AEA Continuing Education in Game Theory Avinash Dixit and David Reiley Session 6: Market Design and Algorithms David Reiley Yahoo! Research January 2011.

Slides:



Advertisements
Similar presentations
An Efficient Dynamic Auction for Heterogeneous Commodities (Lawrence M.Ausubel - september 2000) Authors: Oren Rigbi Damian Goren.
Advertisements

Testing Linear Pricing Algorithms for use in Ascending Combinatorial Auctions (A5) Giro Cavallo David Johnson Emrah Kostem.
(Single-item) auctions Vincent Conitzer v() = $5 v() = $3.
Algorithmic mechanism design Vincent Conitzer
Performance Evaluation Sponsored Search Markets Giovanni Neglia INRIA – EPI Maestro 4 February 2013.
Naveen Garg, CSE, IIT Delhi
Matching Theory.
Seminar in Auctions and Mechanism Design Based on J. Hartline’s book: Approximation in Economic Design Presented by: Miki Dimenshtein & Noga Levy.
1 Auctioning Many Similar Items Lawrence Ausubel and Peter Cramton Department of Economics University of Maryland.
The Voting Problem: A Lesson in Multiagent System Based on Jose Vidal’s book Fundamentals of Multiagent Systems Henry Hexmoor SIUC.
Intermediate Microeconomics Midterm (50%) (4/27) Final (50%) (6/22) Term grades based on relative ranking. Mon 1:30-2:00 ( 社科 757)
An Approximate Truthful Mechanism for Combinatorial Auctions An Internet Mathematics paper by Aaron Archer, Christos Papadimitriou, Kunal Talwar and Éva.
Practical Public Sector Combinatorial Auctions S. RaghavanUniversity of Maryland (joint work with Robert Day, University of Connecticut) Full paper “Fair.
Multi-item auctions with identical items limited supply: M items (M smaller than number of bidders, n). Three possible bidder types: –Unit-demand bidders.
Game Theory in Wireless and Communication Networks: Theory, Models, and Applications Lecture 6 Auction Theory Zhu Han, Dusit Niyato, Walid Saad, Tamer.
Federal Communications Commission NSMA Spectrum Management Conference May 20, 2008 Market Based Forces and the Radio Spectrum By Mark Bykowsky, Kenneth.
Multiagent Coordination Using a Distributed Combinatorial Auction Jose M. Vidal University of South Carolina AAAI Workshop on Auction Mechanisms for Robot.
Selling Billions of Dollars Worth of Keywords Presented By: Mitali Dhoble By Benjamin Edelman, Michael Ostrovsky And Michael Schwarz Reference:
A Prior-Free Revenue Maximizing Auction for Secondary Spectrum Access Ajay Gopinathan and Zongpeng Li IEEE INFOCOM 2011, Shanghai, China.
Competitive Auctions Review Rattapon Limprasittiporn.
Game Theory 1. Game Theory and Mechanism Design Game theory to analyze strategic behavior: Given a strategic environment (a “game”), and an assumption.
Seminar In Game Theory Algorithms, TAU, Agenda  Introduction  Computational Complexity  Incentive Compatible Mechanism  LP Relaxation & Walrasian.
1 Bidding and Matching Procedures Professor Paul Milgrom Stanford and MIT March 19, 2002 *Some of the procedures described herein are subject to issued.
What is game theory… Game theory studies settings where multiple parties (agents) each have –different preferences (utility functions), –different actions.
An Introduction to Game Theory Part I: Strategic Games
Algorithmic Applications of Game Theory Lecture 8 1.
Mechanism Design and the VCG mechanism The concept of a “mechanism”. A general (abstract) solution for welfare maximization: the VCG mechanism. –This is.
An Experimental Test of House Matching Algorithms Onur Kesten Carnegie Mellon University Pablo Guillen University of Sydney.
Lecture 1 - Introduction 1.  Introduction to Game Theory  Basic Game Theory Examples  Strategic Games  More Game Theory Examples  Equilibrium  Mixed.
Distributed Multiagent Resource Allocation In Diminishing Marginal Return Domains Yoram Bachrach(Hebew University) Jeffrey S. Rosenschein (Hebrew University)
1 Multiunit Auctions Part II Thanks to Larry Ausubel and especially to Peter Cramton for sharing their notes.
Mechanism Design and Auctions Jun Shu EECS228a, Fall 2002 UC Berkeley.
Agent Technology for e-Commerce Chapter 10: Mechanism Design Maria Fasli
Auctions Hal R. Varian. Auctions Auctions are very useful mean of price discovery eBay: everyone’s favorite example DoveBid: high value asset sales at.
Multi-Item Auctions 1. Many auctions involve sale of different types of items Spectrum licenses in different regions, seats for a concert or event, advertising.
Communication Networks A Second Course Jean Walrand Department of EECS University of California at Berkeley.
MAHIMA CHAWLA ELIZABETH GORDON The Adoption Market: How can the number of parent- child matchings be increased?
Collusion and the use of false names Vincent Conitzer
Strategic Demand Reduction in homogenous multiunit auctions (where bidders may be interested in more than one unit)
Introduction to Auctions David M. Pennock. Auctions: yesterday Going once, … going twice,...
Yang Cai Sep 8, An overview of the class Broad View: Mechanism Design and Auctions First Price Auction Second Price/Vickrey Auction Case Study:
CPS 173 Mechanism design Vincent Conitzer
Multi-Unit Auctions with Budget Limits Shahar Dobzinski, Ron Lavi, and Noam Nisan.
Auction Seminar Optimal Mechanism Presentation by: Alon Resler Supervised by: Amos Fiat.
Combinatorial Auctions By: Shai Roitman
1 Ascending Auctions with Package Bidding By Larry Ausubel and Paul Milgrom October 27, 2001 This presentation reports research results. Some of the methods.
Mechanism Design CS 886 Electronic Market Design University of Waterloo.
Yang Cai Sep 29, An overview of today’s class Vickrey-Clarke-Groves Mechanism Combinatorial Auctions Case Study: Spectrum Auctions.
Auction Theory Class 9 – Multi-unit auctions: part 2 1.
Slide 1 of 16 Noam Nisan The Power and Limitations of Item Price Combinatorial Auctions Noam Nisan Hebrew University, Jerusalem.
Steffen Staab 1WeST Web Science & Technologies University of Koblenz ▪ Landau, Germany Network Theory and Dynamic Systems Auctions.
Auctions serve the dual purpose of eliciting preferences and allocating resources between competing uses. A less fundamental but more practical reason.
The Stable Marriage Problem
Sep 29, 2014 Lirong Xia Matching. Report your preferences over papers soon! –deadline this Thursday before the class Drop deadline Oct 17 Catalan independence.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 22.
1 Applied Microeconomic Models Prof. Ugo Colombino Asymmetric information: Auctions and Negotiations Three developments in economics and in technology.
Market Design and Analysis Lecture 2 Lecturer: Ning Chen ( 陈宁 )
Decentralized Auctions for Uniformly Semimodular Bidders Mahyar Salek Richard Steinberg MSR Cambridge London School of Economics.
Advanced Subjects in GT Prepared by Rina Talisman Introduction Revenue Equivalence The Optimal Auction (Myerson 1981) Auctions.
Auctions Supplemental Material. In case you haven't noticed: Auctions are Everywhere! eBay → simple auctions for a single item AdWord Auctions → advertisers.
مهندسي سيستم‌هاي تجارت الکترونیکی Electronic Commerce System Engineering (ECSE) رشته مهندسي فناوري اطلاعات- گرايش تجارت الکترونیکی دوره کارشناسی ارشد حضوری.
CPS Mechanism design Michael Albert and Vincent Conitzer
Multi-Item Auctions.
Comp/Math 553: Algorithmic Game Theory Lecture 09
Internet Economics כלכלת האינטרנט
Fair division Lirong Xia Oct 7, 2013.
Vincent Conitzer Mechanism design Vincent Conitzer
Vincent Conitzer CPS 173 Mechanism design Vincent Conitzer
Matching and Resource Allocation
Presentation transcript:

AEA Continuing Education in Game Theory Avinash Dixit and David Reiley Session 6: Market Design and Algorithms David Reiley Yahoo! Research January 2011

FCC spectrum auctions involve bidding on multiple licenses, possibly complementary. Item AItem BPackage AB Bidder 1149 Bidder 2325 Bidder 3056 To handle this, we may want to create a combinatorial version of the Vickrey auction. Example: What is the optimal allocation in this auction? What are the prices paid by bidders in the VCG auction mechanism?

FCC spectrum auctions involve bidding on multiple licenses, possibly complementary. Item AItem BPackage AB Bidder 1149 Bidder 2325 Bidder 3056 Example: Solution: Maximize by giving AB to Bidder 1. Price equals surplus if Bidder 1 were absent, which is 3+5=8.

What happens if we change the values slightly in the example? Now what are the VCG allocation and payments? Item AItem BPackage AB Bidder 1147 Bidder 2325 Bidder 3056

What happens if we change the values slightly in the example? Now what are the VCG allocation and payments? Item AItem BPackage AB Bidder 1147 Bidder 2325 Bidder 3056 Solution: 2 wins A, 3 wins B. Total surplus 8. Without 2, surplus would have been 7. So 2 pays 2. Without 3, surplus would have been 7. So 3 pays 4.

One more example. What are the VCG allocation and payments? What’s undesirable about the outcome? Item AItem BPackage AB Bidder 1030 Bidder 2300 Bidder 3115

One more example. Allocation: 1 gets B, 2 gets A. Surplus is 6. Without A, surplus would be 5. So A pays 2. Without B, surplus would be 5. So B pays 2. What’s undesirable? Not in the core. Item AItem BPackage AB Bidder 1030 Bidder 2300 Bidder 3115

Several problems for VCG auctions with complementarities: The revenues may be low, and the outcome may not be in the core. –Literature on Core-Selecting Auctions If there are winner’s-curse problems, ascending-bid auctions may be better. –SAA, plus work by Ausubel The problem can quickly get computationally complex. –100 items and all possible packages?

The Simultaneous Ascending Auction has been used in practice by the FCC. See McAfee & McMillan (1996). Two interesting strategic problems in market design: Exposure problem: Without package bidding, a package bidder may get “stuck” overpaying for a single license. Threshold problem: two individual- license bidders may tend to free-ride, fail to displace a bidder on a package.

What are the three lessons from Roth’s market-design work?

Provide market thickness. Overcome the congestion that thickness can bring, so that participants can to consider alternative transactions. Make it safe to participate in the market –Rather than staying out –Rather than behaving strategically in a way that distorts market efficiency

More questions on Roth What are the five markets that Roth has worked on? Can you think of any other examples of market design?

More questions on Roth What are the five markets that Roth has worked on? Can you think of any other examples of market design? –Financial markets –Sponsored-search auctions –B2B exchanges –Privatization auctions –College course selection

How does the Gale-Shapley algorithm work? Students and hospitals report complete preference orderings. Order the students (perhaps randomly). The first student proposes to her first-choice hospital. If the hospital finds her acceptable, make this tentative assignment. The next student does the same. If a student proposes to a hospital that already has a tentative match, replace that student if the match can be improved, otherwise go to the next-choice hospital and try again. If someone gets “bumped” from a tentative match, move them to a tentative match with their next available choice. Continue until all students either have been matched, or have no remaining options available from their preference list.

Exercise: use the Gale-Shapley algorithm to compute matchings in the following example. Three potential grooms: A, B, C. Three potential brides: X, Y, Z. Grooms are the proposers, brides are the receivers. Preferences are (best to worst): A: YXZ B: ZYX C: XZY X: BAC Y: CBA Z: ACB

In this example, the algorithm converges in one step. A proposes to Y. –Y tentatively accepts. B proposes to Z. –Z tentatively accepts. C proposes to X. –X tentatively accepts. Final matching: {AY,BZ,CX} Stable matching: No two would exchange places. Grooms: A: YXZ B: ZYX C: XZY Brides: X: BAC Y: CBA Z: ACB

Exercise: What is an example of an unstable matching? Grooms: A: YXZ B: ZYX C: XZY Brides: X: BAC Y: CBA Z: ACB

Exercise: What is an example of an unstable matching? There are six possible matchings. Only three are stable. {AX,BY,CZ} - stable {AX,BZ,CY} - C & Z prefer each other {AY,BX,CZ} - B & Y prefer each other {AY,BZ,CX} - stable {AZ,BX,CY} - stable {AZ,BY,CX} - A & X prefer each other Grooms: A: YXZ B: ZYX C: XZY Brides: X: BAC Y: CBA Z: ACB

Nice properties of Gale-Shapley: Always converges to a stable matching. Proposer side has a (weakly) dominant strategy to report truthfully.

A receiver can have a strategic incentive to shorten her list. With truthtelling and grooms as proposers,the final matching was: {AY,BZ,CX} Though this matching is stable, each bride is getting her last choice. What if Y reports just “CB,” indicating that A is unacceptable to her? Grooms: A: YXZ B: ZYX C: XZY Brides: X: BAC Y: CBA Z: ACB

Suppose Y reports CB instead of CBA. Then the algorithm proceeds as follows. A proposes to Y. –Y rejects. A proposes to X. –X tentatively accepts. B proposes to Z. –Z tentatively accepts. C proposes to X. –X rejects (because she prefers A). C proposes to Z. –Z tentatively accepts, rejecting B. B proposes to Y. –Y tentatively accepts. Final matching: {AX,BY,CZ} Grooms: A: YXZ B: ZYX C: XZY Brides: X: BAC Y: CBA Z: ACB Note that Y is better off than in the previous stable matching: {AY,BZ,CX}.

An example of game theory’s role in market design. Since truthtelling is a dominant strategy for the proposer side in the GS algorithm, we might assign that role to agents whose strategy we want to simplify. –Students in school choice –Doctors in residency matc Note that with larger markets, the incentives for strategic behavior are relatively small on the receiver side as well.