e-commerce lab, csa, iisc 1 design of mechanisms for dynamic environments november 12, 2010 y....
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E-Commerce Lab, CSA, IISc1
Design of Mechanisms for Dynamic
EnvironmentsNovember 12, 2010
Y. NARAHARI
http://lcm.csa.iisc.ernet.in/hari
INDO – US WORKSHOP ON
MACHINE LEARNING, GAME THEORY, AND OPTIMIZATION
Computer Science and AutomationIndian Institute of Science, Bangalore
E-Commerce Lab, CSA, IISc2
OUTLINE
Static Mechanism Design and Our Work
Dynamic Mechanisms and Current Art
Outlook for Future and Opportunities for Collaboration
E-Commerce Lab, CSA, IISc3
Mechanism Design
Design of games / reverse engineering of games Game Engineering
Induces a game among rational and intelligentplayers such that in some equilibrium of the game,
a desired social choice function is implemented
William Vickrey Leonid Hurwicz Eric Maskin Roger Myerson
A Mechanism Without MoneyFair Division of a Cake
MotherSocial PlannerMechanism Designer
Kid 1Rational and Intelligent
Kid 2Rational and Intelligent
A Mechanism with a lot of Money
Sachin Tendulkar IPL Franchisees
1
2
3
4
Mumbai Indians
Kolkata Knight Riders
Bangalore RoyalChallengers
Punjab Lions
IPL CRICKET AUCTION
The Famous Corus Auction (31-1-2007)
CSN (Brazilian Company)
Tata Steel
US$ 12.04 Billion
Problem 1: Procurement Auctions
Buyer
SUPPLIER 1
SUPPLIER 2
SUPPLIER n
T.S. Chandrasekhar, Y. Narahari, Charlie Rosa, Pankaj Dayama, Datta Kulkarni, Jeffrey Tew. IEEE T-ASE, 2006
Supply (cost) Curves
E-Commerce Lab, CSA, IISc8
PROBLEM 2: Sponsored Search Auction Advertisers
CPC
1
2
n
D. Garg and Y. Narahari. IEEE T-ASE, 2009
A. Radhika, Y. Narahari, D. Bagchi, P. Suresh, S.V. Subrahmanya. Journal of IISc, 2010
Division n
Division 1
CCACarbon Credit
Allocator
.
Problem 3: Carbon Credit Allocator
cost
No of Carbon Credits
No of Carbon Credits
cost
E-Commerce Lab, CSA, IISc10
Problem 4: Crowdsourcing
Karthik Subbian, Ramakrishnan Kannan, Y. Narahari, IEEE APSEC, 2007
Resolve any
Dispute
Resolve any
Dispute
PayPay
CompleteComplete
AssignAssignReceive
Bids
Receive Bids
Review Problem
Review Problem
Post Problem
Post Problem
Read RespondDetermine winner
Verify Task Confirm Payment
ReadAskPlace Bids Complete Task
E-Commerce Lab, CSA, IISc11
PROPERTIES OF SOCIAL CHOICE FUNCTIONS
DSIC (Dominant Strategy
Incentive Compatibility)Reporting Truth is always good
BIC (Bayesian NashIncentive Compatibility)
Reporting truth is good wheneverothers also report truth
AE (Allocative Efficiency)Allocate items to those who
value them most
BB (Budget Balance)Payments balance receipts and
No losses are incurred
Non-DictatorshipNo single agent is favoured all
the time
Individual RationalityPlayers participate voluntarilysince they do not incur losses
E-Commerce Lab, CSA, IISc12
POSSIBILITIES AND IMPOSSIBILITIES - 1
Gibbard-Satterthwaite Theorem When the preference structure is rich,
a social choice function is DSIC iff it is dictatorial
Groves TheoremIn the quasi-linear environment, there exist social
choice functions which are both AE and DSIC
The dAGVA MechanismIn the quasi-linear environment, there exist social
choice functions which are AE, BB, and BIC
E-Commerce Lab, CSA, IISc13
POSSIBILITIES AND IMPOSSIBILITIES -2
Green- Laffont TheoremWhen the preference structure is rich, a social
choice function cannot be DSIC and BB and AE
Myerson-Satterthwaite TheoremIn the quasi-linear environment, there cannot exist
a social choice function that isBIC and BB and AE and IR
Myerson’s Optimal MechanismsOptimal mechanisms are possible subject to
IIR and BIC (sometimes even DSIC)
E-Commerce Lab, CSA, IISc14
BIC
AE
WBB
IR
SBB
dAGVA
DSIC
EPE
GROVES MYERSON
VDOPT
SSAOPT CBOPT
MECHANISM DESIGN SPACE
E-Commerce Lab, CSA, IISc15
Our work is summarized in
E-Commerce Lab, CSA, IISc16
Limitations of Classical Mechanisms
Do not model the repeated/sequential nature ofdecision making
Do not model dynamic evolution of types
Do not model dynamic populations
Do not model any learning by the agents
E-Commerce Lab, CSA, IISc17
Dynamic Mechanisms
Types could be dynamic (Dynamic type mechanisms)
Population could be dynamic(Online mechanisms)
Can capture sequential decision makingand learning
Criterion could be social welfare or revenue maximization or cost minimization
Could be with money or without money
E-Commerce Lab, CSA, IISc18
Dynamic (Type) Mechanisms Dirk Bergemann and Juuso Valimaki
The Dynamic Pivot Mechanism, Econometrica, 2010
Susan Athey and Ilya SegalAn Efficient Dynamic Mechanism, Tech Report 2007
Ruggiero Cavallo, Efficiency and Redistribution in Dynamic Mechanism Design, EC 2008
Alessandro Pavan, Ilya Segal, and Jusso ToikkaDynamic Mechanism Design: Incentive Compatibility,
Profit Maximization, Information Disclosure, 2009
Ruggiero Cavallo, David Parkes, and Satinder SinghEfficient Mechanisms with Dynamic Populations and Types,
July 2009
Topics in Game Theory Team, IISc Dynamic Mechanisms for Sponsored Search Auction, Ongoing
E-Commerce Lab, CSA, IISc19
Multi-Armed Bandit Mechanisms
Nikhil Devanur and Sham KakadeThe Price of Truthfulness for Pay-per-click Auctions, EC 2009
Moshe Babaioff, Yogeshwar Sharma, Aleksandrs SlivkinsCharacterizing Truthful MAB Mechanisms, EC 2009
Akash Das Sharma, Sujit Gujar, Y. NarahariTruthful MAB Mechanisms for Multi-slot Auctions, 2010
Sai Ming Li, Mohammad Mahdian, R. Preston McAfeeValue of Learning in Sponsored Search Auctions, WINE 2010
Sham Kakade, Ilan Lobel, and Hamid NazerzadehAn Optimal Mechanism for Multi-armed Bandit Problems, 2010
Avrim Blum and Y. Mansour. Learning, Regret Minimization, And Equilibria. In: Algorithmic Game Theory, 2007
E-Commerce Lab, CSA, IISc20
Online Mechanisms David Parkes and Satinder Singh
An MDP-Based Approach to Online Mechanism Design, NIPS’03
David Parkes, Online Mechanism DesignBook Chapter: Algorthmic Game Theory, 2007
Alex Gershkov and Benny MoldovanuDynamic Revenue Maximization with Heterogeneous Objects
American Economic Journal, 2008
Mallesh Pai and Rakesh VohraOptimal Dynamic Auctions, Kellogg Report, 2008
Florin Constantin and David Parkes, Self-correcting, Sampling-based, Dynamic Multi-unit Auctions, EC 2009
James Jou, Sujit Gujar, David Parkes, Dynamic AssignmentWithout Money, AAAI 2010
Problem 1: Procurement Auctions
Buyer
SUPPLIER 1
SUPPLIER 2
SUPPLIER n
Budget Constraints, Lead Time Constraints, Learning by Suppliers,Learning by Buyer, Logistics constraints, Combinatorial Auctions,
Cost Minimization, Multiple Attributes
Supply (cost) Curves
E-Commerce Lab, CSA, IISc22
PROBLEM 2: Sponsored Search Auction Advertisers
CPC
1
2
n
Budget Constraints, Learning by the Search Engine, Learning by theAdvertisers, Optimal Auctions
Budget constraints, Learning by the Allocator
Division n
Division 1
CCACarbon Credit
Allocator
.
Problem 3: Carbon Credit Allocator
cost
No of Carbon Credits
No of Carbon Credits
cost
E-Commerce Lab, CSA, IISc24
Problem 4: Crowdsourcing
Ticket Allocation, Group Ticket Allocation, Learning, Dynamic Population
Resolve any
Dispute
Resolve any
Dispute
PayPay
CompleteComplete
AssignAssignReceive
Bids
Receive Bids
Review Problem
Review Problem
Post Problem
Post Problem
Read RespondDetermine winner
Verify Task Confirm Payment
ReadAskPlace Bids Complete Task
Problem 5: Amazon Mechanical Turk
A Plea to Amazon: Fix Mechanical Turk! Noam Nisan’s Blog – October 21, 2010
E-Commerce Lab, CSA, IISc26
Dynamic Mechanisms: Some Generic Issues Possibility and Impossibility Results
For example: Does Green-Laffont Theorem hold for dynamic mechanisms?
Incorporate learning into the mechanismsBayesian mechanisms, Reinforcement Learning
Approximate Solution ConceptsApproximate Nash Equilibrium, etc.
Budget ConstraintsThese constraints are very common in most problems
Computational ChallengesApproximation algorithms?
Dynamic Mechanisms without MoneyPowerful applications can be modeled here
An Interesting Dynamic Mechanism Design Problem
AMALGAM
Researchers and Grad Students (India)
Researchers and Grad Students (USA)
Algorithms based onMAchine Learning,GAme Theory, andMechanism design
E-Commerce Lab, CSA, IISc28
Questions and Answers …
Thank You …