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

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

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

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Distribution Independence

Michael H. BirnbaumCalifornia State University,

Fullerton

Page 2: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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4-DI is violated by CPT

If W(P) is nonlinear, we should be able to predict violations of 4-DI from CPT.

• RAM satisfies 4-DI• TAX violates 4-DI in the

opposite way as CPT with its inverse-S weighting function.

Page 3: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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′ z > ′ x > x > y > ′ y > z > 0

S → ( ′ z ,r;x, p;y, p;z,1− 2p − r)

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

We manipulate r in both gambles, r’ > r. This changes where the two equally probable branches fall with respect to the gamble’s distribution.

Page 4: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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4-Distribution Independence (4-DI)

S = ( ′ z ,r;x, p;y, p;z,1− 2p − r) f

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

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

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

Page 5: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Example Test of 4-DIS: 59 to win $3

20 to win $45

20 to win $49

01 to win $109

R: 59 to win $3

20 to win $11

20 to win $97

01 to win $109

S’: 01 to win $3

20 to win $45

20 to win $49

59 to win $109

R’: 01 to win $3

20 to win $11

20 to win $97

59 to win $109

Page 6: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Generic Configural Model

w1u( ′ z ) + w2u(x) + w3u(y) + w4u(z) > w1u( ′ z ) + w2u( ′ x ) + w3u( ′ y ) + w4u(z)

S f R ⇔

⇔w3

w2

>u( ′ x ) − u(x)

u(y) − u( ′ y )

There will be no violation if this ratio is independent of r

Page 7: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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CPT Analysis of S vs. R

Choice between S and R, r small

0

1

0 1

Decumulative Probability, P

Decumulative Weight, W(P)

r p p 1 - 2p - r

w2 > w3

w1

w2

w3

w4

Page 8: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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CPT Analysis of S’ vs. R’Choice between S' and R', r' large

0

1

0 1

Decumulative Probability, P

Decumulative Weight, W(P)

r' p p 1 - 2p - r

w2 < w3

w1

w2

w3

w4

Page 9: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Violation of 4-DI in CPT

w2 > w3 ⇒w3

w2

<1∩ ′ w 2 < ′ w 3 ⇒ 1<′ w 3′ w 2

R ′ S : S p R∧ ′ S f ′ R ⇔w3

w2

<u( ′ x ) − u(x)

u(y) − u( ′ y )<

′ w 3′ w 2

If W(P) has inverse-S shape, the ratios

depend on r. CPT implies RS’.

Page 10: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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RAM Weights

w1 = a(1,4)t(r) /T

w2 = a(2,4)t(p) /T

w3 = a(3,4)t(p) /T

w4 = a(4,4)t(1− 2 p − r) /T

T = a(1,4)t(r) + a(2,4)t(p) + a(3,4)t(p) +

+a(4,4)t(1− 2p − r)

Page 11: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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RAM Satisfies 4-DI• RAM satisfies 4-DI because the

ratio of weights is independent of r.

w3

w2

=a(3,4)t(p)

a(2,4)t(p)=

′ w 3′ w 2

Page 12: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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TAX Model

w1 =t(r) − 3δt(r) /5

t(r) + t(p) + t( p) + t(1− 2 p − r)

w2 =t( p) −δt(p) /5 −δt(p) /5 + δt(r) /5

t(r) + t(p) + t( p) + t(1− 2 p − r)

w3 =t( p) −δt(p) /5 + δt(p) /5 + δt(r) /5

t(r) + t(p) + t( p) + t(1− 2 p − r)

w4 =t(1− 2p − r) + δt( p) /5 + δt( p) /5 + δt(r) /5

t(r) + t( p) + t(p) + t(1− 2p − r)

Page 13: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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TAX Model Implies SR’• TAX violates 4-DI in the opposite

pattern as CPT with inverse-S.• Weight ratios:

• This implies the SR’ pattern.

w3

w2

=t( p) −δt(p) /5 + δt(p) /5 + δt(r) /5

t( p) −δt(p) /5 −δt(p) /5 + δt(r) /5>

′ w 3′ w 2

Page 14: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Summary of Predictions

• EU and RAM satisfy 4-DI.• CPT as fit to previous data violates

4-DI with RS’ pattern. • TAX as fit to previous data predicts

the SR’ pattern of violations.

Page 15: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Study of 4-DI• Birnbaum, M. H., & Chavez, A. (1997).

Tests of Theories of Decision Making: Violations of Branch Independence and Distribution Independence. Organizational Behavior and Human Decision Processes, 71, 161-194.

• 100 participants, 12 tests with (r, r’) = (.01, .59) and (.05, .55).

• Study also tested RBI and other properties. Significantly more SR’ than RS’ violations.

Page 16: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Example TestS: 59 to win $3

20 to win $45

20 to win $49

01 to win $109

R: 59 to win $3

20 to win $11

20 to win $97

01 to win $109

S’: 01 to win $3

20 to win $45

20 to win $49

59 to win $109

R’: 01 to win $3

20 to win $11

20 to win $97

59 to win $109

Page 17: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Results for this Example

Choice Pattern

SS’ SR’ RS’ RR’

43 23* 6 28

Page 18: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Violations predicted by TAX, not CPT

• EU and RAM are refuted by systematic violations of 4-DI.

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

• CPT, with its inverse-S weighting function predicted opposite pattern.

Page 19: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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To Rescue CPT:

• CPT can handle the results if it uses an S-shaped rather than an inverse-S shaped weighting function.

Page 20: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Summary

Property CPT RAM TAX

4-DI RS’Viols No Viols SR’ Viols

UDI S’R2’

ViolsNo Viols R’S2’

Viols

RBI RS’ Viols SR’ Viols SR’ Viols

Page 21: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Summary-Continued

Property CPT RAM TAX

LCI No Viols Viols Viols

UCI No Viols Viols Viols

UTI No Viols R’S1Viols R’S1Viols

LDI RS2 Viols No Viols No Viols

3-2 LDI RS2 Viols No Viols No Viols

Page 22: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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Summary-Continued

• CPT violates RBI, 4-DI, and UDI, but the results show the opposite pattern. It violates 3-LDI and 3-2 LDI, but violations not found. CPT satisfies LCI, UCI, and UTI, but there are systematic violations.

• TAX correctly predicts all 8 results; RAM correct in 6 cases where it agrees with TAX; RAM disproved by violations of 4-DI and UDI.

Page 23: 1 Distribution Independence Michael H. Birnbaum California State University, Fullerton

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End of Series on Tests of Independence

• This presentation concludes the series on Lower and Upper Cumulative Independence, Lower and Upper Distribution Independence, Upper Tail Independence, Restricted Branch Independence, and 4-Distribution Independence.

• If you have not yet viewed them, the series of programs on Stochastic Dominance Violations and Allais Paradoxes will also be of interest, as will the separate programs on various models of decision making.