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Self-Enforcing Strategic Demand Reduction

Self-Enforcing Strategic Demand Reduction. Paul S. A. Reitsma 1 , Peter Stone 2 , J á nos A. Csirik 3 , Michael L. Littman 4 1 Brown University 2 U. Texas at Austin 3 AT&T Research 4 Stowe Research. Overview. Complex, high-stakes auctions

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Self-Enforcing Strategic Demand Reduction

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  1. Self-Enforcing Strategic Demand Reduction Paul S. A. Reitsma1, Peter Stone2, János A. Csirik3, Michael L. Littman4 1Brown University 2U. Texas at Austin 3AT&T Research 4Stowe Research

  2. Overview • Complex, high-stakes auctions • Complex, realistic simulations • Highly effective strategy • Robust, stable, simple • Theoretical issues

  3. Auctions Important • Tiny toys to giant resources • Commercial interest • Theoretical interest • testbed for ideas • Agents appearing in auctions

  4. FCC Auction #35 • 422 licenses (spectrum blocks) • 195 markets (major US cities) • 80 bidders • 101 rounds • Dec 12 – Jan 26 2001

  5. FCC Rules • Theory: more information  more efficient • all bids known • current winners known • Bids: only 1 to 9 bid increments • 10% - 20% of current price • Eligibility requirements • i.e., complex scenario

  6. Auction Simulator • FAucS • Faithful to published rules • Client-server architecture • Runs auctions with agents and/or humans

  7. FAucS Agents • 5 important bidders • modeled individually • input from actual bidder team • Other 75 served to raise prices • model as 5 secondary bidders • same role  price floor 75%

  8. Agent Goals • Utility is profit • Separate values per market • based on Merril Lynch data, real bidder input, real auction analysis • per-agent • Desire 0-2 licenses per market • Assume no inter-market dependencies

  9. Uncertain Knowledge • Estimate other agents’ goals, budget • budget: within 20% • license valuations: within 20% • per-license, per-agent • desired licenses / market: 25% chance wrong • even one error can double perceived total desires

  10. General Agent Strategy • Each round: • Get prices from server • Compute remaining budget, eligibility • Compute market values, costs • Choose desired licenses within constraints • Submit bids to server

  11. Bidding Strategies • Self-Only • knapsack approach effective • Strategic Bidding (consider others) • threats • budget stretching • Strategic Demand Reduction (SDR) • explicit communication not allowed…

  12. Randomized SDR • Determine allocations dynamically • bid for desired licenses • tie-breaking creates allocation • respect allocation; no competition • ignore secondary bidders • don’t waste profit • great expected results

  13. Luck • Great expected results • Random  luck • Unlucky  winning little of desires • low satisfaction • Incentive to defect • lowers expected profits

  14. Fairing • Unlucky bidder takes licenses until satisfaction near average • Also bias compensation • Equitable distribution • Yet, incentive to defect again!

  15. Crime and Punishment • Temptation to take too much • big profit gain • destroys fairness, destabilizes strategy • Punish cheater to remove all profit gain • removes incentive • stabilizes strategy • Punishing RSDR

  16. Detection • Should take licenses only if: • Low satisfaction rating • Punishing a cheater • i.e., focused • Cheater takes when satisfied • Cheater takes indiscriminately • Flawless detection

  17. Enforcement Effects • Large win for uncaught cheater • All extra profit lost when cheater caught • strong disincentive • Slight enforcement cost • raises expected profit by dissuading cheating • less aggressive punishment scheme possible • people willing to pay to punish cheaters

  18. Alternative Scenarios • Change price floor • PRSDR preserves profit nearly optimally • larger profit margin  larger absolute and relative profit from PRSDR • Large numbers of defectors • drop back to all-Knapsack without loss

  19. Algorithm Overview • Bid on desired licenses • Tie-breaking creates allocation • No competition • Fairing  balance • Auto-punish defectors • Punishment removes defection incentive

  20. Improved Auction Design • Information sources: • via low prices • from auctioneer • Traditionally, more info  greater efficiency • However, more info  more strategies • PRSDR hard to thwart • less efficiency? • tradeoffs in auction design

  21. Game Theory • Analyze as 3-option Prisoner’s Dilemma: • Cooperate (RSDR) • Hedge (PRSDR) • Defect (Knapsack) • Pure Nash equilibrium • Suggestive, not conclusive, for auction

  22. Real-World Application • Relies on few assumptions: • Bidders desire maximum profit • Bidders know of PRSDR, benefits • Bidders willing to try, risk-free • Information available

  23. Conclusions • Effective • Realistic • related real strategies • safe to try • Stable • self-enforcing • Robust • Fair

  24. Questions?

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