Upper Distribution Independence

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1 Upper Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Upper Distribution Independence. Michael H. Birnbaum California State University, Fullerton. UDI is Violated CPT. CPT violates UDI but EU and RAM satisfy it. TAX violates UDI in the opposite direction as CPT. - PowerPoint PPT Presentation

Transcript of Upper Distribution Independence

Page 1: Upper Distribution Independence

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Upper Distribution Independence

Michael H. BirnbaumCalifornia State University,

Fullerton

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UDI is Violated CPT

• CPT violates UDI but EU and RAM satisfy it.

• TAX violates UDI in the opposite direction as CPT.

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′ z > ′ x > x > y > ′ y > 0

S → ( ′ z ,1− 2p;x, p;y, p)

R → ( ′ z ,1− 2p; ′ x , p; ′ y , p)The upper branch consequence, z’, has different probabilities in the two choices.

′ p > p⇒ 1− 2 ′ p <1− 2p

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Upper Distribution Independence (3-UDI)

′ S = ( ′ z ,1− 2p;x, p;y, p) f

′ R = ( ′ z ,1− 2p; ′ x , p; ′ y , p)

S ′ 2 = ( ′ z ,1− 2 ′ p ;x, ′ p ;y, ′ p ) f

R ′ 2 = ( ′ z ,1− 2 ′ p ; ′ x , ′ p ; ′ y , ′ p )

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Example Test

S’: .10 to win $40

.10 to win $44

.80 to win $100

R’: .10 to win $4

.10 to win $96

.80 to win $100

S2’: .45 to win $40

.45 to win $44

.10 to win $100

R2’: .45 to win $4

.45 to win $96

.10 to win $100

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Generic Configural Model

U(G) = w1u( ′ z ) + w2u(x) + w3u(y)

where

u( ′ z ) > u(x) > u(y) > 0

CPT, RAM, and TAX disagree on

w1,w2,w3

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Generic Configural Model

w1u( ′ z ) + w2u(x) + w3u(y) > w1u( ′ z ) + w2u( ′ x ) + w3u( ′ y )

The generic model includes RDU, CPT, EU, RAM, TAX, GDU, as special cases.

′ S f ′ R ⇔

⇔w3

w2

>u( ′ x ) − u(x)

u(y) − u( ′ y )

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Violation of 3-UDI

′ w 1u( ′ z ) + ′ w 2u(x) + ′ w 3u(y) < ′ w 1u( ′ z ) + ′ w 2u( ′ x ) + ′ w 3u( ′ y )

A violation will occur if S’ f R’ and

S ′ 2 p R ′ 2 ⇔

⇔′ w 3′ w 2

<u( ′ x ) − u(x)

u(y) − u( ′ y )

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2 Types of Violations:

′ S f ′ R ∧S ′ 2 p R ′ 2 ⇔w3

w2

>u( ′ x ) − u(x)

u(y) − u( ′ y )>

′ w 3′ w 2

′ S p ′ R ∧S ′ 2 f R ′ 2 ⇔w3

w2

<u( ′ x ) − u(x)

u(y) − u( ′ y )<

′ w 3′ w 2

S’R2’:

R’S2’:

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EU allows no violations

• In EU, the weights are equal to the probabilities; therefore

w3

w2

=p

p=

′ p ′ p =

′ w 3′ w 2

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RAM Weights

w1 = a(1,3)t(1− 2p) /T

w2 = a(2,3)t(p) /T

w3 = a(3,3)t(p) /T

T = a(1,3)t(1− 2 p) + a(2,3)t( p) + a(3,3)t( p)

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RAM allows no Violations

• RAM model with any parameters satisfies 3-UDI.

w3

w2

=a(3,3)t(p)

a(2,3)t(p)=

a(3,3)t( ′ p )

a(2,3)t( ′ p )=

′ w 3′ w 2

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Cumulative Prospect Theory/ RDU

w1 = W (1− 2p)

w2 = W (1− p) −W (1− 2p)

w3 =1−W (1− p)

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CPT implies violations

• If W(P) = P, CPT reduces to EU.• From previous data, we can

calculate where to expect violations and predict which type of violation should be observed.

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CPT Analysis of 3-UDI Choices 15 & 18

0.4

0.6

0.8

1

0.6 0.8 1 1.2 1.4Weighting Function Parameter, γ

, Exponent of Utility Function

β

2R'S '

2S'R '

2S'S '

2R'R '

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CPT implies S’R2’ Violations

• When γ = 1, CPT reduces to EU.• Given the inverse-S weighting function,

the fitted CPT model implies S’R2’ pattern.

• If γ > 1, S-Shaped, but the model can handle the opposite pattern.

• A series of tests can be devised to provide overlapping combinations of parameters.

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TAX Model

Each term has the same denominator; however, unlike the case of LDI, here the middle branch can gain more weight than it gives up.

w1 =t(1− 2p) − 2δt(1− 2 p) /4

t(1− 2 p) + t(p) + t( p)

w2 =t( p) + δt(1− 2p) /4 −δt( p) /4

t(1− 2p) + t( p) + t(p)

w3 =t( p) + δt(1− 2p) /4 + δt( p) /4

t(1− 2p) + t( p) + t(p)

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Special TAX: R’S2’ Violations

• Special TAX model violates 3-UDI. • Here the ratio depends on p.

w3

w2

=t( p) + δt(1− 2p) /4 + δ t(p) /4

t( p) + δt(1− 2p) /4 −δ t(p) /4<

′ w 3′ w 2

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Summary of Predictions

• RAM, & EU satisfy 3-UDI• CPT violates 3-UDI: S’R2’

• TAX violates 3-UDI: R’S2’

• Here CPT is the most flexible model, RAM defends the null hypothesis, TAX makes opposite prediction from that of CPT.

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Results n = 1075

′ S

′ R 1075

.10 to win $40

.10 to win $44

.80 to win $100

.10 to win $4

.10 to win $96

.80 to win $100

56

.45 to win $40

.45 to win $44

.10 to win $100

.45 to win $4

.45 to win $96

.10 to win $100

33

R’S2’

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Results: n = 503

20 white to win $28

20 blue to win $30

60 red to win $100

20 yellow to win $4

20 green to win $96

60 black to win $100

70.8*

45 white to win $28

45 purple to win $30

10 blue to win $100

45 black to win $4

45 green to win $96

10 red to win $100

59.1*

R’R2’ (CPT predicted S’R2’ )

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Summary: Observed Violations fit TAX, not CPT

• RAM and EU are refuted in this case by systematic violations.

• TAX model, fit to previous data correctly predicted the modal choices.

• Violations opposite those implied by CPT with its inverse-S W(P) function.

• Fitted CPT was correct when it agreed with TAX, wrong otherwise.

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To Rescue CPT:

• CPT can handle the result of any single test, by choosing suitable parameters.

• For CPT to handle these data, let γ

> 1; i.e., an S-shaped W(P) function, contrary to previous inverse- S.

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CPT Analysis of 3-UDI Choices 15 & 18

0.4

0.6

0.8

1

0.6 0.8 1 1.2 1.4Weighting Function Parameter, γ

, Exponent of Utility Function

β

2R'S '

2S'R '

2S'S '

2R'R '

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Adds to the case against CPT/RDU/RSDU

• Violations of 3-UDI favor TAX over RAM and are opposite predictions of CPT.

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Preview of Next Program

• The next programs reviews tests of Restricted Branch Independence (RBI).

• It turns out the violations of 3-RBI are opposite the predictions of CPT with inverse-S function.

• They refute EU but are consistent with RAM and TAX.