INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft...

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INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos, Cornell University

Transcript of INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft...

Page 1: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS

Vasilis SyrgkanisMicrosoft Research, NYC

Joint with David Kempe, USCEva Tardos, Cornell University

Page 2: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Ad Auctions and Information Asymmetries

Web Impression

Ad Space

Page 3: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Ad Auctions and Information Asymmetries

Web Impression

Ad Space

Amazon Cookie

Kayak Cookie

Page 4: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Ad Auctions and Information Asymmetries

Web Impression of Unknown

Common Value Ad

Space

Amazon Cookie

Kayak Cookie

Page 5: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Common Value Single-Item Auction

Item of Unknown Common Value

Private Signal

Private Signal

Page 6: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Assumptions

• Signal of each bidder comes from Discrete Ordered Set

Page 7: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Assumptions

• Signal of each bidder comes from Discrete Ordered Set

• Informative: Higher Signal – Higher Expected Value

Page 8: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Assumptions

• Signal of each bidder comes from Discrete Ordered Set

• Informative: Higher Signal – Higher Expected Value

• Affiliated: Higher Signal – Stochastically Higher Signal for Opponent

Page 9: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Assumptions

• Signal of each bidder comes from Discrete Ordered Set

• Informative: Higher Signal – Higher Expected Value

• Affiliated: Higher Signal – Stochastically Higher Signal for Opponent

• Signals Drawn from Arbitrary Asymmetric and Correlated Distribution (with full support)

Page 10: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Traditional Applications

Oil Lease for land with unknown

common value

Access to Test

Access to Test

Page 11: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Main Questions

• How do bidders behave in equilibrium?

Page 12: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Main Questions

• How do bidders behave in equilibrium?

• Which auction formats yield higher revenue?

Page 13: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Main Questions

• How do bidders behave in equilibrium?

• Which auction formats yield higher revenue?

• How does extra information affect player utilities and seller’s revenue?

Page 14: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Auctions Considered – Hybrid Auctions

• Highest Bidder Wins.

• Pays his bid with some positive probability and the second highest bid with the remaining

Page 15: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Auctions Considered – Hybrid Auctions

• Highest Bidder Wins.

• Pays his bid with some positive probability and the second highest bid with the remaining

• : First Price Auction

Page 16: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Auctions Considered – Hybrid Auctions

• Highest Bidder Wins.

• Pays his bid with some positive probability and the second highest bid with the remaining

• : First Price Auction

• : Limit Equilibrium of Second Price Auction (Equilibrium

Selection)

Page 17: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Related Work• Value of information in auctions: [Milgrom ’79], [Milgrom/Weber ’82], . . .• Common-value auctions with binary signals: [Banerjee ’05], [Abraham/Athey/Babaioff/Grubb ’12]• Continuous values/signals: [Engelbrecht-Wiggans/Milgrom/Weber ’83], . . ., [Parreiras ’06]• Other common-value models: [Rothkopf ’69], [Reece ’78], [Hausch ’87], [Wang ’91], [Laskowski/Slonim ’99], [Kagel/Levin ’02].• Value of information: [Lehmann ’88], [Persico ’00], [Athey/Levin ’01], [Compte/Jehiel’07], [Es˝o/Szentes ’07]

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Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

How do bidders behave in equilibrium?

Theorem. There exists a unique equilibrium which is mixed and can be found constructively.

Page 19: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

How do bidders behave in equilibrium?• Mixed Nash Equilibrium must look as follows:

𝐸𝑉 (1,1) 𝑏

Player

Player

bids

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Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

How do bidders behave in equilibrium?• Mixed Nash Equilibrium must look as follows:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player

2 3 4

Range of Bids Conditional on Receiving Signal 3

bids

Page 21: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

How do bidders behave in equilibrium?• Mixed Nash Equilibrium must look as follows:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player

2 3 4

Range of Bids Conditional on Receiving Signal 3

bids

Page 22: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

How do bidders behave in equilibrium?• Mixed Nash Equilibrium must look as follows:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player

2 3 4

Range of Bids Conditional on Receiving Signal 3

CDFs of Player

CDFs of Player

bids

Page 23: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

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Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 24: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 25: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 26: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 27: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 28: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 29: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 30: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 31: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• There exists a unique equilibrium defined by a recursive process:

𝐸𝑉 (1,1) 𝑏

1 2 3 4

1Player

Player Z

2 3 4CDFs of Player

CDFs of Player

bids

Page 32: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

5

Unique Equilibrium• As approaches (Second Price Auction)

𝐸𝑉 (1,1)

1 2 3 4

1Player

Player Z

2 3 4

𝐸𝑉 (1,2) 𝐸𝑉 (5,4)𝐸𝑉 (3,3)… …

CDFs of Player

CDFs of Player

bids

Page 33: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Vasilis Syrgkanis

A Simple Example: First Price – Binary Signal

• One player receives a binary signal and the other is uninformed

𝐸 [𝑉∨𝐿] 𝐸 [𝑉∨𝐻 ]𝐸 [𝑉 ]

𝐹𝑍 (𝑏 )

𝐹𝐻𝑌 (𝑏)

𝐹 𝐿𝑌 (𝑏 )

Player

Player Z

Page 34: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Vasilis Syrgkanis

A Simple Example: First Price – Binary Signal

Page 35: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Vasilis Syrgkanis

A Simple Example: First Price – Binary Signal

• One player receives a binary signal and the other is uninformed

𝐸 [𝑉∨𝐿] 𝐸 [𝑉∨𝐻 ]𝐸 [𝑉 ]

Player

Player Z 𝐹𝑍 (𝑏 )=𝐸 [𝑉|𝐻 ]−𝐸 [𝑉 ]𝐸 [𝑉|𝐻 ]−𝑏

𝐹𝐻𝑌 (𝑏)=

Pr [𝐿 ] (𝑏−𝐸 [𝑉|𝐿 ])Pr [𝐻 ] (𝐸 [𝑉|𝐻 ]−𝑏 )

Pr [𝐿 ]

Page 36: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Vasilis Syrgkanis

A Simple Example: Limit to Second Price• One player receives a binary signal and the other is uninformed

𝐸 [𝑉∨𝐿] 𝐸 [𝑉∨𝐻 ]𝐸 [𝑉 ]

Player

Player ZPr [𝐿 ]

Page 37: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Vasilis Syrgkanis

A Simple Example: Limit to Second Price• One player receives a binary signal and the other is uninformed

𝐸 [𝑉∨𝐿] 𝐸 [𝑉∨𝐻 ]𝐸 [𝑉 ]

Player

Player ZPr [𝐿 ]

Page 38: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Vasilis Syrgkanis

Second Price Selection – No Revenue Collapse

𝐸 [𝑉∨𝐿] 𝐸 [𝑉∨𝐻 ]𝐸 [𝑉 ]

Player

Player ZPr [𝐿 ] Pr [𝐻 ]

• Different prediction than the collapsed revenue equilibrium predicted by tremble-robust equilibrium selection of Abraham et al.

Page 39: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Only one informed bidder• Informed Bidder bids “truthfully”• Uninformed Bidder simulates informed bidder’s bid• First and Second Price: Revenue Equivalent

𝐸 [𝑉∨𝑆1]𝐸 [𝑉∨𝑆2]

Player

Player ZPr [𝑆1 ] Pr [𝑆2 ]

𝐸 [𝑉∨𝑆3]

Pr [𝑆3 ]

𝐸 [𝑉∨𝑆4]

Pr [𝑆4 ]

𝐸 [𝑉∨𝑆5]

Pr [𝑆5 ]

𝐸 [𝑉∨𝑆6]

Pr [𝑆6 ]

Page 40: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Complete Revenue Ranking

• Our Result: The equilibrium revenue is a non-increasing function of the probability that the 𝜅winner pays his bid.

Page 41: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Complete Revenue Ranking

• Our Result: The equilibrium revenue is a non-increasing function of the probability that the 𝜅winner pays his bid.

• Complete Revenue Ranking among Hybrid Auctions

Page 42: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Complete Revenue Ranking

• Our Result: The equilibrium revenue is a non-increasing function of the probability that the 𝜅winner pays his bid.

• Complete Revenue Ranking among Hybrid Auctions

• First Price – Worst Revenue

Page 43: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Complete Revenue Ranking

• Our Result: The equilibrium revenue is a non-increasing function of the probability that the 𝜅winner pays his bid.

• Complete Revenue Ranking among Hybrid Auctions

• First Price – Worst Revenue

• Revenue monotonically increases as we move from first price to second price

Page 44: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Complete Revenue Ranking

• Our Result: The equilibrium revenue is a non-increasing function of the probability that the 𝜅winner pays his bid.

• Complete Revenue Ranking among Hybrid Auctions

• First Price – Worst Revenue

• Revenue monotonically increases as we move from first price to second price

• Limit Equilibrium of Second Price Selected, has highest revenue among hybrid auctions

Page 45: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Should seller reveal his private signals?

Web Site Visitor of Unknown

Common Value Ad

Space

Amazon Cookie

Kayak Cookie

MSN Cookie

Page 46: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Failure of the Linkage Principle• Linkage Principle [Milgrom-Weber’82]: In common value settings, the more information you link to the price of the winning bidder the higher the revenue.• Implication: Seller should always reveal affiliated signals.

Page 47: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Failure of the Linkage Principle• Linkage Principle [Milgrom-Weber’82]: In common value settings, the more information you link to the price of the winning bidder the higher the revenue.• Implication: Seller should always reveal affiliated signals.

• Our Result: Fails when bidders have asymmetric information! • Implication: Revealing policy not necessarily optimal in a market with information asymmetry!

Page 48: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Failure of the Linkage Principle• Linkage Principle [Milgrom-Weber’82]: In common value settings, the more information you link to the price of the winning bidder the higher the revenue.• Implication: Seller should always reveal affiliated signals.

• Our Result: Fails when bidders have asymmetric information! • Implication: Revealing policy not necessarily optimal in a market with information asymmetry!• Breaks even in first price auction when each bidder and the

auctioneer have binary signals of different accuracy

Page 49: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Failure of the Linkage Principle• First Price Auction• Value either or , a prior is with prob. • Player gets a binary signal that is correct with • Player gets a binary signal that is correct with • Seller has a signal that is correct with

𝑎0 1

: without revelation

: with revelation

𝑝𝑌=0.9 ,𝑝𝑍=0.75 ,𝑞=0.7

Page 50: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

How does extra information affect player utilities?

Ad Space

Amazon

Cookie

Kayak Cookie

Third Party Information SellersBuy Access to

Extra Cookies

Page 51: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Surprising Externality Effects

• Obviously, information can have negative externalities

Page 52: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Surprising Externality Effects

• Obviously, information can have negative externalities

But…

• Information can also have positive externalities

Page 53: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Surprising Externality Effects

• Obviously, information can have negative externalities

But…

• Information can also have positive externalities• E.g. both bidders might strictly prefer that a specific bidder

receives the extra signal

Page 54: INFORMATION ASYMMETRIES IN COMMON VALUE AUCTIONS WITH DISCRETE SIGNALS Vasilis Syrgkanis Microsoft Research, NYC Joint with David Kempe, USC Eva Tardos,

Information Asymmetries in Common Value Auctions Vasilis Syrgkanis

Recap• Information Asymmetries in Common Value Auctions

• Unique Equilibrium if winner pays his bid with positive probability

• Failure of the Linkage Principle – Not always optimal for seller to reveal information even in pure common value

• Complete Revenue RankingLimit Equilibrium of Second Price Hybrid First Price

• Extra Information can have positive externalities