Class 13: Auctioning Air

Class 13: Auctioning Air
https://uvammm.github.io/class13

Markets, Mechanisms, and Machines
University of Virginia
cs4501/econ4559 Spring 2019
David Evans and Denis Nekipelov
https://uvammm.github.io/

Transcript

1. MARKETS, MECHANISMS, MACHINES University of Virginia, Spring 2019 Class 13: Auctioning Air 26 February 2019 cs4501/econ4559 Spring 2019 David Evans and Denis Nekipelov https://uvammm.github.io
2. Plan Recap and Finish: A-K-Q Poker Equilibrium Strategy Auctions Ascending Price Auctions Spectrum Auctions 1
3. Recap: A-K-Q Poker 2 State: " ← A, K, Q , c) ← A, K, Q − { " } " = bet " = check 5 +1 if c" > c) −1 if ) > " ) = bet ) = fold +1 5 +2 if c" > c2 −2 if ) > " Payoff for Player 1 shown; Player 2 payoff is negation.
4. Player 1: Ace Queen Bet Bet Check Player 2 Ace Call -2 -1 King Call +2 -2 -1 Fold +1 +1 The Tough Decisions What if Player 1 always bluffs?
5. Always Bluff Strategy Player 1: A K Q Bet Check Bet Player 2 A Call -1 -2 K Call +2 -2 Fold +1 +1 +1 Q Fold +1 +1
6. Always Bluff Strategy Player 1: A K Q Bet Check Bet Player 2 A Call -1 -2 K Call +2 -2 Fold +1 +1 +1 Q Fold +1 +1 ",A) BCDDE FGHI = 1 3 1 2 + 2 2 + 1 3 − 1 2 + 1 2 + 1 3 −2 = 0 ",A) MNDOE FGHI = 1 3 1 2 + 1 2 + 1 3 − 1 2 + 1 2 + 1 3 −2 2 + 1 2 = − 1 6
7. Recap If player 1 never bluffs: If player 1 always bluffs: Looks like a break-even game for Player 1: is there a better strategy? " = 0, ) = 0 " = − 1 6 , ) = 1 6
8. Mixed Strategy Player 1: A K Q Bet Check Bet Player 2 A Call -1 -2 K Call +2 -2 Q Fold +1 +1 Always Bluff Player 1: A K Q Bet Check Check Player 2 A Call -1 -1 K Fold/Call +1 -1 Q Fold +1 +1 Never Bluff Pure strategy: always do the same action for a given input state. Mixed strategy: probabilistically select from a set of pure strategies. " = 0, ) = 0 " = − 1 6 , ) = 1 6
9. Strategies Player 1 Bluff with Queen Check with Queen Find the best strategy for Player 1 assuming Player 2 plays optimally. " " Q = bet, ) = call = − 1 6 " " Q = check, ) = call = 1 6 " " Q = bet, ) = fold = 1 6 " " Q = check, ) = fold = 0 Player 2 Call with King Fold with King
10. Nash Equilibrium Player 1 is making the best decision she can, taking into account Player 2’s decisions. Player 2 is making the best decision he can, taking into about Player 1’s decisions. Neither player can improve its expected value by deviating from the strategy. Hence, to find the best strategy for Player 1, we need to find a strategy that makes Player 2 indifferent between his options.
11. Winning the AKQ Game Bluff Check Call -1 +1 Fold +1 0 Player 1 wants to make Player 2 indifferent between ) = call and ) = fold " " Q = bet, ) = call = − 1 6 " " Q = check, ) = call = 1 6 " " Q = bet, ) = fold = 1 6 " " Q = check, ) = fold = 0
12. Winning the AKQ Game Bluff Check Call -1 +1 Fold +1 0 Player 1 wants to make Player 2 indifferent between ) = call and ) = fold
13. Winning the AKQ Game Bluff Check Call -1 +1 Fold +1 0 Player 1 wants to make Player 2 indifferent between ) = call and ) = fold ) , ) = call = ) , ) = fold ) , ) = call = −2 " = A ) + 2(" = | ) = −2 1 − + 2 ) , ) = fold = −1 = " = Q " = bet, ) = K)
14. Winning the AKQ Game Player 1 wants to make Player 2 indifferent between ) = call and ) = fold ) , ) = call = ) , ) = fold ) , ) = fold = −1 = (" = Q | " = bet, c) = K) −2 1 − + 2 = −1 = 1/4 Given experimental observations, how do you determine the probability of an event? ) , ) = call = −2 " = A ) + 2(" = | ) = −2 1 − + 2
15. Thomas Bayes, 1702-1761 Essay Towards Solving a Problem in the Doctrine of Chances (presented to Royal Society in 1763 after Bayes’ death)
16. Bayes’ Theorem Prior Probability: likelihood of regardless of other event Conditional Probability: likelihood of given that you observed . ) = () () () )
17. AKQ Example Given that I have a King, what is the probability that you have an Ace? ) = () ()
18. AKQ Example Given that I have a King, what is the probability that you have an Ace? " = A ) = K) = ) = K " = A) (" = A) () = K) ) = () ()
19. AKQ Example Given that I have a King and you bet, what (should be) the probability that you have a Queen (assuming you are playing optimally)? = (" = Q | " = bet, ) = K) For Player 2 to be indifferent: = 1/4
20. When to Bluff? How often should Player 1 bluff with a Queen? = (" = Q | " = bet, ) = K) = " _
21. Value of Game for Player 1 20 " , = 1 3 1 + 1 3 + 1 3 0 + 1 3 ( 2 3 −1 + 1 3 ( 1 2 −2 + 1 2 ( 1 3 −2 + 2 3 1 )
22. Auctions 21
23. “English Auction” Open, ascending first price auction 22 https://youtu.be/iiO_1XRnMt4?t=18
24. Open Ascending Auction Initialization: , = None, starting bid while new bid (bidder, ) where > : , = bidder, announce winner, bid outcome = (, ) 23 How does this compare to a second price auction?
25. Simultaneous Ascending Auction Initialization: , = Bidders, starting bid while | | > 1: = , + announce remaining active bidders outcome = (. ℎ(), ) 24 How does this compare to a second price auction?
26. Top-Level Domains 25
27. Top-Level Domains 26
28. Top-Level Domains 27
29. Why is mobile service so bad in the US? 28
36. Multiple Simultaneous Auctions 35 Multiple items (spectrum licenses) up for sale Each round: bidders submit bids for items they want must increase previous high bid for that item Rounds continue as long as price increases for at least one item (alternate proposal: item is sold after one round with no increase) Bidders cannot withdraw bids: if they are the current high bid for a license, committed to buying High bidder pays final price
37. Spectrum Auction: Bidding Rules 36 Before auction, each bidder established initial eligibility - makes deposits for amount of spectrum it wants to buy Must maintain activity level each round: - submit bids (or hold highest bid from previous round) that cover fraction (varies by phases) of its eligibility (or have eligibility reduced) - 5 waivers (intended to prevent errors, but also could be used strategically)
38. Walrasian “Auction” 37 Léon Walras tâtonnement (“trial-and-error”) to reach equilibrium
39. Simple Spectrum Auction Model 38 = 1, … , licenses for sale ⊆ subset of licenses p value of to bidder ∈ ℝt u vector of prices (price for each license) Paul Milgrom, Putting Auction Theory to Work: The Simultaneous Ascending Auction. Journal of Political Economy, 2000. Total price for = v x
40. Simple Spectrum Auction Model 39 = 1, … , licenses for sale ⊆ subset of licenses p value of to bidder ∈ ℝt u vector of prices (price for each license) Paul Milgrom, Putting Auction Theory to Work: The Simultaneous Ascending Auction. Journal of Political Economy, 2000. Total price for = v x p = argmax x {p − v x } Demand Correspondence
41. Maximize Value Theorem Theorem: Assume that the licenses are mutual substitutes for all bidders, and all bidders bid “straightforwardly”. Then: 1. The auction ends after a finite number of rounds 2. Final assignment maximizes total value (to within a single bid increment). 40
42. Maximize Value Theorem Theorem: Assume that the licenses are mutual substitutes for all bidders, and all bidders bid “straightforwardly”. Then: 1. The auction ends after a finite number of rounds 2. Final assignment maximizes total value (to within a single bid increment). 41 Proof of #1: At each round, each bidders bid on each item either stays the same or increases by some amount. Each bidder has some maximum bid (without exceeding } ()), so must eventually stop increasing bid.
43. Maximize Value Theorem Theorem: Assume that the licenses are mutual substitutes for all bidders, and all bidders bid “straightforwardly”. Then: 1. The auction ends after a finite number of rounds 2. Final assignment, ∗, ∗ , maximizes total value (to within a single bid increment). 42 Intuition: because of mutual substitutes assumption, each bidder will keep bidding until either they meet their demand, or another bidder is willing to pay more.
44. Really Mutual Substitutes? 43
45. Scenario 44 North Charlottesville South Charlottesville Both Charlottesville Bidder 1 (AT&T) $130K $230K $420K Bidder 2 (Sprint) $140K $240K $380K
46. 45 What is actual value of 30 MHz of Charlottesville’s spectrum?
47. C Block (1995-1996) Auction https://www.fcc.gov/auction/5 46
48. 47 https://www.fcc.gov/sites/default/files/wireless/auctions/05/charts/5markets.xls
49. 48 https://www.fcc.gov/sites/default/files/wireless/auctions/05/charts/5markets.xls $7,415,250 (won by Virginia Wireless)
50. What could possibly go wrong? 49
51. Collusion in Practice 50 Peter Cramton and Jesse A. Schwartz. Collusive Bidding: Lessons from the FCC Spectrum Auctions. Journal of Regulatory Economics, May 2000.
52. Collusion in Practice 51 Peter Cramton and Jesse A. Schwartz. Collusive Bidding: Lessons from the FCC Spectrum Auctions. Journal of Regulatory Economics, May 2000.
55. 54 Get your 5G bandwidth bids ready!
56. Project 4 Teaming Team must have 3 or 4 students, at least one in CS, one in Econ Cannot team with someone you have already worked with twice 55