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Crowdsourced Bayesian Auctions. Pablo Azar Jing Chen Silvio Micali. MIT. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A. Agenda. 1. Motivation for Crowdsourced Bayesian. 2. Our Model. 3. What We Can Do In-Principle in Our Model. - PowerPoint PPT Presentation
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Crowdsourced Bayesian Auctions
MIT
Pablo Azar Jing Chen Silvio Micali
Agenda1. Motivation for Crowdsourced Bayesian2. Our Model3. What We Can Do In-Principle in Our Model4. What We Constructively Do in Our Model
Tools♦ Richer Strategy Spaces (again!)
♦ New Solution Concept (mutual knowledge of rationality)
5. Comparison
1. Motivation for Crowdsourced Bayesian
Mechanism Design: Leveraging the Players’ Knowledge and Rationalityto obtain an outcome satisfying a desired property
Wanted Property: “Good” revenue in auctions
Auctions in General
n players
a set of goods
Valuation (for subsets) ({ }) = 310
Allocation:
Outcome: allocation (A0, A1, …, An) + prices (P1, …, Pn)
Utility:
: { }
Revenue:
:
Bayesian :
designer
[Myerson’81]: optimal revenue for single-good auctions
4, D
players
n, D3, D2, D
1, D
D
Centralized Bayesian :
Very Strong!Designer knows D further assumes: Independent distribution
4, Dn, D3, D
2, D1, D
D
Bayesian Nash further assumes:
Still Strong!
ignorant
players know each other better than designer knows them
, D , D, D , D , D
Bayesian :
♦ D common knowledge to players
ignorant4, Dn, D3, D
2, D1, D
, D , D, D , D , D
I know that he knows that I know that he knows that I know that
Bayesian : Bayesian Nash further assumes:
♦ D common knowledge to players
ignorant
♦ (Hidden:) Each i knows ≥ and ≤
4, Dn, D3, D
2, D1, D
, D , D, D , D , D
Bayesian :
!!!
E.g., [Cremer and McLean’88]
Bayesian Nash further assumes:
♦ D common knowledge to players
2. Our Crowdsourced Bayesian Model
Crowdsourced if:
ignorant
♦ Each i individually knows ≥
♦ No common knowledge required
2, D|S2
1, D|S1 3, D|S3
4, D|S4
n, D|Sn
Bayesian :
♦ Designer ignorant
2, D|S2
1, D|S1 3, D|S3
4, D|S4
n, D|Sn
♦ D: iid, independent, correlated…
Our Crowdsourced Bayesian Assumption
Each player i knows an arbitrary refinement of D|θi
θ:
Si1
Si2
Si3
i, D|Si2
Ignorant Designer Mechanism gets players’ strategies only
i knows D|θi and refines as much as he can
Can We Leverage?
Yes, with proper tools!
Tool 1: Richer Strategy Spaces
Each i’s strategy space
♦ Classical Revealing Mechanism:
♦ Our Revealing Mechanism:
“richer language” for player i
Tool 2: Two-Step DST
Recall (informally): DST mechanismDefine (informally): Two-Step DST mechanism1.
2.
3.
θi is the best strategy regardless what the others do
1.
2. θi is the best regardless what the others do
D|Si is the best given first part actions = θ
regardless i’s second part action
regardless the others’ second part actions
DST = Dominant Strategy Truthful
,
,
,
,
,
,θi
,θn
,θ1
,θi D|Sii
Tool 2: Two-Step DST
♦ Mutual Knowledge of Rationality
DST = Dominant Strategy TruthfulDefine (informally): Two-Step DST mechanism1.
2.
3.
θi is the best regardless what the others do
D|Si is the best given first part actions = θ
regardless i’s second part action
regardless the others’ second part actions
3. What We Can Do In-Principle in Our Model
Revenue In General Auctions
optimal DST revenue under centralized Bayesian
Hypothetical benchmark
♦ Not asymptotic
♦ n=1000? 100? Wonderful!
♦ n=2? “Tight” (even for single-good auctions)!
Mechanism
[B’50]:
♦ Choose a player i uniformly at random
1. Player i announces
2. Each other player j announces
♦ Run the optimal DST mechanism M with
♦ Reward i using Brier’s Scoring Rule
for -i
Allegedly:Allegedly:
Player i gets nothing and pays nothing
bounded in [-2, 0]
to a real numberexpectation maximized if
♦ Black-box usage of the optimal DST mechanism
[Myerson’81] “almost optimal” for single-good auction with independent distribution under
crowdsourced Bayesian
♦ An existential result
MechanismRemarks
♦ Leverage one player’s knowledge about the others
4. What We Constructively Do in Our Model
Revenue In Single-Good Auctions♦ Our Star Benchmark :
[Ronen’01]
the monopoly price for given the others’ knowledge
p, Y/N?
Mechanism
♦ Aggregate all but ’s knowledge
♦ Loses δ fraction in revenue for 2-step strict DST
♦ Is NOT of perfect information
Remarks Only
Crucial: The other players must not see otherwise nobody will be truthful
5. Comparison
Mechanism
♦ [Caillaud and Robert’05]: single good auction, ignorant designer, for independent D common knowledge to players, Bayesian equilibrium
♦ Ours: for n=2 under crowdsourced Bayesian
“Tight” for 2-player, single-good, independent D
Separation between the two models
( For General Auctions, )
♦ [Ronen’01]: under centralized Bayesian
Mechanism( For Single-Good Auctions, )
♦ Ours: under crowdsourced Bayesian
♦ [Segal’03], [Baliga and Vohra’03]: as
When
♦ Ours: for any n≥2 under crowdsourced Bayesian
Mechanism
Prior-free: Doesn’t need anybody to know D
( For Single-Good Auctions, )
In Sum
ignorantdesigner
4, D|S4
informedplayers
n, D|Sn3, D|S3
2, D|S2
1, D|S1
2-Step DSTCrowdsourced
Bayesian
Thank you!
Complete Information
1 2 …n
informedplayers
ignorantdesigner
MR’88JPS’94AM’92GP’96CHM’10ACM’10
2-Step Dominant-Strategy TruthfulRecall: DST mechanism
Define: 2-Step DST mechanism
Each i’s strategy space
1.
2.
3.
Mechanism
AnalysisBSR [B’50]:
♦ Choose a player i uniformly at random
1. Player i announces
2. Each other player j announces
♦ Run the optimal DST mechanism with
♦ Reward i using Brier’s Scoring Rule
Mechanism
Analysis: 2-Step DST
(b) Brier’s SR [B’50]:
♦ Choose a player i uniformly at random
1. Player i announces
2. Each other player j announces
♦ Run the optimal DST mechanism M with
♦ Reward i using Brier’s Scoring Rule
for -i
(a) M DST announcing is dominant for j≠i
Allegedly:Allegedly:
Player i gets nothing and pays nothing
announcing is 2-step DST for i
Mechanism
Analysis: RevenueConvex mechanism M: for any partition P of the valuations space,
M is convex
♦ Choose a player i uniformly at random
1. Player i announces
2. Each other player j announces
♦ Run the optimal DST mechanism M with
♦ Reward i using Brier’s Scoring Rule
for -i
M is optimal
Generalization♦ Recall
♦ Generalization
Incomplete Information
Centralized Bayesian Assumption: Designer knows D
But: Why should the designer know?
Mechanism gets players’ strategies and D
Bayesian:
Crowdsourced Bayesian
ignorant4, …
informedplayers
n, …3, …
2, …
1, …
designer
Strong Crowdsourced Bayesian Assumption: D is common knowledge to the players
Crowdsourced Bayesian
Mechanism gets players’ strategies only
Knowledge is distributed among individual players
Each player i has no information about θ-i beyond D|θi
More information incentive to deviate
Indeed very strong
I knows that he knows that I knows that he knows that …
Bayesian Nash equilibrium requires even more:We require even less …
Single-parameter games satisfying some propertyDhangwatnotai, Roughgarden, and Yan’10: approximate optimal revenue when n goes infinity
Mechanism
[B’50]:
♦ Choose a player i uniformly at random
1. Player i announces
2. Each other player j announces
♦ Run the optimal DST mechanism M with
♦ Reward i using Brier’s Scoring Rule
for -i
Allegedly:Allegedly:
Player i gets nothing and pays nothing
bounded in [-2, 0]
to a real numberexpectation maximized if
♦ Choose a player i uniformly at random
1. Player i announces
2. Each other player j announces
♦ Run the optimal DST mechanism M with
♦ Reward i using Brier’s Scoring Rule
for -i
Remarks♦ Black-box usage of any DST mechanism M
[Myerson’81] “almost optimal” for single-good auction with independent distribution♦ Works for any n≥2
♦ An existential result
Mechanism