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COS 444 Internet Auctions: Theory and Practice Spring 2010 Ken Steiglitz ken@cs. princeton. edu COS 444 Internet Auctions: Theory and Practice Spring 2010 Ken Steiglitz [email protected] princeton. edu week 1 1

Mechanics • COS 444 home page • Classes: - assigned reading: come ready to Mechanics • COS 444 home page • Classes: - assigned reading: come ready to discuss - theory (ppt + chalk) - practice/discussion/news - experiments • Grading: - problem sets, programming assignments - class participation - term paper week 1 2

Background • • • week 1 Freshman calculus, integration by parts Basic probability, order Background • • • week 1 Freshman calculus, integration by parts Basic probability, order statistics Statistics, significance tests Game theory, Nash equilibrium Java or UNIX tools or equivalent 3

Why study auctions? • Auctions are trade; trade makes civilization possible • Auctions are Why study auctions? • Auctions are trade; trade makes civilization possible • Auctions are for selling things with uncertain value • Auctions are a microcosm of economics • Auctions are algorithms run on the internet • Auctions are a social entertainment week 1 4

Goals • The central theory, classic papers • A bigger picture • Even bigger Goals • The central theory, classic papers • A bigger picture • Even bigger picture • Experimental and empirical technique week 1 5

Cassady on the romance of auctions (1967) Who could forget, for example, riding up Cassady on the romance of auctions (1967) Who could forget, for example, riding up the Bosporus toward the Black Sea in a fishing vessel to inspect a fishing laboratory; visiting a Chinese cooperative and being the guest of honor at tea in the New Territories of the British crown colony of Hong Kong; watching the frenzied but quasi-organized bidding of would-be buyers in an Australian wool auction; observing the "upside-down" auctioning of fish in Tel Aviv and Haifa; watching the purchasing activities of several hundred screaming female fishmongers at the Lisbon auction market; viewing the fascinating "string selling" in the auctioning of furs in Leningrad; eating fish from the Seas of Galilee while seated on the shore of that historic body of water; … week 1 6

Cassady on the romance of auctions (1967). . . observing Cassady on the romance of auctions (1967). . . observing "whispered“ bidding in such far-flung places as Singapore and Venice; watching a "handshake" auction in a Pakistanian go-down in the midst of a herd of dozing camels; being present at the auctioning of an early Van Gogh in Amsterdam; observing the sale of flowers by electronic clock in Aalsmeer, Holland; listening to the chant of the auctioneer in a North Carolina tobacco auction; watching the landing of fish at 4 A. M. in the market on the north beach of Manila Bay by the use of amphibious landing boats; observing the bidding of Turkish merchants competing for fish in a market located on the Golden Horn; and answering questions about auctioning posed by a group of eager Japanese students at the University of Tokyo. week 1 7

Auctions: Methods of Study • • • week 1 Theory (1961 --) Empirical observation Auctions: Methods of Study • • • week 1 Theory (1961 --) Empirical observation (recent on internet) Field experiments (recent on internet) Laboratory experiments (1980 --) Simulation (not much) f. MRI (? ) 8

History week 1 9 History week 1 9

History week 1 10 History week 1 10

History week 1 11 History week 1 11

History week 1 12 History week 1 12

History week 1 13 History week 1 13

History week 1 14 History week 1 14

History Route 6: Long John Nebel pitching hard week 1 15 History Route 6: Long John Nebel pitching hard week 1 15

Google Ad Auctions – Hal Varian week 1 16 Google Ad Auctions – Hal Varian week 1 16

Standard theoretical setup • • • One item, one seller n bidders Each knows Standard theoretical setup • • • One item, one seller n bidders Each knows her value vi (private value) Each wants to maximize her surplusi = vi – paymenti Values usually randomly assigned Values may be interdependent week 1 17

English auctions: variations • Outcry ( jump bidding allowed ) • Ascending price • English auctions: variations • Outcry ( jump bidding allowed ) • Ascending price • Japanese button Truthful bidding is dominant in Japanese button auctions Is it dominant in outcry? Ascending price? week 1 18

Vickrey Auction: sealed-bid second-price William Vickrey, 1961 week 1 Vickrey wins Nobel Prize, 1996 Vickrey Auction: sealed-bid second-price William Vickrey, 1961 week 1 Vickrey wins Nobel Prize, 1996 19

Truthful bidding is dominant in Vickrey auctions Japanese button and Vickrey auctions are (weakly) Truthful bidding is dominant in Vickrey auctions Japanese button and Vickrey auctions are (weakly) strategically equivalent week 1 20

Dutch descending-price week 1 Aalsmeer flower market, Aalsmeer, Holland, 1960’s 21 Dutch descending-price week 1 Aalsmeer flower market, Aalsmeer, Holland, 1960’s 21

week 1 22 week 1 22

Sealed-Bid First-Price • Highest bid wins • Winner pays her bid How to bid? Sealed-Bid First-Price • Highest bid wins • Winner pays her bid How to bid? That is, how to choose bidding function Notice: bidding truthfully is now pointless! week 1 23

Dutch and First-Price auctions are (strongly) strategically equivalent So we have two pairs, comprising Dutch and First-Price auctions are (strongly) strategically equivalent So we have two pairs, comprising the four most common auction forms week 1 24

Enter John Nash • Equilibrium translates question of human behavior to math • How Enter John Nash • Equilibrium translates question of human behavior to math • How much to shade? Nash wins Nobel Prize, 1994 week 1 25

Equilibrium • A strategy (bidding function) is a (symmetric) equilibrium if it is a Equilibrium • A strategy (bidding function) is a (symmetric) equilibrium if it is a best response to itself. • That is, if all others adopt the strategy, you can do no better than to adopt it also. Note: Cannot call this “optimal” week 1 26

Simple example: first-price • n=2 bidders • v 1 and v 2 uniformly distributed Simple example: first-price • n=2 bidders • v 1 and v 2 uniformly distributed on [0, 1] • Find b (v 1 ) for bidder 1 that is best response to b (v 2 ) for bidder 2 in the sense that E [surplus ] = max Note: We need some probability theory for “uniformly distributed” and “E[ ]” week 1 27

Verifying a guess • Assume for now that v/ 2 is an equilibrium strategy Verifying a guess • Assume for now that v/ 2 is an equilibrium strategy • Bidder 2 bids v 2 / 2 ; Fix v 1. What is bidder 1’s best response b (v 1 ) ? E[surplus] = … the average is over the values of v 2 when 1 wins Bidders 1’s best choice of bid is b = v 1 / 2 … QED. week 1 28

and Hurwicz + Myerson + Maskin win Nobel prize in 2007 for theory of and Hurwicz + Myerson + Maskin win Nobel prize in 2007 for theory of mechanism design week 1 29

New directions: Simulation Agent-Based Simulation of Dynamic Online Auctions, “ H. Mizuta and K. New directions: Simulation Agent-Based Simulation of Dynamic Online Auctions, “ H. Mizuta and K. Steiglitz, Winter. Simulation Conference, Orlando, FL, Dec. 1013, 2000 week 1 30

New directions: Sociology M. Shohat and J. Musch “Online auctions as a research tool: New directions: Sociology M. Shohat and J. Musch “Online auctions as a research tool: A field experiment on ethnic discrimination” Swiss Journal of Psychology 62 (2), 2003, 139 -145 week 1 31

New directions: Category clustering Courtesy of Matt Sanders ’ 09 • Categories connected by New directions: Category clustering Courtesy of Matt Sanders ’ 09 • Categories connected by mutual bidders • Darker lines mean higher probability that two categories will share bidders • Categories with higher totals near center • Color random • Only top 25% lines by weight are shown • Based on 278, 593 recorded auctions from bid histories of 18, 000 users week 1 32