Game Theory. 2007. [pdf]
 Blum, Hartline, "Nearoptimal Online Auctions", 2005.
[pdf]
 Hartline, "Lecture Notes on Frugality" [pdf]
 Karlin, Kempe, Tamir, "Beyond VCG: Frugality of Truthful Mechanisms", 2005. [pdf]
 Chen, Elkind, Gravin, Petrov, "Frugal Mechanism Design via Spectral Techniques", 2009. [link]
 Chawla, Hartline, Malec, Sivan, "Multiparameter Mechanism Design and Sequential Posted Pricing", 2010. [pdf]
Project Schedule.
 May 13th: proposals due.
 June 3: rough drafts due.
 June 10: final papers due.

