Mechanism design with correlated distributions

Slides:



Advertisements
Similar presentations
(Single-item) auctions Vincent Conitzer v() = $5 v() = $3.
Advertisements

Algorithmic mechanism design Vincent Conitzer
Class 4 – Some applications of revenue equivalence
On Optimal Single-Item Auctions George Pierrakos UC Berkeley based on joint works with: Constantinos Daskalakis, Ilias Diakonikolas, Christos Papadimitriou,
Approximating optimal combinatorial auctions for complements using restricted welfare maximization Pingzhong Tang and Tuomas Sandholm Computer Science.
CPS Bayesian games and their use in auctions Vincent Conitzer
Seminar in Auctions and Mechanism Design Based on J. Hartline’s book: Approximation in Economic Design Presented by: Miki Dimenshtein & Noga Levy.
Michael R. Baye, Managerial Economics and Business Strategy, 3e. ©The McGraw-Hill Companies, Inc., 1999 Managerial Economics & Business Strategy Chapter.
Game Theory in Wireless and Communication Networks: Theory, Models, and Applications Lecture 6 Auction Theory Zhu Han, Dusit Niyato, Walid Saad, Tamer.
Optimal auction design Roger Myerson Mathematics of Operations research 1981.
A Prior-Free Revenue Maximizing Auction for Secondary Spectrum Access Ajay Gopinathan and Zongpeng Li IEEE INFOCOM 2011, Shanghai, China.
6.853: Topics in Algorithmic Game Theory Fall 2011 Matt Weinberg Lecture 24.
Algorithmic Applications of Game Theory Lecture 8 1.
Yang Cai Sep 24, An overview of today’s class Prior-Independent Auctions & Bulow-Klemperer Theorem General Mechanism Design Problems Vickrey-Clarke-Groves.
Agent Technology for e-Commerce Chapter 10: Mechanism Design Maria Fasli
Mechanisms for a Spatially Distributed Market Moshe Babaioff, Noam Nisan and Elan Pavlov School of Computer Science and Engineering Hebrew University of.
Auctioning one item PART 2 Tuomas Sandholm Computer Science Department Carnegie Mellon University.
Automated Mechanism Design: Complexity Results Stemming From the Single-Agent Setting Vincent Conitzer and Tuomas Sandholm Computer Science Department.
Yang Cai Sep 15, An overview of today’s class Myerson’s Lemma (cont’d) Application of Myerson’s Lemma Revelation Principle Intro to Revenue Maximization.
CPS 173 Mechanism design Vincent Conitzer
Combinatorial Auctions By: Shai Roitman
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 21.
Auction Theory תכנון מכרזים ומכירות פומביות Topic 7 – VCG mechanisms 1.
By: Amir Ronen, Department of CS Stanford University Presented By: Oren Mizrahi Matan Protter Issues on border of economics & computation, 2002.
Automated Design of Multistage Mechanisms Tuomas Sandholm (Carnegie Mellon) Vincent Conitzer (Carnegie Mellon) Craig Boutilier (Toronto)
Yang Cai Oct 08, An overview of today’s class Basic LP Formulation for Multiple Bidders Succinct LP: Reduced Form of an Auction The Structure of.
CPS Application of Linear and Integer Programming: Automated Mechanism Design Guest Lecture by Mingyu Guo.
Mechanism Design Ruta Mehta. Game design (not video games!) to achieve a desired goal, like fairness, social welfare maximization, etc.
Yang Cai Oct 06, An overview of today’s class Unit-Demand Pricing (cont’d) Multi-bidder Multi-item Setting Basic LP formulation.
Automated Mechanism Design Tuomas Sandholm Presented by Dimitri Mostinski November 17, 2004.
Mechanism Design II CS 886:Electronic Market Design Sept 27, 2004.
6.853: Topics in Algorithmic Game Theory Fall 2011 Constantinos Daskalakis Lecture 22.
Auctions serve the dual purpose of eliciting preferences and allocating resources between competing uses. A less fundamental but more practical reason.
Automated mechanism design Vincent Conitzer
Advanced Subjects in GT Prepared by Rina Talisman Introduction Revenue Equivalence The Optimal Auction (Myerson 1981) Auctions.
Comp/Math 553: Algorithmic Game Theory Lecture 10
Comp/Math 553: Algorithmic Game Theory Lecture 11
Automated mechanism design
By Audrey Hu and Liang Zou University of Amsterdam
Bayesian games and mechanism design
False-name Bids “The effect of false-name bids in combinatorial
Bayesian games and their use in auctions
Applications of Automated Mechanism Design
Comp/Math 553: Algorithmic Game Theory Lecture 08
CPS Mechanism design Michael Albert and Vincent Conitzer
Failures of the VCG Mechanism in Combinatorial Auctions and Exchanges
Comp/Math 553: Algorithmic Game Theory Lecture 09
Tuomas Sandholm Computer Science Department Carnegie Mellon University
Comp/Math 553: Algorithmic Game Theory Lecture 14
Implementation in Bayes-Nash equilibrium
Auction Theory.
Game Theory in Wireless and Communication Networks: Theory, Models, and Applications Lecture 6 Auction Theory Zhu Han, Dusit Niyato, Walid Saad, Tamer.
Implementation in Bayes-Nash equilibrium
Comp/Math 553: Algorithmic Game Theory Lecture 15
Economics and Computation Week #13 Revenue of single Item auctions
Robust Mechanism Design with Correlated Distributions
Vincent Conitzer Mechanism design Vincent Conitzer
Vincent Conitzer CPS 173 Mechanism design Vincent Conitzer
Automated mechanism design
Preference elicitation/ iterative mechanisms
Auctions Lirong Xia. Auctions Lirong Xia Sealed-Bid Auction One item A set of bidders 1,…,n bidder j’s true value vj bid profile b = (b1,…,bn) A sealed-bid.
Vincent Conitzer Computer Science Department
Near-Optimal Simple and Prior-Independent Auctions Tim Roughgarden (Stanford)
CPS Preference elicitation/ iterative mechanisms
Implementation in Bayes-Nash equilibrium
Information, Incentives, and Mechanism Design
Auction Theory תכנון מכרזים ומכירות פומביות
Vincent Conitzer CPS Mechanism design Vincent Conitzer
CPS Bayesian games and their use in auctions
Presentation transcript:

Mechanism design with correlated distributions Michael Albert and Vincent Conitzer malbert@cs.duke.edu and conitzer@cs.duke.edu

Impossibility results from mechanism design with independent valuations Myerson auction is revenue optimal for independent valuations This is an impossibility result in disguise! Myerson auction doesn’t always allocate the item, and it doesn’t always charge the bidders valuation Bidder’s virtual valuation ψ(vi)= vi - (1 - Fi(vi))/fi(vi) The bidder with the highest virtual valuation (according to his reported valuation) wins (unless all virtual valuations are below 0, in which case nobody wins) Winner pays value of lowest bid that would have made him win Combined with the revenue equivalence theorem, we have an impossibility result. The impossibility result is: we can’t efficiently allocate an item and maximize revenue at the same time. More than that, we have to give some of the utility to the bidders because they have private information.

Why should we care about maximizing revenue? Auctions are one of the fundamental tools of the modern economy In 2012 four government agencies purchased $800 million through reverse auctions (Government Office of Accountability 2013) In 2014, NASA awarded contracts to Boeing and Space-X worth $4.2 billion and $2.6 billion through an auction process (NASA 2014) In 2014, $10 billion of ad revenue was generated through auctions (IAB 2015) The FCC spectrum auction, currently in the final round, expects to allocate between $60 and $80 billion worth of broadcast spectrum It is important that the mechanisms we use are revenue optimal!

Do current techniques get us “close enough”? Standard simple mechanisms do very well with large numbers of bidders VCG mechanism revenue with n+1 bidders ≥ optimal revenue mechanism with n bidders, for IID bidders (Bulow and Klemperer 1996) For “thin” markets, must use knowledge of the distribution of bidders We use the distribution to set the reserve price for a Myerson auction Thin markets are a large concern Sponsored search auctions with rare keywords or ad quality ratings Of 19,688 reverse auctions by four governmental organizations in 2012, one third had only a single bidder (GOA 2013)

What if Types are Correlated? This result is for all possible distributions over bidder valuations Specifically, the impossibility of efficient allocation and revenue maximization must encompass the case where the agents types are independent. This is unlikely to hold in many situations Oil drilling rights Sponsored search auctions Anything with resale value Anything with a common value component (like similar inputs) Under correlation, we can break this impossibility result Cremer and McLean (1985, 1988), Albert, Conitzer, Lopomo (2016)

Example: Divorce arbitration Outcomes: Each agent is of high type w.p. .2 and low type w.p. .8 Preferences of high type: u(get the painting) = 11,000 u(museum) = 6,000 u(other gets the painting) = 1,000 u(burn) = 0 Preferences of low type: u(get the painting) = 1,200 u(museum) = 1,100 Distribution under independent valuations H L H .2*.2 = .04 .8*.2 = .16 L .2*.8 = .16 .8*.8 = .64

Perfectly Correlated Distribution high low high .2 low .8 Maximum Social Welfare = 12,000*.2 + 2,200*.8 = 4,160

Clarke (VCG) mechanism high low high Both pay 5,000 Husband pays 200 low Wife pays 200 Both pay 100 Expected sum of divorcees’ utilities = (12,000-10000)*.2 + (2200-200)*.8 = 2000

Mechanism with Perfect Correlation high low high Both pay nothing Both pay nothing low Both pay nothing Both pay nothing Expected sum of divorcees’ utilities = (12,000)*.2 + (2200)*.8 = 4,160

Maximum Revenue with Perfect Correlation high low high Both pay $6000 Both pay nothing low Both pay nothing Both pay $1100 Expected Revenue = 4160 Expected sum of divorcees’ utilities = (12,000 – 12,000)*.2 + (2200-2200)*.8 = 0

Clarke (VCG) mechanism + side payments high low high Husband pays 200 Both pay 5,000 & husband pays 1,100, Wife pays 1,000 & both pay 1,000 low Wife pays 200 Both pay 100 & husband pays 1,000, Wife pays 1,100 & both pay 1,100 Expected sum of divorcees’ utilities = (12,000 – 12,000)*.2 + (2200-2200)*.8 = 0 Expected Revenue = 4160

How much correlation do we need to maximize revenue? Need to look at ex-interim individually rational (IR) mechanisms: Σθ-i π(θ-i| θi) [vi(θi, o(θ1, θ2, …, θi, …, θn)) - xi(θ1, θ2, …, θi, …, θn)] ≥ 0 For now we will use dominant strategy (ex-post) incentive compatible: vi(θi, o(θ1, θ2, …, θi, …, θn)) - xi(θ1, θ2, …, θi, …, θn) ≥ vi(θi, o(θ1, θ2, …, θi’, …, θn)) - xi(θ1, θ2, …, θi’, …, θn) Nearly any correlation will do! In fact, for bidders with two types each, any correlation at all will do!

Cremer-McLean Condition  

Can we do better than Cremer-McLean? The Cremer-McLean condition is sufficient, but not necessary While the condition is generic for two (or more) bidders with the same number of types, is this always going to be the case? What if we really have an external signal that we are using to condition payments, so that there is only one bidder? Ad auctions with click through rates of related ads Prices of commodities that are used as part of the production process What if we don’t know the distribution well? Maybe we want to “bin” the other bidders bids in order to estimate a smaller distribution What is both necessary and sufficient?

Necessary and Sufficient Condition for Ex-Interim IR and Dominant Strategy IC  

Why restrict ourselves to Dominant Strategy IC? While dominant strategy IC is sufficient to give us a generic condition when there are sufficient bidders, we’ve already seen that is not necessarily the case. Can we relax the necessary conditions if we consider BNE incentive compatibility? Σθ-i π(θ-i| θi) [vi(θi, o(θ1, θ2, …, θi, …, θn)) - xi(θ1, θ2, …, θi, …, θn)] ≥ Σθ-i π(θ-i| θi) [vi(θi, o(θ1, θ2, …, θi’, …, θn)) - xi(θ1, θ2, …, θi’, …, θn)] This gives us the ability to have multiple lotteries over the external signal.

Necessary and Sufficient Condition for Ex-Interim IR and BNE IC  

Impossibility results from mechanism design with independent valuations Myerson-Satterthwaite Impossibility Theorem [1983]: We would like a mechanism that: is efficient, is budget-balanced (all the money stays in the system), is BNE incentive compatible, and is ex-interim individually rational This is impossible! v( ) = x v( ) = y