Self-Enforcing Strategic Demand Reduction

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Self-Enforcing Strategic Demand Reduction

• Paul S. A. Reitsma1, Peter Stone2, János A.
  Csirik3, Michael L. Littman4
• 1Brown University 2U. Texas at
  Austin
• 3ATT Research 4Stowe Research

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Overview

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

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Auctions Important

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

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FCC Auction 35

• 422 licenses (spectrum blocks)
• 195 markets (major US cities)
• 80 bidders
• 101 rounds
• Dec 12 Jan 26 2001

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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

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Auction Simulator

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

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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

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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

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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

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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

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Bidding Strategies

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

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Randomized SDR

• Determine allocations dynamically
• bid for desired licenses
• tie-breaking creates allocation
• respect allocation no competition
• ignore secondary bidders
• dont waste profit
• great expected results

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Luck

• Great expected results
• Random ? luck
• Unlucky ? winning little of desires
• low satisfaction
• Incentive to defect
• lowers expected profits

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Fairing

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

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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

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Detection

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

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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

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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

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Algorithm Overview

1. Bid on desired licenses
2. Tie-breaking creates allocation
3. No competition
4. Fairing ? balance
5. Auto-punish defectors
6. Punishment removes defection incentive

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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

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Game Theory

• Analyze as 3-option Prisoners Dilemma
• Cooperate (RSDR)
• Hedge (PRSDR)
• Defect (Knapsack)
• Pure Nash equilibrium
• Suggestive, not conclusive, for auction

3 3 0
3 3 2
5 1 1
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Real-World Application

• Relies on few assumptions
• Bidders desire maximum profit
• Bidders know of PRSDR, benefits
• Bidders willing to try, risk-free
• Information available

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Conclusions

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

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Questions?