1. E-Commerce Lab, CSA, IISc 1 Design of Mechanisms for Dynamic Environments November 12, 2010 Y. NARAHARI http://lcm.csa.iisc.ernet.in/hari http://lcm.csa.iisc.ernet.in/hari INDO – US WORKSHOP ON MACHINE LEARNING, GAME THEORY, AND OPTIMIZATION Computer Science and Automation Indian Institute of Science, Bangalore

2. E-Commerce Lab, CSA, IISc 2 OUTLINE Static Mechanism Design and Our Work Dynamic Mechanisms and Current Art Outlook for Future and Opportunities for Collaboration

3. E-Commerce Lab, CSA, IISc 3 Mechanism Design Design of games / reverse engineering of games Game Engineering Induces a game among rational and intelligent players such that in some equilibrium of the game, a desired social choice function is implemented William Vickrey Leonid Hurwicz Eric Maskin Roger Myerson

4. A Mechanism Without Money Fair Division of a Cake Mother Social Planner Mechanism Designer Kid 1 Rational and Intelligent Kid 2 Rational and Intelligent

5. 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

6. The Famous Corus Auction (31-1-2007) CSN (Brazilian Company) Tata Steel US$ 12.04 Billion

7. 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

8. E-Commerce Lab, CSA, IISc 8 PROBLEM 2: Sponsored Search Auction Advertisers CPC D. Garg and Y. Narahari. IEEE T-ASE, 2009

9. A.Radhika, Y. Narahari, D. Bagchi, P. Suresh, S.V. Subrahmanya. Journal of IISc, 2010 Division n Division 1 CCA Carbon Credit Allocator. Problem 3: Carbon Credit Allocator cost No of Carbon Credits cost

10. E-Commerce Lab, CSA, IISc 10 Problem 4: Crowdsourcing Karthik Subbian, Ramakrishnan Kannan, Y. Narahari, IEEE APSEC, 2007 Resolve any Dispute PayCompleteAssign Receive Bids Review Problem Post Problem ReadRespon d Determine winner Verify TaskConfirm Payment ReadAsk Place BidsComplete Task

11. E-Commerce Lab, CSA, IISc 11 PROPERTIES OF SOCIAL CHOICE FUNCTIONS DSIC (Dominant Strategy Incentive Compatibility ) Reporting Truth is always good BIC (Bayesian Nash Incentive Compatibility) Reporting truth is good whenever others 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-Dictatorship No single agent is favoured all the time Individual Rationality Players participate voluntarily since they do not incur losses

12. E-Commerce Lab, CSA, IISc 12 POSSIBILITIES AND IMPOSSIBILITIES - 1 Gibbard-Satterthwaite Theorem When the preference structure is rich, a social choice function is DSIC iff it is dictatorial Groves Theorem In the quasi-linear environment, there exist social choice functions which are both AE and DSIC The dAGVA Mechanism In the quasi-linear environment, there exist social choice functions which are AE, BB, and BIC

13. E-Commerce Lab, CSA, IISc 13 POSSIBILITIES AND IMPOSSIBILITIES -2 Green- Laffont Theorem When the preference structure is rich, a social choice function cannot be DSIC and BB and AE Myerson-Satterthwaite Theorem In the quasi-linear environment, there cannot exist a social choice function that is BIC and BB and AE and IR Myerson’s Optimal Mechanisms Optimal mechanisms are possible subject to IIR and BIC (sometimes even DSIC)

14. E-Commerce Lab, CSA, IISc 14 BIC AE WBB IR SBB dAGVA DSIC EPE GROVES MYERSON VDOPT SSAOPT CBOPT MECHANISM DESIGN SPACE

15. E-Commerce Lab, CSA, IISc 15 Our work is summarized in

16. E-Commerce Lab, CSA, IISc 16 Limitations of Classical Mechanisms Do not model the repeated/sequential nature of decision making Do not model dynamic evolution of types Do not model dynamic populations Do not model any learning by the agents

17. E-Commerce Lab, CSA, IISc 17 Dynamic Mechanisms Types could be dynamic (Dynamic type mechanisms) Population could be dynamic (Online mechanisms) Can capture sequential decision making and learning Criterion could be social welfare or revenue maximization or cost minimization Could be with money or without money

18. E-Commerce Lab, CSA, IISc 18 Dynamic (Type) Mechanisms Dirk Bergemann and Juuso Valimaki The Dynamic Pivot Mechanism, Econometrica, 2010 Susan Athey and Ilya Segal An Efficient Dynamic Mechanism, Tech Report 2007 Ruggiero Cavallo, Efficiency and Redistribution in Dynamic Mechanism Design, EC 2008 Alessandro Pavan, Ilya Segal, and Jusso Toikka Dynamic Mechanism Design: Incentive Compatibility, Profit Maximization, Information Disclosure, 2009 Ruggiero Cavallo, David Parkes, and Satinder Singh Efficient Mechanisms with Dynamic Populations and Types, July 2009 Topics in Game Theory Team, IISc Dynamic Mechanisms for Sponsored Search Auction, Ongoing

19. E-Commerce Lab, CSA, IISc 19 Multi-Armed Bandit Mechanisms Nikhil Devanur and Sham Kakade The Price of Truthfulness for Pay-per-click Auctions, EC 2009 Moshe Babaioff, Yogeshwar Sharma, Aleksandrs Slivkins Characterizing Truthful MAB Mechanisms, EC 2009 Akash Das Sharma, Sujit Gujar, Y. Narahari Truthful MAB Mechanisms for Multi-slot Auctions, 2010 Sai Ming Li, Mohammad Mahdian, R. Preston McAfee Value of Learning in Sponsored Search Auctions, WINE 2010 Sham Kakade, Ilan Lobel, and Hamid Nazerzadeh An Optimal Mechanism for Multi-armed Bandit Problems, 2010 Avrim Blum and Y. Mansour. Learning, Regret Minimization, And Equilibria. In: Algorithmic Game Theory, 2007

20. E-Commerce Lab, CSA, IISc 20 Online Mechanisms David Parkes and Satinder Singh An MDP-Based Approach to Online Mechanism Design, NIPS’03 David Parkes, Online Mechanism Design Book Chapter: Algorthmic Game Theory, 2007 Alex Gershkov and Benny Moldovanu Dynamic Revenue Maximization with Heterogeneous Objects American Economic Journal, 2008 Mallesh Pai and Rakesh Vohra Optimal 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 Assignment Without Money, AAAI 2010

21. 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

22. E-Commerce Lab, CSA, IISc 22 PROBLEM 2: Sponsored Search Auction Advertisers CPC Budget Constraints, Learning by the Search Engine, Learning by the Advertisers, Optimal Auctions

23. Budget constraints, Learning by the Allocator Division n Division 1 CCA Carbon Credit Allocator. Problem 3: Carbon Credit Allocator cost No of Carbon Credits cost

24. E-Commerce Lab, CSA, IISc 24 Problem 4: Crowdsourcing Ticket Allocation, Group Ticket Allocation, Learning, Dynamic Population Resolve any Dispute PayCompleteAssign Receive Bids Review Problem Post Problem ReadRespon d Determine winner Verify TaskConfirm Payment ReadAsk Place BidsComplete Task

25. Problem 5: Amazon Mechanical Turk A Plea to Amazon: Fix Mechanical Turk! Noam Nisan’s Blog – October 21, 2010

26. E-Commerce Lab, CSA, IISc 26 Dynamic Mechanisms: Some Generic Issues Possibility and Impossibility Results For example: Does Green-Laffont Theorem hold for dynamic mechanisms? Incorporate learning into the mechanisms Bayesian mechanisms, Reinforcement Learning Approximate Solution Concepts Approximate Nash Equilibrium, etc. Budget Constraints These constraints are very common in most problems Computational Challenges Approximation algorithms? Dynamic Mechanisms without Money Powerful applications can be modeled here

27. An Interesting Dynamic Mechanism Design Problem AMALGAM Researchers and Grad Students (India) Researchers and Grad Students (USA) A lgorithms based on MAchine Learning, GAme Theory, and Mechanism design

28. E-Commerce Lab, CSA, IISc 28 Questions and Answers … Thank You …