auctions for digital goods ali echihabi university of waterloo – nov 2004
TRANSCRIPT
Auctions for Digital GoodsAli Echihabi
University of Waterloo – Nov 2004
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Talk Overview Review of four papers. New context, new approach. Focus on: “Competitive Auctions”, Goldberg,
Hartline, Karlin,Wright, Saks.
Extra: experiments and extensions. Discussion.
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Our Context Unlimited supply. Identical goods (digital). Utilities may vary a lot. Allocation no longer a problem. Pricing is. Holds for bounded supply. Why auctions then?
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New Approach What CS people do best! Algorithms are like auctions:
Correctness : Truthfulness Efficiency: Time Performance: Revenue
New concepts for auctions: Provable performance guarantees. Over all possible inputs.
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Competitive Auctions Bayesian can go wrong. Run detail-free auctions. Quantify the value of information:
How much revenue did we miss? What metric do we use?
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Benchmark Two metrics:
Maximum possible ever. You sell at bidder’s valuation. Multi-price.
Optimal price-fixing. Single-price for all bidders. We use this one.
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Definitions
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Definitions (2)
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Bid-independent Auction
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Important Result Deterministic Auctions are not competitive.
Symmetric: outcome is independent of the order of the bids.
Need to consider Randomized.
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Randomized Auctions Lower Bound:
Random Sampling:1. Dual-Price Sampling Optimal Threshold (DOST)
2. Sampling Cost Sharing (SCS).
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Definition of DOST
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Properties of DOST Truthful. More is better:
Best DOST can do is 4-competitive.
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Definition of SCS Special case of Moulin-Shenker mechanism A.k.a Consensus Revenue Estimate (CORE)
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Properties of SCS Truthful. 4-competitive. May lose half of potential profit:
Pick a ratio r < 1. Competitive ratio grows: 4/r.
Only sells if revenue reachable.
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Important Result: is good Competitive auctions do not outperform:
Considering all possible inputs.
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Results Summary No truthful deterministic auction is
competitive. Several randomized auctions are truthful and
competitive. is a good benchmark.
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Experiments: Number of Bids
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Experiments: Sample Size
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Experiments: Sample Size (2)
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Extensions: Online vs Offline Paper borrows concepts from online
algorithms That doesn’t make the auction online:
Decide the price of current bid before next bid arrives [3]
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Extensions: Envy Bidder X rejected. Bidder Y wins and pays a price
lower than X’s bid. Envy is bad for seller. No constant-competitive truthful auction is envy-
free. Relax constraints.
CORE: Consensus Revenue Estimate (or SCS) Truthful, envy-free, competitive, group-strategy proof
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Extensions: Online vs. Offline Using theory for online algorithms doesn’t
make your auction online. Online auction: determine price for bid i,
before next bid arrives. Can’t make all pricing decisions at once [3] gives a randomized online competitive
auction within
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Excellent References [1]: “Competitive Auctions”, Goldberg, Hartline,
Karlin, Wright, Saks.
[2]: “Competitive Auctions and Digital Goods”, Goldberg, Hartline, Wrigth.
[3]: “Incentive-Compatible Online Auctions for Digital Goods”, Bar-Yossef, Hildrum, Wu.
[4]: “Envy-Free Auctions for Digital Goods”, Goldberg, Hartline.
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Thank you!
Discussion