SORTING VS SCREENING - Cowles FoundationSORTING VS SCREENING- PRICES AS OPTIMAL COMPETITIVE SALES...

SORTING VS SCREENING -PRICES AS OPTIMAL COMPETITIVE SALES MECHANISMS

Jan Eeckhout1 Philipp Kircher2

1 University Pompeu Fabra – 2 Oxford University – 1,2 University of Pennsylvania

Cowles Foundation and JET Symposium on Search TheorySeptember 2008

BROAD MOTIVATION

• price posting is pervasive(even eBay has half of its goods sold via a posted price)

• yet in many settings auctions are superior(standard auctions theory, McAffee ’93, Peters ’97)

• obvious explanations: transaction costs, no scarcity

• we propose an additional explanation based on the meetingtechnology:

• are meetings a rival good for the buyers?(externalities in meeting)

• auctions screen ex post; prices induce sorting ex ante - andex-ante sorting if meetings are a rival good

BROAD MOTIVATION

MatchingMatching

BilateralMatching

BROAD MOTIVATION

MatchingSelectionMeeting + =

Trading

BilateralMatching

Trading Protocol(Mechanism)

MeetingTechnology

(Mechanism)

Search Choices

BROAD MOTIVATION

Trading

BilateralMatchingShi (2002),

Trading Protocol(Mechanism)

MeetingTechnology

(Mechanism)

Search Choices

• How are goods/labor sold depending on the frictions? (fixed prices/auctions/bargaining)• How is competing mechanism design affected by the meeting process?

BROAD MOTIVATIONMEETING FUNCTION EXAMPLE

E l M ti T h lExample Meeting Technology: • urnball application process• N applications can be openedpp p

E l M ti T h lExample Meeting Technology: • urnball application process• N applications can be opened

N =∞

Why does it matter: (1) Types of feasible mechanisms. (2) Interaction among types!

N =∞

good type

N =∞

N (meetings are non‐rival)

N = 2(meetings are rival)

N = 1(meetings are fully rival)

good type

N =∞

pp p“Directed Search”:Peters (1984, 1991, 1997a,b, …);McAffee (1993); Burdett, Shi, Wright (2001); Shi (2002); Shimer (2005); Albrecht GautierN Shi (2002); Shimer (2005); Albrecht, Gautier, Vroman (2006); Galenianos, Kircher (2009)

((Moscarini 2001,…))(meetings are non‐rival)

N = 1 “Competitive Search”:Shi (2001); Rocheteau, Wright (2005);Guerrieri (2008), Menzio (‘09); Eeckhout Kircher (‘09);

(meetings are fully rival)Eeckhout, Kircher ( 09);Guerrieri, Shimer, Wright (2009)(Moen 1997; Mortensen, Wright 2003...)

((Albrecht, Vroman 2002,…))

N =∞

pp p“Directed Search”:Peters (1984, 1991, 1997a,b, …);McAffee (1993); Burdett, Shi, Wright (2001); Shi (2002); Shimer (2005); Albrecht GautierN Shi (2002); Shimer (2005); Albrecht, Gautier, Vroman (2006); Galenianos, Kircher (2009)

((Moscarini 2001,…))(meetings are non‐rival)

N = 1 “Competitive Search”:Shi (2001); Rocheteau, Wright (2005);Guerrieri (2008), Menzio (‘09); Eeckhout Kircher (‘09);

(meetings are fully rival)

),How does the nature of the frictions affect the type of trading protocol (mechanism)?

Eeckhout, Kircher ( 09);Guerrieri, Shimer, Wright (2009)(Moen 1997; Mortensen, Wright 2003...)

((Albrecht, Vroman 2002,…))

THIS PAPER’S APPROACHThe approach in this paper:

• Lay out (potentially) multilateral meeting function

• Specify mechanism space

• Analyze which mechanisms (homogeneous, risk-neutral) sellersuse to attract (risk-neutral) buyers• homogeneous buyers• heterogeneous buyers with private values (v̄ , v )

The game:

1 each buyer draws private value (if heterogeneous).

2 each seller posts mechanisms m.

3 each buyer decides which mechanisms m to seek.

4 this gives buyer-seller ratios at each mechanism.

5 meeting function: how many buyers of each type arrive at seller.

6 mechanisms are being played.

THIS PAPER’S APPROACHThe approach in this paper:

• Lay out (potentially) multilateral meeting function

• Specify mechanism space

• Analyze which mechanisms (homogeneous, risk-neutral) sellersuse to attract (risk-neutral) buyers• homogeneous buyers• heterogeneous buyers with private values (v̄ , v )

The game:

1 each buyer draws private value (if heterogeneous).

2 each seller posts mechanisms m.

3 each buyer decides which mechanisms m to seek.

4 this gives buyer-seller ratios at each mechanism.

5 meeting function: how many buyers of each type arrive at seller.

6 mechanisms are being played.

RESEARCH QUESTIONS

Questions (focused on price posting relative to auctions, bargaining...):

• When is price posting profitable/equilibrium? When isprice-posting outcome socially efficient?

• What is the relationship to random search?

• What is the relationship to the meeting technology?[Difference: "competitive" vs "directed" search]

Open questions even for standard urnball (N = ∞):

• McAffee (1993) shows that auctions are always a best reply, andstrictly preferred to price posting if individual seller is uncertainabout buyer types.

• But under price posting each seller only faces one buyer type(no uncertainty), and allocation satisfy some planner problem.

• Are auctions only a weak best reply; are prices equally good?

RESEARCH QUESTIONS

Questions (focused on price posting relative to auctions, bargaining...):

• When is price posting profitable/equilibrium? When isprice-posting outcome socially efficient?

• What is the relationship to random search?

• What is the relationship to the meeting technology?[Difference: "competitive" vs "directed" search]

Open questions even for standard urnball (N = ∞):

• McAffee (1993) shows that auctions are always a best reply, andstrictly preferred to price posting if individual seller is uncertainabout buyer types.

• But under price posting each seller only faces one buyer type(no uncertainty), and allocation satisfy some planner problem.

• Are auctions only a weak best reply; are prices equally good?

RESULTS

h bhomogeneous buyers:

Price Posting

a) equilibrium is constrained efficientb) search is random in equilibrium (and this is efficient)

Posting Arbitrary Mechanisms (for any meetings function)Looks like second price auctions under random search

RESULTS

Price Posting

heterogeneous buyers(high and low valuations):(high and low valuations):

Price Postinga) constrained efficient if only non‐discriminatory mech. allowedb) search is non‐random in equilibriumc) perfect screening (no two buyer types at same seller)

Mechansisms (MultilateralMeet.) (non‐rival)Mechanisms (BilateralMeet.) (rival)price‐posting equ efficient if all mech allowed

⇔ ⇔

c) perfect screening (no two buyer types at same seller)

price‐posting equ not eff if all mech allowedprice‐posting equ. efficient if all mech. allowedprices are equiliriummech. random search is not efficient

price‐posting equ. not eff. if all mech. allowedprices are not equiliriummech. random search is efficient

RESULTS

Price Posting

heterogeneous buyers(high and low valuations):(high and low valuations):

Price Postinga) constrained efficient if only non‐discriminatory mech. allowedb) search is non‐random in equilibriumc) perfect screening (no two buyer types at same seller)

Mechansisms (MultilateralMeet.) (non‐rival)Mechanisms (Multilat.Meet.) (rival)price‐posting equ efficient if all mech allowed

⇔ ⇔

c) perfect screening (no two buyer types at same seller)

price‐posting equ not eff if all mech allowedprice‐posting equ. efficient if all mech. allowedprices are equiliriummech. random search is not efficient

price‐posting equ. not eff. if all mech. allowedprices are not equiliriummech. random search is efficient

THE MODELMEETING FUNCTION - MECHANISMS - PLAYERS AND COMPETITION

Consider “island” m with• a measure 1 of homogenous sellers• a measure λ of (possibly heterogeneous) buyers

The meeting function (depending on λ):• Pn(λ): Prob. that seller meets n buyers• Qn(λ): Prob. that buyer meets a seller with n buyers.

Assumptions on meeting function:• Consistency: nPn(λ) = λQn(λ), ∀n ≥ 1

• Monotonicity (FOSD):∑∞

n Pn(λ) increasing, ∀n ≥ 1.

• Concavity: 1− P0(λ) str . concave.

• Heterogeneity: λ high and λ high types imply probabilityλ/(λ + λ) that a buyer in a match is high type.

→ Q̃n,n(λ, λ)

• Heterogeneity: λ high and λ high types imply probabilityλ/(λ + λ) that a buyer in a match is high type. → Q̃n,n(λ, λ)

Examples:

• urnball: Pn(λ) = λne−λ/n!

• Kiyotaki-Wright: P1(λ) = αλ/(1 + λ) = 1− P0(λ)

Definition 1: Directed Search (meetings are not rival)

• 1−Q0(λ) constant

• example: urnball 1−Q0(λ) = 1

Definition 2: Competitive Search (meetings are fully rival)

• λ(1−Q0(λ)) = 1− P0(λ) [if and only if bilateral meeting]

• example: Kiyotaki-Wright

Definition 3: Intermediate (meetings are partially rival)

• neither 1 nor 3

• example: prob. γ open all envelopes, prob.1− γ open one

• example: prob. γ urnball, prob. 1− γ Kiyotaki-Wright

Mechanism Space M is a subspace of anonymous extensive formgames with representation: Given n low buyers

and n of high buyers

• seller’s exp payoff: πn

• low buyer exp utility: un

trade probability xn

• high buyer exp utility: un,n trade probability xn,n

Resource Constraint:

+ n un

,n + n un,n

≤ n xn

+ n xn,n v .

Ex-Ante Incentive Compatibility:

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

Q̃n,n(λ, λ)un,n+1

Examples: posting price p with representation πn

= p and un

= (v − p)/

, bargaining, auctions,....

Mechanism Space M is a subspace of anonymous extensive formgames with representation: Given n low buyers and n of high buyers

• seller’s exp payoff: πn

• low buyer exp utility: un

trade probability xn

+ n un

,n + n un,n

≤ n xn

+ n xn,n v .

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

Examples: posting price p with representation πn

= p and un

= (v − p)/

, bargaining, auctions,....

• seller’s exp payoff: πn,n

• low buyer exp utility: un,n trade probability xn,n

πn,n + n un,n

+ n un,n

≤ n xn,n v

+ n xn,n v .

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

Examples: posting price p with representation πn,n = p and un,n = (v − p)/(n + n), bargaining, auctions,....

πn,n + n un,n

+ n un,n

≤ n xn,n v

+ n xn,n v .

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

πn,n + n un,n

+ n un,n

≤ n xn,n v + n xn,n v .

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

πn,n + n un,n + n un,n ≤ n xn,n v + n xn,n v .

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

πn,n + n un,n + n un,n ≤ n xn,n v + n xn,n v .

LOW :∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,n ≥∞∑

∞∑n=0

Q̃n,n(λ, λ)[un,n+1 + xn,n+1(v − v)

]HIGH :

∞∑n=0

Q̃n,n(λ, λ)[un+1,n + xn+1,n(v − v)

∞∑n=0

THE MODELMEETING FUNCTION - MECHANISMS - PLAYERS AND GAME

Large Game (Mas-Colell 1984). Players:

• Measure 1 of homogeneous sellers

• Measure b (b) of buyers with private value v (v)

The Market Interaction:

• Sellers post mechanisms m ∈M according to measure µ

• Buyers choose mechanisms m ∈ Supp(µ) according to measureν (ν)

• On Supp(µ): buyer-seller-ratio λ = dν/dµ (λ = dν/dµ) .

Overall expected payoffs from choosing m:

• seller: Π(m|µ, ν, ν) =∑∞

n=0∑∞

n=0 Pn,n(λ(m), λ(m))πmn,n

• low buyer: U(m|µ, ν, ν) =∑∞

n=0∑∞

n=0 Q̃n,n(λ(m), λ(m))umn+1,n

• high buyer: U(m|µ, ν, ν) =∑∞

n=0∑∞

n=0 Q̃n,n(λ(m), λ(m))umn,n+1

• For low buyers m is in the support of ν only if

U(m|µ, ν, ν) = U ≡ maxm̃∈Supp(µ)

U(m̃|µ, ν, ν) (1)

• For high buyers m is in the support of ν only if

U(m̃|µ, ν, ν) (2)

• At off-equilibrium mech. the queue λ(m), λ(m) s.t.

• U ≥∑∞

n=0∑∞

n=0 Q̃n,n(λ(m), λ(m))umn+1,n; “ =′′ iff λ(m) > 0

• U ≥∑∞

n=0∑∞

n=0 Q̃n,n(λ(m), λ(m))umn,n+1; “ =′′ iff λ(m) > 0

(for general mech. not necessarily unique; assumption: seller coordinates buyers (McAffee ’93))

• Sellers post m in the support of µ only if

Π(m|µ, ν, ν) = Π ≡ maxm̃∈M

Π(m̃|µ, ν, ν) (3)��

��

DEFINITION (EQUILIBRIUM )An equilibrium is a tuple (µ, ν, ν) s.t. (1), (2) and (3) hold.

U(m̃|µ, ν, ν) (1)

U(m̃|µ, ν, ν) (2)

• U ≥∑∞

n=0∑∞

• U ≥∑∞

n=0∑∞

Π(m̃|µ, ν, ν) (3)��

��

U(m̃|µ, ν, ν) (1)

U(m̃|µ, ν, ν) (2)

• U ≥∑∞

n=0∑∞

• U ≥∑∞

n=0∑∞

Π(m̃|µ, ν, ν) (3)��

��

U(m̃|µ, ν, ν) (1)

U(m̃|µ, ν, ν) (2)

• U ≥∑∞

n=0∑∞

• U ≥∑∞

n=0∑∞

Π(m̃|µ, ν, ν) (3)

��

U(m̃|µ, ν, ν) (1)

U(m̃|µ, ν, ν) (2)

• U ≥∑∞

n=0∑∞

• U ≥∑∞

n=0∑∞

Π(m̃|µ, ν, ν) (3)��

��

MECHANISMS - THE SELLER’S PROBLEM

In equilibrium with U∗ and U∗

the seller chooses mechanism mand (λ, λ) such that

max(λ,λ)∈R2

+, m∈M

∞∑n=0

Pn,n(λ, λ)πn,nm (4)

such that∞∑

∞∑n=0

Q̃n,n(λ, λ)un+1,nm = U∗ if λ > 0, (5)

∞∑n=0

Q̃n,n(λ, λ)umn,n+1 = U

∗. if λ > 0, (6)

(fulfilling incentive compatibility and the resource constraint).

Under full trade πmn + num

n = v ⇒ Π(m, λ) = 1− P0(λ)− λU∗

In equilibrium with U∗

and U∗

the seller chooses mechanism mand (λ

) such that

max(λ

+, m∈M

∞∑n=0

such that∞∑

∞∑n=0

= U∗ if λ > 0, (5)

∞∑n=0

∗. if λ > 0,

(fulfilling incentive compatibility and the resource constraint).Under full trade πm

n + numn = v ⇒ Π(m, λ) = 1− P0(λ)− λU∗

In equilibrium with U∗

and U∗

the seller chooses mechanism mand (λ

) such that

max(λ

+, m∈M

∞∑n=0

such that∞∑

∞∑n=0

= U∗ if λ > 0, (5)

∞∑n=0

∗. if λ > 0,

(fulfilling incentive compatibility and the resource constraint).

Under full trade πmn + num

n = v ⇒ Π(m, λ) = 1− P0(λ)− λU∗

PRICE POSTING AND MECHANISMS1.1) HOMOGENEOUS BUYERS�

�PROPOSITION (PRICE POSTING W/ HOMOG. BUYERS )Under price posting, in equilibrium one price is offered, buyersselect at random, and randomness is efficient

Def.: A class of mechanisms is payoff complete if it has some dimension (likethe reserve price in an auction) to shift payoffs between buyers and sellers.�

PROPOSITION (EQUIVALENCE )In any class of pay-off complete (full-trade) mechanisms• an equilibrium mechanism exists• remains equilibrium mech. when other mech. are added• equilibrium payoffs are identical as under price posting• search is (essentially) random.

Generalized Camera and Selcuk (2009), Kultti (1999). Second price auctions: r∗ = 1 + λ∗P′0(λ∗)/P1(λ∗)

PRICE POSTING1.2) HETEROGENEOUS BUYERS�

PROPOSITION (PRICE POSTING W/ HETEROG. BUYERS )Price Posting leads in equilibrium to• two prices, one for each type• buyers separate by "voting with their feet"• constrained efficient given frictions and within the class of

non-discriminatory mechanisms (Hosios’ Condition).

Seller’s maximization problem

maxp,λ,λ

[1− P0(λ + λ)]p

s.t.∑n≥1

Qn(λ + λ)[v − p]

n= U∗ if λ > 0

s.t.∑n≥1

Qn(λ + λ)[v − p]

∗if λ > 0

Sketch of separation argument:• low types want low price more (single crossing property)• pricing effectively separates the types.

PRICE POSTING1.2) HETEROGENEOUS BUYERS�

PROPOSITION (PRICE POSTING W/ HETEROG. BUYERS )Price Posting leads in equilibrium to• two prices, one for each type• buyers separate by "voting with their feet"• constrained efficient given frictions and within the class of

non-discriminatory mechanisms (Hosios’ Condition).

Seller’s maximization problem

maxp,λ,λ

[1− P0(λ + λ)]p

s.t.∑n≥1

Qn(λ + λ)[v − p]

n= U∗ if λ > 0

s.t.∑n≥1

Qn(λ + λ)[v − p]

∗if λ > 0

Sketch of separation argument:• low types want low price more (single crossing property)• pricing effectively separates the types.

PRICE POSTING1.2) SINGLE CROSSING - SEPARATION "BY FEET"

Buyer’s indifference curves

Iso-profit curve at a single market price

Equilibrium with two prices

MECHANISM POSTING2.1) “DIRECTED” (MULTILATERAL MATCHING - NON-RIVAL)�

PROPOSITION (MECHANISM POSTING - NON-RIVAL MEETINGS )If meetings are non-rival• Identical auctions are more efficient than price posting• Price posting is not an equ. when auction are available.

Sketch of Proof:

• Random search yields most matches [1− P0 concave]

• More matches with identical auctions than w/ price posting

• High types choose randomly and get the object fist

• ⇒ most matches for high types.

• Most matches & most matches for high types ⇒ efficiency.

• Individual deviation to auction mechanisms is profitable.

MECHANISM POSTING2.2.) "COMPETITIVE" (BILATERAL MEETING - FULLY RIVAL)�

PROPOSITION (MECHANISM POSTING - FULLY RIVAL )If meetings are fully rival (bilateral meetings)

• Price posting is constrained efficient.

• Price posting is an equilibrium.

• Random search is never constrained efficient.

Sketch of Proof:

• The presence of low types precludes high types from meeting.

• Sellers never see high types when a low type is present.

• All "selection" before the seller can intervene.

• Best not to mix types.

• Under separation: prices do a good job.

MECHANISM POSTING2.3) THE INTERMEDIATE CASE - SOME EXTERNALITIES

Definition: Full information surplus SF (λ, λ) = PH(λ, λ)v + PL(λ, λ)v�

PROPOSITION (MECHANISM POSTING - PARTIALLY RIVAL )Assume for all λ, λ > 0 there exists α ∈ [0, 1] s.t.

SF (λ, λ) ≷ αSF (λ/α, 0) + (1− α)SF (0, λ/(1− α))

• “<” then price posting is an equilibrium

• “>” then price posting is not an equilibrium

Lemma: For given v , v and meetings P = γPC + (1− γ)PD

• γ close to zero: “<”• γ close to one: “>”

Lemma: For given v and any P with partial crowding• v small: “<”• v large: “>”

Definition: Full information surplus SF (λ, λ) = PH(λ, λ)v + PL(λ, λ)v�

PROPOSITION (MECHANISM POSTING - PARTIALLY RIVAL )Assume for all λ, λ > 0 there exists α ∈ [0, 1] s.t.

SF (λ, λ) ≷ αSF (λ/α, 0) + (1− α)SF (0, λ/(1− α))

• “<” then price posting is an equilibrium

• “>” then price posting is not an equilibrium

Lemma: For given v , v and meetings P = γPC + (1− γ)PD

• γ close to zero: “<”• γ close to one: “>”

Lemma: For given v and any P with partial crowding• v small: “<”• v large: “>”

Proof: Consider deviant seller attracting λ, λ > 0. Resource constraint:

πn,n + n un,n + n un,n ≤

v if n > 0v if n = 0, n > 00 otherwise

⇒∑

Pn,n(λ, λ)[πn,n + n un,n + n un,n] ≤ SF (λ, λ)

Recall nPn = λQn and∑ ∑

Qn u = U, then

Π + λ U + λ U ≤ SF (λ, λ) < αSF (λ

α, 0) + (1− α)SF (0,

1− α)

Consider playing lottery over prices:• p attracting λ/α low buyers at U: SF ( λ

α, 0) = Π(p) + λ

• p attracting λ1−α

high buyers at U: SF (0, λ1−α

) = Π(p) + λ1−α

Π + λ U + λ U ≤ α[Π(p) + (λ/α)U] + (1− α)[Π(p) + (λ/(1− α))U]

⇔ Π < αΠ(p) + (1− α)Π(p)

Sellers prefer to use prices. And there is a unique price posting equ.

⇒∑

Qn u = U, then

Π + λ U + λ U ≤ SF (λ, λ)

< αSF (λ

α, 0) + (1− α)SF (0,

1− α)

α, 0) = Π(p) + λ

) = Π(p) + λ1−α

⇔ Π < αΠ(p) + (1− α)Π(p)

⇒∑

Qn u = U, then

Π + λ U + λ U ≤ SF (λ, λ) < αSF (λ

α, 0) + (1− α)SF (0,

1− α)

α, 0) = Π(p) + λ

) = Π(p) + λ1−α

⇔ Π < αΠ(p) + (1− α)Π(p)

⇒∑

Qn u = U, then

Π + λ U + λ U ≤ SF (λ, λ) < αSF (λ

α, 0) + (1− α)SF (0,

1− α)

α, 0) = Π(p) + λ

) = Π(p) + λ1−α

⇔ Π < αΠ(p) + (1− α)Π(p)

⇒∑

Qn u = U, then

Π + λ U + λ U ≤ SF (λ, λ) < αSF (λ

α, 0) + (1− α)SF (0,

1− α)

α, 0) = Π(p) + λ

) = Π(p) + λ1−α

⇔ Π < αΠ(p) + (1− α)Π(p)

⇒∑

Qn u = U, then

Π + λ U + λ U ≤ SF (λ, λ) < αSF (λ

α, 0) + (1− α)SF (0,

1− α)

α, 0) = Π(p) + λ

) = Π(p) + λ1−α

⇔ Π < αΠ(p) + (1− α)Π(p)

CONCLUSION

• Bilatateral (rival ) meetings (“Competitive”) or homog.:• Prices are equilibrium, and allocation is constrained

efficient(other mechanisms only replicate the pricing outcome)

• Random search is not efficient under buyer heterogeneity.

• Multilateral non-rival meetings (“Directed”):• Prices are not equilibrium. Posting allocation not

constrained efficient(when auction mechanisms are available).

• Random search is efficient(when auction mechanisms are available – Caveat: onlywhen sellers are homogeneous).

• Larger relevance:• Clarifies when prices do a "good job".• Highlights the relevance of the meeting technology for

competing mechanisms design.• Highlights when we can focus on one buyer type (even

under additional problems such as moral hazard ect.)

Buyer’s indifference curves

Iso-profit curve at a single market price

Equilibrium with two prices

SORTING VS SCREENING - Cowles FoundationSORTING VS SCREENING- PRICES AS OPTIMAL COMPETITIVE SALES...

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Henry Chandler Cowles and Succession

Symon Cowles

COWLES FOUNDATION DISCUSSION PAPER NO. 2263

Instruction Manual - Keystone View Vision Screeners · 2018. 10. 24. · VS-V PEDIATRIC SCREENING With the introduction of the VS type instrument, vision screening becomes more complete

COWLES FOUNDATION FOR RESEARCH IN ECONOMICS AT YALE UNIVERSITY Box 2125, Yale Station COWLES FOUNDATION DISCUSSION PAPER NO… · COWLES FOUNDATION DISCUSSION PAPER NO. 1150 Note:

Screening vs. gevolution: in chase of a perfect

Employment Screening: Due Diligence vs Data Dumps [Infographic]

VS-V Pediatric manual - Keystone View Vision Screeners€¦ · VS-V PEDIATRIC SCREENING With the introduction of the VS type instrument, vision screening becomes more complete and

Latest research: Neonics vs. pollinators · 2018-01-24 · Latest research: Neonics vs. pollinators Richard Cowles, CT Agric. Expt. Station, Valley Lab. Overview ... American Bee

CREDIT CARDS AND INFLATION By June 2009 COWLES …June 2009 COWLES FOUNDATION DISCUSSION PAPER NO. 1709 COWLES FOUNDATION FOR RESEARCH IN ECONOMICS YALE UNIVERSITY Box 208281 New Haven,

Colon and Rectal Cancer Screening: Home Based Tests vs ...€¦ · Colon and Rectal Cancer Screening: Home Based Tests vs. Colonoscopy John A Dumot, DO, FASGE Director, Digestive

COWLES FOUNDATION FOR RESEARCH IN … PROFITS IN THE AMERICAN ECONOMY: THEORY AND MEASUREMENT By William D. Nordhaus April 2004 COWLES FOUNDATION DISC USSION PAPER NO. 1457 COWLES

P. BURKE vs A. PONTI - Rete Oncologica Piemonte · senologia diagnostica ovvero screening opportunistico vs screening mammografico programmato costi e benefici p. burke vs a. ponti

VAR Modelling Approach and Cowles Commission Heritage · VAR Modelling Approach and Cowles Commission Heritage ... VAR Modelling Approach and Cowles Commission ... The VAR methodology

Colonoscopy vs Fecal Immunochemical Testing in Colorectal Cancer Screening

Cowles Egan - Evolutions Transatlantic

When do Consumers Talk? - Cowles Foundation

BOOSTINGTHEHODRICK-PRESCOTTFILTER By May2019 COWLES

Cowles Library Needs Assessment · Cowles Library Needs Assessment ... Revised DRAFT

d1009.tif231 - Cowles Foundation