**Summary: **The
course covers classic and state-of-the-art results on computational and
game-theoretic questions related to electronic marketplaces.

**Instructor:** Prof.
Tuomas Sandholm (sandholm@cs.cmu.edu)

**This page:** http://www.cs.cmu.edu/~sandholm/cs15-892F09/cs15-892.htm

**Class times:** TuTh 10:30-11:50 am, in Gates Hillman
Center (GHC) room 4211.

**Instructor’s office hour:** Tu noon-1 pm, GHC 9205.

**Reading materials:**

There is no book that adequately covers all of the covered
topics. However, we will be using the
book **Combinatorial Auctions** (MIT Press 2006); each student should acquire that
book. In addition, we will use a
collection of readings from recent research papers, chapters that are about to
appear in other books, and slides by the instructor. Some of these papers are brand new, and have
not even appeared publicly yet.

**Format: **

- Lectures by Prof. Sandholm. In addition, guest lectures by outside experts.
- In advance of each lecture, each student is expected to download the paper and the slides for that lecture from the course web page, to print them, and to read the paper carefully before the lecture. There may be surprise quizzes on the readings.
- 3 homework assignments, which will mostly consist of analytical questions.
- Final
project. Each student is expected
to complete an original final project (alone, or for a larger project, in
a pair). The students are free to
pick the topics of the final projects, but they have to be related to the
topic of the course. The projects
can involve analytical work, computer experiments, building a prototype,
etc. The project might also involve
applying some of the techniques learned in this course to the student’s
research in another area. 2-page
project plans are due on October 27
^{th}by the beginning of class. Students are expected to present their final projects in the last couple of lectures. The writeups of the final projects are due on December 3^{rd}(i.e., last class) in the beginning of class. These are hard deadlines.

**Evaluation:** Participation 10%, homework assignments
and quizzes 40%, final project 50%. The
course must be taken for credit: there is no audit option.

**Prerequisites:** Knowledge of algorithms and
computational complexity. Knowledge of
basic probability theory. This is a
full-semester course given by the Computer Science Department primarily to
Ph.D. candidates. However, others may also take it with the instructor's
permission.

Here is the set of topics that we will cover, and a list of papers for each topic. Only some of the papers will be covered (the papers most likely to be covered are marked in red). THIS LIST WILL BE UPDATED DYNAMICALLY DURING THE SEMESTER.

General review articles

·
Computing
in Mechanism Design. by T.
Sandholm. In the *New Palgrave Dictionary in Economics, 2008*.

·
Computational Mechanism Design (PDF) In
Lecture notes of Tutorials at *10th Conf.
on Theoretical Aspects of Rationality and Knowledge (TARK-05*), Institute of
Mathematical Sciences, University of Singapore, 2008.

· Combinatorial Auctions (a survey) by Blumrosen and Nisan. Chapter 11 of the book Algorithmic Game Theory.

·
“Auctions:
Theory” by Lawrence Ausubel. To
appear in the *New Palgrave Dictionary of
Economics*.

Basics of mechanism design

·
Nisan, N. 2007. Introduction to Mechanism
Design (for Computer Scientists). Chapter 9 of
the book Algorithmic Game Theory.

·
Mas-Colell, Whinston & Green. Microeconomic theory. Chapter 23.

· Review article [Parkes 01] [bibliography for this article]

· Review article [Maskin & Sjostrom 01] (Does not cover dominant strategy implementation; first 80% is for complete information environments; focuses on implementation that does not have bad equilibria also).

· Osborne and Rubinstein. A Course in Game Theory, MIT Press, 1994.

In designing (exact or approximate) mechanisms, it can help to know what mechanism families are incentive compatible, and what is (im)possible:

· Truthful germs are contagious: A local to global characterization of truthfulness. A. Archer and R. Kleinberg, EC-08.

· A Modular Approach to Roberts' Theorem. Dobzinski and Nisan. SAGT-09.

·
Two Simplified
Proofs for Roberts' Theorem. By Ron
Lavi, Ahuva Mu'alem, and Noam Nisan. *Social Choice and Welfare*,
32, pp. 407 -- 423, 2009.

·
The Limits of Ex Post
Implementation. Philippe Jehiel, Moritz Meyer-ter-Vehn,
Benny Moldovanu,
and William R. Zame, *Econometrica*, 2006.

·
Ex
post implementation. Dirk Bergemann
and Stephen Morris. *Games and Economic Behavior* 63 (2008) 527-566.

· Multi-Unit Auctions with Budget Limits. Shahar Dobzinski, Ron Lavi, and Noam Nisan. FOCS-08.

· On Characterizations of Truthful Mechanisms for Combinatorial Auctions and Scheduling. Shahar Dobzinski and Mukund Sundararajan. EC-08. See also an addendum.

· Paths, Cycles and Mechanism Design , by Vohra, 2007.

·
Weak
Monotonicity characterizes deterministic dominant strategy implementation
by S. Bikhchandani, S. Chatterji, R. Lavi, A. Mu'alem, N. Nisan, and A.
Sen. *Econometrica*, 74(4),
pp. 1109 -- 1132, 2006. See also
the supplementary
material for this paper.

· Characterization of Revenue Equivalence, by B. Heydenreich, Rudolf Muller, Marc Uetz, and Rakesh Vohra, 2007.

· Characterizing Dominant Strategy Mechanisms with Multi-Dimensional Types. [Gui, Mueller, Vohra 2004 draft]

· Truthful Mechanism Design for Multi-Dimensional Scheduling via Cycle Monotonicity. By Ron Lavi and Chaitanya Swamy In EC-07.

Auctioning a single item

·
Profit Maximization in
Mechanism Design. By Hartline and
Karlin. Chapter 13 of the book Algorithmic Game Theory. Read Sections 13.1-13.2.

·
*Bayesian Optimal No-deficit Mechanism Design*.
By Shuchi Chawla, Jason Hartline, Uday Rajan and R. Ravi, WINE'06.

· Review article [Wolfstetter 94]

· Advanced material on non-private value auctions [Dasgupta & Maskin QJE-00], [Jehiel & Moldovanu 1998]

Optimal (offline) clearing of multi-item and/or multi-unit markets

· Optimal winner determination algorithms. Chapter 14 of the Combinatorial Auctions book.

· Lehmann, D., Mueller, R., and Sandholm, T. 2006. The Winner Determination Problem. Chapter 12 of the Combinatorial Auctions book.

·
Sandholm, T. 2007. Expressive Commerce and Its Application to Sourcing: How We
Conducted $35 Billion of Generalized Combinatorial Auctions. *AI
Magazine,* 28(3), 45-58.

· A Kernel Method for Market Clearing. By Sebastien Lahaie. IJCAI-09.

· Bidding and allocation in combinatorial auctions [Nisan EC-00]

· CABOB: A fast optimal algorithm for combinatorial auctions [Sandholm et al IJCAI-01]

·
Winner determination in
combinatorial auction generalizations. [Sandholm et al AAMAS-02]

· Side constraints and non-price attributes in markets. [Sandholm et al IJCAI-01 workshop: Distributed constraint reasoning]

· Computational complexity of clearing exchanges with supply-demand curves [Sandholm-Suri ISAAC-01]

· Computational complexity of clearing multi-unit auctions [Sandholm-Suri IJCAI-01]

· Fast Vickrey-Clarke-Groves computation in networks [Suri-Hirschberg FOCS-01]

Expressiveness of mechanisms

·
Benisch, M., Sadeh, N.,
and Sandholm, T. A Theory of Expressiveness in Mechanisms. AAAI-08.

·
Milgrom, P. 2009. Simplified
mechanisms with applications to Sponsored Search and Package Auctions,
*Games and Economic Behavior*, forthcoming.

Multi-stage market designs with preference elicitation

·
Preference elicitation
in combinatorial auctions [Sandholm-Boutilier
Chapter 10 in the book “Combinatorial Auctions”, 2006]

· Iterative combinatorial auctions (iBundle etc.) [Parkes’s chapter in the forthcoming book “Combinatorial Auctions” 2006] [OLD: Parkes ACM-EC-99, AAAI-00a, AAAI-00b]

·
The Communication Requirements of Combinatorial
Allocation Problems. By Ilya Segal,
Chapter 11 of the book *Combinatorial
Auctions*, 2006.

·
Ascending Price Vickrey Auctions for
General Valuations (PDF)
*Journal of Economic Theory* **132**, 2007.

· Exponential Communication Inefficiency of Demand Queries by N. Nisan and I. Segal. TARK 2005.

Multi-Item Vickrey-Dutch Auctions(PDF) Draft

· Communication complexity of approximate set packing and covering [Nisan 01]

· Linear programming and Vickrey auctions [Vohra et al. draft 01]

· Dynamic auction for multiple distinguishable items [Ausubel 00] (slides from Nisan’s course)

·
AkBA [Wurman et al ACM-EC-00]

·
Auction Design with Costly Preference
Elicitation (PDF) In Annals of Mathematics and AI **44**, 2005,
pages 269-302.

Automated mechanism design

Work on the general problem

·
Sandholm, T., Conitzer, V., and Boutilier,
C. 2007.
Automated
Design of Multistage Mechanisms. In
Proceedings of the *International Joint
Conference on Artificial Intelligence (IJCAI)*.

·
Conitzer, V. and Sandholm, T. 2007. Incremental
Mechanism Design. In Proceedings of
the *International Joint Conference on
Artificial Intelligence (IJCAI)*.

·
Conitzer, V. and Sandholm, T. 2004. Self-Interested
Automated Mechanism Design and
Implications for Optimal Combinatorial Auctions. In
Proceedings of the *ACM Conference on Electronic Commerce (EC), pp. 132-141*.

·
Conitzer, V. and Sandholm, T. 2004. An Algorithm
for Automatically Designing Deterministic Mechanisms without Payments. In Proceedings of the *International Joint Conference on Autonomous Agents and Multiagent
Systems (AAMAS)*, pp. 128-135,

·
Conitzer, V. and Sandholm, T. 2003. Applications
of automated mechanism design. In Proceedings of the *UAI Bayesian
Modeling Applications Workshop*,

· Conitzer, V. and Sandholm, T. 2003. Automated mechanism design with a structured outcome space. Draft.

·
Conitzer, V. and Sandholm,
T. 2002. Complexity of Mechanism Design. In Proceedings of the *18th Conference on Uncertainty in Artificial Intelligence
(UAI)*, August 1-4,

·
Conitzer, V. and Sandholm, T. 2003. Automated
Mechanism Design: Complexity Results Stemming from the Single-Agent Setting.
In Proceedings of the *International Conference on Electronic
Commerce (ICEC)*,

Work on auctions, other selling mechanisms, etc.

·
Likhodedov, A. and
Sandholm, T. 2005. Approximating Revenue-Maximizing Combinatorial Auctions.
In Proceedings of the *National Conference on Artificial
Intelligence (AAAI)*,

·
Likhodedov, A. and Sandholm, T. 2004. Methods
for Boosting Revenue in Combinatorial Auctions. In
Proceedings of the *National Conference on
Artificial Intelligence (AAAI)*, pp. 232-237, San Jose, California.

·
Sandholm, T. and Gilpin, A. 2003. Sequences of
Take-It-or-Leave-It Offers: Near-Optimal Auctions without Full Valuation
Revelation. In Proceedings of the *AAMAS workshop on Agent-Mediated
Electronic Commerce (AMEC V)*,

·
Likhodedov, A. and Sandholm, T. 2004. Mechanism
for Optimally Trading Off Revenue and Efficiency in Multi-unit Auctions. Short
paper in proceedings of the *ACM Conference on Electronic Commerce*. Extended
version. (Early version in Proceedings of the *AAMAS
workshop on Agent-Mediated Electronic Commerce (AMEC V)*,

· On approximating optimal auctions [Ronen EC-01]

·
R. Jurca and B. Faltings. Collusion
Resistant, Incentive Compatible Feedback Payments. *Proceedings of the
ACM Conference on E-Commerce (EC'07)*, pp. 200-209,

·
R. Jurca and B.
Faltings. Minimum
Payments that Reward Honest Reputation Feedback. *Proceedings of the ACM
Conference on Electronic Commerce (EC2006)*, pp. 190-199, Ann Arbor,
Michigan, June 11-15 2006. [PS]

Auction and exchange design without priors

· Mechanism Design via Machine Learning. JCSS, 2008.

· Competitive generalized auctions [Fiat, Goldberg, Hartline, Karlin]

· Truthful and Competitive Double Auctions [Deshmukh, Goldberg, Hartline, Karlin]

·
Pricing without demand curves [Segal, *American Economic Review*]

· Market research and Market Design [Vohra & Baliga]

·
[OLD

Incentive-compatible (IC) approximation by the auctioneer

·
Computationally-Efficient Approximation Mechanisms. Chapter by Lavi in the
book Algorithmic Game Theory.

· Algorithmic mechanism design [Nisan-Ronen GEB 2001]

· Truth revelation in rapid approximately efficient combinatorial auctions [Lehman-O’Callaghan-Shoham JACM-02]

· Truthful and Near-optimal Mechanism Design via Linear Programming, by Ron Lavi and Chaitanya Swamy (early version in FOCS-05).

· On the Power of Randomization in Algorithmic Mechanism Design, FOCS-09. Shahar Dobzinski and Shaddin Dughmi.

· Truthful Randomized Mechanisms for Combinatorial Auctions by S. Dobzinski, N. Nisan, and M. Schapira. STOC 2006.

· Impersonation-Based Mechanisms, By Moshe Babaioff, Ron Lavi, and Elan Pavlov, AAAI-06.

· Two Randomized Mechanisms for Combinatorial Auctions by S. Dobzinski, APPROX-08.

· Limitations of VCG-based Mechanisms by S. Dobzinski and N. Nisan. STOC 2007.

· Mechanisms for Multi-Unit Auctions by S. Dobzinski and N. Nisan. EC-07.

· Algorithms for selfish agents [Nisan 01]

· Computationally feasible VCG mechanism [Nisan-Ronen 00]

· Algorithms for rational agents [Ronen] – section 7 (if not subsumed by Ronen’s EC-01 paper)

· Mechanism design for resource-bounded agents [Monderer-Tennenholtz-Kfir Dahav ICMAS-00]

Bidding agents with hard valuation problems

·
Efficient Metadeliberation
Auctions. Cavallo and Parkes,
AAAI-08.

·
Larson, K. and Sandholm,
T. 2005. Mechanism Design and Deliberative Agents. Proceedings of the *International Joint Conference
on Autonomous Agents and Multi-Agent Systems (AAMAS)*.

·
Larson, K. and Sandholm,
T. 2001. Costly Valuation Computation in Auctions. In Proceedings of the** ***Theoretical
Aspects of Reasoning about Knowledge (TARK)*.

·
Larson, K. and Sandholm, T. 2001. Computationally
Limited Agents in Auctions. In
Proceedings of the** ***International
Conference on Autonomous Agents, Workshop on Agent-based Approaches to B2B*.

· Issues in computational Vickrey auctions [Sandholm IJEC-00 (originally ICMAS-96)]

· Valuation complexity explains last-minute bidding [Eric Rasmusen draft-03]

· Computationally feasible VCG mechanism [Nisan-Ronen 00] (This paper contains the second-chance mechanism.)

·
Ben-Sasson,
E., Kalai, A., and Kalai E. An
Approach to Bounded Rationality. *NIPS*.
(This is not really about valuation calculation, but has some results
about strategies with costs.)

Avoiding manipulation using computational complexity; Mechanism design for computationally limited agents; Non-truth-promoting mechanisms

·
Othman, A. and Sandholm,
T. 2009. Better
with Byzantine: Manipulation-Optimal Mechanisms. In
Proceedings of the *Symposium on
Algorithmic Game Theory (SAGT)*.

·
Conitzer, V. and Sandholm, T. 2003. Computational Criticisms of the Revelation
Principle. In Proceedings of the *Workshop on Agent Mediated **Electronic
Commerce (AMEC V)*. Newer draft.

·
Conitzer, V., Sandholm, T., and Lang, J.
2007.
When Are Elections with Few Candidates Hard to Manipulate? *Journal
of the ACM*, 54(3).

·
Conitzer, V. and
Sandholm, T. 2003. Universal Voting Protocol Tweaks to Make Manipulation Hard. In Proceedings of the *International
Joint Conference on Artificial Intelligence (IJCAI).*

·
Conitzer, V. and
Sandholm, T. 2006. Nonexistence of Voting Rules That Are Usually Hard to Manipulate. In Proceedings of the *National Conference on Artificial Intelligence (AAAI)*.

· Ariel D. Procaccia and Jeffrey S. Rosenschein. 2007. Junta Distributions and the Average-Case Complexity of Manipulating Elections. Journal of Artificial Intelligence Research. Volume 28, pages 157-181. [download]

· E. Friedgut, G. Kalai, and N. Nisan. 2007. Elections can be Manipulated Often. Draft.

Online mechanisms for the auctioneer

·
Online Mechanisms, by David Parkes. Chapter 16 of the book __Algorithmic Game Theory__.

· Self-Correcting Sampling-Based Dynamic Multi-Unit Auctions. By Florin Constantin and David C. Parkes. Bonn workshop on mechanism design, 2009.

·
__Self-Correcting
Sampling-Based Dynamic Multi-Unit Auctions.__ B

·
__An Efficient Dynamic Mechanism__, Susan
Athey and Ilya Segal, 2007.

·
__Dynamic Marginal Contribution Mechanisms.__ Dirk Bergemann and Juuso Välimäki. Mimeo, 2007.

·
__Efficiency Levels in Sequential Auctions with Dynamic
Arrivals__. Lavi and
Segev. Draft 2009.

·
__Prompt Mechanisms for Online Auctions__. Richard Cole, Shahar Dobzinski, and Lisa
Fleischer. SAGT-08.

·
Online
algorithms for clearing exchanges __[Blum-Sandholm-Zinkevich
JACM, 2006]__

·
An
Ironing-Based Approach to Adaptive Online Mechanism Design in Single-Valued
Domains (__PDF__)

· Online auctions with reusable goods [Hajiaghayi et al. EC-05]

· Reducing truth-telling online mechanisms to online optimization [Awerbuch et al. STOC-03]

· Online learning in online auctions [Blum et al. SODA-03]

· Pricing WiFi at Starbucks: Issues in online mechanism design [Friedman & Parkes EC-03]

·
Adaptive
limited-supply online auctions __[Hajiaghayi et al. EC-04]__

·
Approximately
efficient online mechanism design __[Parkes, Singh and Yanovsky NIPS-04]__

· An MDP-Based Approach to Online Mechanism Design, D. C. Parkes and S. Singh, Proc. NIPS'03, 2003.

· The price of truth: frugality in truthful mechanisms [Talwar STOC-03]

·
MDPOP:
Faithful Distributed Implementation of Efficient Social Choice Problems (__PDF__) In the Proc. 5th
International Joint Conference on Autonomous Agents and Multiagent Systems
(AAMAS'06), pages 1397-1404, 2006.

·
Specification
Faithfulness in Networks with Rational Nodes (__PDF__) In the Proc. 23rd ACM
Symp. on Principles of Distributed Computing (PODC'04), St. John's, Canada,
pages 88-97, 2004.

·
__Google's auction for TV ads__, N. Nisan et
al., ICALPS 2009

·
Optimize-and-Dispatch
Architecture for Expressive Ad Auctions (__PDF__) In the Proceedings of
First Workshop on Sponsored Search Auctions, 2005.

· OTHER PAPERS, e.g., on bid optimization

·
Brandt,
F. and Sandholm, T. 2005. __Decentralized Voting with Unconditional Privacy.__ AAMAS-05.

·
Practical
Secrecy-Preserving, Verifiably Correct and Trustworthy Auctions (__PDF__) In Electronic Commerce
Research and Applications, 2007, to appear.

·
Cryptographic
Securities Exchanges (__PDF__)
In Proc. *International
Conference on Financial Cryptography and Data Security*, 2007.

Exotic
contract types (“It’s not the figures lying, it’s the liars figuring”)

·
Leveled
commitment contracts and strategic breach __[Sandholm-Lesser GEB 2001]__

·
Algorithms
for optimizing leveled commitment contracts __[Sandholm-Sikka-Norden IJCAI-99]__

Worst-case
Nash equilibrium (what’s the price of anarchy?)

·
Entire
section on this in the book __Algorithmic
Game Theory__.

·
__[Koutsoupias & Papadimitriou]__

·
How
bad is selfish routing? __[Roughgarten & Tardos]__

· Basics: core, Shapley value, Sandholm’s network routing example, iterative payoff division schemes

·
Coalition
structure generation __[Sandholm et al AIJ-99]__

·
Coalition
formation among agents whose computation is costly __[Sandholm&Lesser AIJ-97]__

·
Sharing
the cost of multicast __[Feigenbaum et al]__

·
Coalition-proof implementation via LP
duality __[Vazirani et al]__

·
Safe
exchange planner __[Sandholm-Ferrandon ICMAS-00]__

· Defection-free exchange mechanisms for information goods [Yokoo ICMAS-00]

· Cryptographic safe exchange techniques

Reputation
systems (these systems are prevalent - e.g., eBay - but they are all
manipulable)

Speculator
agent for general equilibrium markets __[Sandholm-Ygge 99]__

Bargaining
with deadlines __[Extended version of Sandholm-Vulkan AAAI-99]__

·
Mehta,
T. Roughgarden, and M. Sundararajan, * Beyond Moulin Mechanisms*, EC '07.

·
Utku
Unver. __Dynamic Kidney Exchange__, *Review of Economic Studies*,
forthcoming.

·
Al
Roth’s __Game Theory, Experimental Economics, and Market Design Page__.

- Sep 8: Course organization. Introduction. Automated negotiation in different
stages of an ecommerce transaction.
Utility theory. Measures of
how good an interaction mechanism is.
Slides. Read this
brief overview paper from the
Palgrave Dictionary of Economics 2008 regarding the topics in this course.
- Sep 10: Extensive form and matrix form representations
of games. Dominant strategy
equilibrium. Nash equilibrium. Mixed strategy Nash equilibrium. Subgame perfect equilibrium and credible
threats. Software as a commitment device.
Bayesian games. Bayes Nash
equilibrium. Perfect Bayesian equilibrium. Sequential equilibrium. Strong Nash equilibrium. Coalition-proof Nash equilibrium. Ex post equilibrium. Slides.
- Sep 15: Guest lecture by Abe Othman: Prediction markets. Read Chapter 26 of Algorithmic Game Theory.
- Sep 17: Guest lecture by Abe Othman: Prediction markets 2.
- Sep 22: NO CLASS: Gates Hillman Complex Opening Ceremony.
- Sep 24: Social choice theory (preference aggregation). Binary protocol. Plurality rule. Borda count. Paradoxes in social choice. Arrow’s impossibility theorem (weak version). Arrow’s impossibility theorem (strong version). Voting protocols that circumvent Arrow’s impossibility. Read Sections 9.1-9.2.3 of Algorithmic Game Theory. Slides.
- Sep 29: Mechanism design. Revelation principle. Dominant strategy implementation. Gibbard-Satterthwaite impossibility theorem. Read Sections 9.2.4-9.4.3 of Algorithmic Game Theory. Slides.
- Oct 1: Groves mechanism for quasilinear environments. Clarke tax. When do these work? When are these the only mechanisms that work? Budget imbalance. Collusion. Read rest of Chapter 9 of Algorithmic Game Theory. Homework1 posted.
- Oct 6: More on characterizing dominant-strategy implementation in quasilinear environments. Bayes-Nash implementation in incomplete information environments. Expected externality mechanism for quasilinear environments. Budget balance. Participation constraints. Myerson-Satterthwaite impossibility for exchanges. Additional optional readings: review of network view of incentive compatibility, virtual Bayesian implementation. Slides on characterising dominat-strategy implementability. Slides on Bayes-Nash implementation.
- Oct 8: Guest lecture by David Parkes (Harvard): Dynamic Knapsack Auctions through Computational Ironing. Paper to read in advance.
- Oct 13: Guest lecture by Michael Benisch: Theory of Expressiveness in Mechanisms. Slides.
- Oct 15: Sponsored search auctions. Guest lecture by Michael Benisch: Applying Expressiveness Theory to Evaluate and Improve Efficiency in Sponsored Search Auctions. Paper to read in advance. Guest lecture by Amin Sayedi: Expressive Auctions for Externalities in Online Advertising.
- Oct 20: Homework 1 due by the beginning of class. Auctioning a single item. Private vs. correlated vs. common value auction settings. English, Japanese, Dutch, first-price sealed-bid, and second-price sealed bid (Vickrey) auction mechanisms. Revenue equivalence theorem. Slides.
- Oct 22: More on auctioning a single item. Revenue non-equivalence. Revenue-maximizing (Myerson) auction. Winner’s curse. Asymmetric information.
- Oct 27: Project proposals due (by email to Prof. Sandholm) by the beginning of class. More on auctioning a single item. Single-crossing property and its implications. Linkage principle. Collusion. Auctions where bidders can invest effort/computation to determine their own valuations. Homework 2 posted.
- Oct 29: Auctions with multi-dimensional signals. Last-minute bidding (sniping) and its strategic implications. Mobile bidder agents.
- Nov 3: Multi-unit auctions. Uniform-price auction. Demand reduction lie. Multi-unit Vickrey auctions. Bidding languages (price bids; PQ bids; PQ bids with XORs, with OR-of-XORs, with XOR-of-ORs, and with OR*; price-quantity graph bids). Computational complexity of clearing auctions, reverse auctions, and exchanges under the different bidding languages - with nondiscriminatory and discriminatory pricing. Slides.
- Nov 5: Homework 2 due at the beginning of class. Multi-item auctions. Sequential auction mechanisms. Parallel auction mechanisms. Combinatorial auctions. Approaches to winner determination in combinatorial auctions. Bidding languages. Read Chapter 14 of Combinatorial Auctions book. Additional optional reading: Chapter 12 of Combinatorial Auctions book. Slides.
- Nov 10: Tree search-based winner determination algorithms, including MIP. Generalized Vickrey auction. Side constraints and non-price attributes in markets.
- Nov 12: More on tree search-based winner determination algorithms. Multi-unit combinatorial auctions. Combinatorial reverse auctions. Combinatorial exchanges. Free disposal vs. not. Complexity of clearing these markets. Preference elicitation in combinatorial auctions. Read Chapter 10 of Combinatorial Auctions book. Slides. Homework 3 posted.
- Nov 17: More on preference elicitation in combinatorial auctions.
- Nov 19: Ascending (and descending) combinatorial auctions. Primal-dual approaches. Read Chapter 2 of Combinatorial Auctions book. Slides. Automated mechanism design. Complexity. Applications. Revenue-maximizing combinatorial auctions. Affine maximizer combinatorial auctions. Virtual valuations combinatorial auctions. Automated design of multi-stage mechanisms. Slides. (Other related topics we won’t have time for in this lecture: Network view of incentive compatibility constraints; optimal mechanisms derived using that view.) Algorithmic mechanism design, i.e., incentive-compatible approximation by the auctioneer.
- Nov 24: Homework 3 due at the beginning of class. Bidding agents with hard valuation problems. Mechanism design for such agents. Nonexistence of incentive-compatible mechanisms. Slides. Benefits of non-truth-promoting mechanisms. Possibility and impossibility of manipulation-optimal mechanisms. Second-chance mechanism. Assuming agents can only solve problems that are worst-case polynomial time. Easiness of typical cases of manipulation problems. Slides.
- Nov 26: NO CLASS: Thanksgiving break.
- Dec 1: Final project presentations.
- Dec 3: (last class): Final project presentations. Final project writeups due: HARD DEADLINE.

Al
Roth’s __Game Theory, Experimental Economics, and Market Design Page__
at Harvard

Al
Roth’s __Market Design Ideas Page__

David
Parkes’s course __Computational Mechanism Design__ at Harvard

Al
Roth and Peter Coles’s course __Market Design__ at Harvard

__Noam Nisan’s course at Hebrew University, Israel__

Utku Ünver’s course __Matching Market Design__ at University of
Pittsburgh

Robert
Wilson’s course __Market Design__ at Stanford

Tim
Roughgarden’s course __Introduction to Algorithmic Game Theory__ at
Stanford

__Christos Papadimitriou’s course at University of California,
Berkeley__

__Joan Feigenbaum’s course at Yale University__

__Amy Greenwald’s course at Brown University__

__Yoav Shoham’s course Computer Science and Game Theory at
Stanford__