1 / 30

Introduction to Auctions

Introduction to Auctions. David M. Pennock. Going once, … going twice,. Auctions: yesterday. Ebay: 4 million auctions 450k new/day >800 others auctionrover.com biddersedge.com. Auctions: today. Auctions: yesterday vs. today. What is an auction?.

cherie
Télécharger la présentation

Introduction to Auctions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Auctions David M. Pennock

  2. Going once, … going twice, ... Auctions: yesterday

  3. Ebay: 4 million auctions 450k new/day >800 others auctionrover.com biddersedge.com Auctions: today

  4. Auctions: yesterday vs. today

  5. What is an auction? • Definition [McAfee & McMillan, JEL 1987]: • a market institution with an • explicit set of rules • determining resource allocation and prices • on the basis of bids from the market participants. • Examples:

  6. B2B auctions and ecommerce • Online B2B marketplaces have been established recently for more than a dozen major industries, including the automotive; pharmaceuticals; scientific supplies; asset management; building and construction; plastics and chemicals; steel and metals; computer; credit and financing; energy; news and information; and livestock sectors. • Reuters March 29, 2000

  7. Why auctions? • For object of unknown value • Flexible • Dynamic • Mechanized • reduces complexity of negotiations • ideal for computer implementation • Economically efficient!

  8. Taxonomy of common auctions • Open auctions • English • Dutch • Sealed-bid auctions • first price • second price (Vickrey) • Mth price, M+1st price • continuous double auction

  9. Open One item for sale Auctioneer begins low; typically with seller’s reserve price Buyers call out bids to beat the current price Last buyer remaining wins;pays the price that (s)he bid English auction

  10. Dutch auction • Open • One item for sale • Auctioneer begins high;above the maximum foreseeable bid • Auctioneer lowers price in increments • First buyer willing to accept price wins;pays last announced price • less information

  11. Sealed-bid first price auction • All buyers submit their bids privately • buyer with the highest bid wins;pays the price (s)he bid  $150 $120 $90 $50

  12. Sealed-bid second price auction (Vickrey) • All buyers submit their bids privately • buyer with the highest bid wins;pays the price of the second highest bid Only pays $120  $150 $120 $90 $50

  13. Incentive compatibility • Telling the truth is optimal in second-price auction • Suppose your value for the item is $100;if you win, your net gain (loss) is $100 - price • If you bid more than $100: • you increase your chances of winning at price >$100 • you do not improve your chance of winning for < $100 • If you bid less than $100: • you reduce your chances of winning at price < $100 • there is no effect on the price you pay if you do win • Dominant optimal strategy: bid $100 • Key: the price you pay is out of your control

  14. Collusion • Notice that, if some bidders collude, they might do better by lying (e.g., by forming a ring) • In general, essentially all auctions are subject to some sort of manipulation by collusion among buyers, sellers, and/or auctioneer.

  15. Revenue equivalence • Which auction is best for the seller? • In second-price auction, buyer pays < bid • In first-price auction, buyers “shade” bids • Theorem: • expected revenue for seller is the same! • requires technical assumptions on buyers, including “independent private values” • English = 2nd price; Dutch = 1st price

  16. Mth price auction • English, Dutch, 1st price, 2nd price:N buyers and 1 seller • Generalize to N buyers and M sellers • Mth price auction: • sort all bids from buyers and sellers • price = the Mth highest bid • let n = # of buy offers >= price • let m = # of sell offers <= price • let x = min(n,m) • the x highest buy offers and x lowest sell offers win

  17. Buy offers (N=4) Sell offers (M=5) Mth price auction $300 $150 $170 $120 $130 $90 $110 $50 $80

  18. Sell offers (M=5) Buy offers (N=4) Mth price auction 1 $300 $170 2 $150 3  $130 4 price = $120 $120 5  $110  $90 $80  $50 • Winning buyers/sellers

  19. Sell offers (M=5) Buy offers (N=4) M+1st price auction 1 $300 $170 2 $150 3  $130 4 $120 5  price = $110 $110  6 $90 $80  $50 • Winning buyers/sellers

  20. Incentive compatibility • M+1st price auction is incentive compatible for buyers • buyers’ dominant strategy is to bid truthfully • M=1 is Vickrey second-price auction • Mth price auction is incentive compatible for sellers • sellers’ dominate strategy is to make offers truthfully

  21. Impossibility • Essentially no auction whatsoever can be simultaneously incentive compatible for both buyers and sellers! • if buyers are induce to reveal their true values, then sellers have incentive to lie, and vice versa • the only way to get both to tell the truth is to have some outside party subsidize the auction

  22. Sell offers (M=5) Buy offers (N=4) k-double auction 1 $300 $170 2 $150 3  $130 4 price = $110 + $10*k $120 5  $110  6 $90 $80  $50 • Winning buyers/sellers

  23. Continuous double auction • k-double auction repeated continuously over time • buyers and sellers continually place offers • as soon as a buy offer > a sell offer, a transaction occurs • At any given time, there is no overlap btw highest buy offer & lowest sell offer

  24. Continuous double auction

  25. Winner’s curse • Common, unknown value for item (e.g., potential oil drilling site) • Most overly optimistic bidder wins; true value is probably less

  26. Combinatorial auctions • E.g.: spectrum rights, computer system, … • n goods  bids allowed  2n combinations Maximizing revenue: NP-hard (set packing) • Enter computer scientists (hot topic)...

  27. Prediction auctionsIowa Electronic Markets http://www.biz.uiowa.edu/iem $1 if Hillary Clinton wins $1 if Rick Lazio wins $1 if Rudy Giuliani wins

  28. Prediction auction gamesHollywood Stock Exchange http://www.hsx.com/

  29. Prediction auction gamesForesight Exchange http://www.ideosphere.com/ $1 iff Cancer curedby 2010 Canada breaks upby 2020 Machine Go championby 2020 http://www.us.newsfutures.com/ http://www.100world.com/

  30. Prediction markets

More Related