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![Page 1: Measuring the impact of uncertainty resolution Mohammed Abdellaoui CNRS-GRID, ESTP & ENSAM, France Enrico Diecidue & Ayse Onçüler INSEAD, France ESA conference,](https://reader036.fdocuments.us/reader036/viewer/2022062519/5697bfc91a28abf838ca8fa5/html5/thumbnails/1.jpg)
Measuring the impact of uncertainty resolution
Mohammed AbdellaouiCNRS-GRID, ESTP & ENSAM, France
Enrico Diecidue & Ayse Onçüler INSEAD, France
ESA conference, Roma, June 2007
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Research question & motivations
• How does the evaluation of prospects change when they are to be resolved in the future?
Examples: • Lottery ticket to be drawn today versus in a month• End-of-year bonus as a stock option or cash• New product development• Medical tests
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Research question & motivations
• Intuition: sooner rather than later uncertainty resolution is preferred.
• Motivations: – i) value of perfect information cannot be
negative (Raiffa 1968) – ii) psychological disutility for waiting
(Wu 1999) – iii) opportunity for planning and budgeting.
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Related literature
• Markowitz (1959), Mossin (1969), Kreps & Porteus (1978), Machina (1984), Segal (1990), Albrecht & Weber (1997), Smith (1998), Wakker (1999), Klibanoff & Ozdenoren (2007)
• Wu (1999): – model for evaluating lotteries with delayed resolution
of uncertainty. Model is rank-dependent utility with time dependent probability weighting functions.
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Background and notation
• Interested in (x, p; y)t
uncertainty resolved at t in [0, T], (temporal prospects)
• Outcomes received at T, expressed as changes wrt status quo
• Prospects rank-ordered
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Background and notation (cont.)
• Value of the temporal prospect (x, p; y)t
wit(p)U(x) + (1-wi
t(p))U(y),
where i = + for gains & i = - for losses.
• The decision maker selects the temporal prospect that has the highest evaluation.
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Background and notation
• Interested in 3 functions: wit(p) and U(·)
– The utility function U reflects the desirability of outcomes and satisfies U(0) = 0.
– Outcomes received at the same T, we consider the same utility function U.
• Probability weighting functions strictly increasing satisfy w+
t(0) = w-t(0) = 0
w+t(1) = w-
t(1) = 1 for all t in [0, T].
• The impact of uncertainty resolution at a resolution date t for an event of probability p can be quantified through the comparison of wit(p) and wi0(p).
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Background and notation
• Preferences for two temporal prospects (either gain prospects or loss prospects) with common outcomes but different resolution dates depend only on the probabilities and resolution dates, and not the common outcomes.
• The usefulness of this condition is also emphasized in Wu (1999, p. 172): “weak independence” and formulated as follows: if a temporal prospect (x, p; y)t is preferred to the temporal prospect (x, q; y)t’ for x > y > 0 [x < y < 0] then, for all x’ > y’ [x’ < y’ < 0], the prospect (x’, p; y’)t should be preferred to the prospect (x’, q; y’)t’.
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Measuring the impact of uncertainty resolution
• 56 individual interviews, instructions, training sessions, random draw mechanism, hypothetical questions
• Task: choice between two temporal prospects• Six iterations (i.e. choice questions) to obtain an
indifference• Iterations generated by a bisection method.• Counterbalance; control for response errors: repeated the
third choice question of all indifferences at the end of each step described in Table 1.
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The stimuli
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The stimuli
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The method: 4 stepsSteps Objective
Assessed Quantity
Indifference
Gains
Step 1
Elicitation ofU(.) and w0
+(.)
Gi/6
G13/6
G23/6
G33/6
G43/6
G53/6
Gi/6 ~ (1000, i/6; 0)0, i = 1,…,6
G13/6 ~ (2000, 3/6; 0 )0
G23/6 ~ (2000, 3/6; 1000)0
G33/6 ~ (1000, 3/6; 500 )0
G43/6 ~ (1500, 3/6; 1000)0
G53/6 ~ (2000, 3/6; 1500)0
Step 2
Elicitation ofwT
+(.)
g1
g2
g3
g4
g5
(1000, 1/6; 0)0 ~ (g1, 1/6; 0)T
(1000, 2/6; 0)0 ~ (g2, 2/6; 0)T
(1000, 3/6; 0)0 ~ (g3, 3/6; 0)T
(1000, 4/6; 0)0 ~ (g4, 4/6; 0)T
(1000, 5/6; 0)0 ~ (g5, 5/6; 0)T
Losses
Step 3
Elicitation of U(.) and w0
-(.)
Li/6
L13/6
L23/6
L33/6
L43/6
L53/6
-Li/6 ~ (-1000, i/6; 0)0, i = 1,…,6
-L13/6 ~ (-2000, 3/6; 0 )0
-L23/6 ~ (-2000, 3/6; -1000)0
-L33/6 ~ (-1000, 3/6; -500 )0
-L43/6 ~ (-1500, 3/6; -1000)0
-L53/6 ~ (-2000, 3/6; -1500)0
Step 4
Elicitation ofwT
-(.)
l1
l2
l3
l4
l5
(-1000, 1/6; 0)0 ~ (-l1, 1/6; 0)T
(-1000, 2/6; 0)0 ~ (-l2, 2/6; 0)T
(-1000, 3/6; 0)0 ~ (-l3, 3/6; 0)T
(-1000, 4/6; 0)0 ~ (-l4, 4/6; 0)T
(-1000, 5/6; 0)0 ~ (-l5, 5/6; 0)T
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On step 2
• U and w0+(.) known.
• wT+(.) can be elicited from the indifferences
(1000, i/6; 0)0 ~ (gi, i/6; 0)T, i = 1,…,5
• From these indifferences
wT+(i/6) = w0
+(i/6)[U(1000)/U(gi)],
i = 1, …, 5.
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Results
t = 0 t = T = 6
Median Mean Std. Median Mean Std.
Gains
wt+
(1/6) 0.1971 0.1863 0.0588 0.1576 0.1567 0.0534
wt+ (2/6) 0.3223 0.3107 0.0636 0.2867 0.2802 0.0635
wt+ (3/6) 0.4032 0.4012 0.0646 0.3867 0.3841 0.0697
wt+ (4/6) 0.5291 0.5268 0.0822 0.5285 0.5261 0.0876
wt+ (5/6) 0.7133 0.7031 0.0852 0.7229 0.7238 0.0939
Losses
wt-(1/6) 0.1547 0.1395 0.0747 0.1500 0.1381 0.0702
wt-(2/6) 0.2788 0.2585 0.1003 0.2772 0.2556 0.0973
wt-(3/6) 0.3886 0.3601 0.1032 0.3798 0.3620 0.1091
wt-(4/6) 0.5188 0.4913 0.1275 0.5074 0.4929 0.1426
wt-(5/6) 0.7010 0.6945 0.1176 0.7233 0.7034 0.1602
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Results
1/6 2/6 3/6 4/6 5/60
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Probabilities
Med
ian
Dec
isio
n W
eig
ht
w+0 (.)
w+6 (.)
w-0 (.)
w-6 (.)
NS
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ResultsGains Losses
# : > 0 t test Corr. # : > 0 t test Corr.
w0(1/6) vs. w6(1/6) 49 10.04 0.927 24 0.60 0.973
w0(2/6) vs. w6(2/6) 48 10.82 0.945 27 1.20 0.983
w0(3/6) vs. w6(3/6) 33 4.63 0.917 20 -0.64 0.979
w0(4/6) vs. w6(4/6) 21 0.13 0.897 22 -0.28 0.957
w0(5/6) vs. w6(5/6) 9 -2.90 0.827 18 -0.74 0.837
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Results
0 1/6 2/6 3/6 4/6 5/6 10
1/6
2/6
3/6
4/6
5/6
1 PWFs for gains: Non-delayed vs. delayed uncertainty resolution
Probability p
w+ 0(p
), w
+ 6(p)
w(p)= p
w+0(.)
w+6(.)
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Results
0 1/6 2/6 3/6 4/6 5/6 10
1/6
2/6
3/6
4/6
5/6
1 PWFs for losses: Non-delayed vs. delayed uncertainty resolution
Probability p
w- 0(p
), w
- 6(p)
w(p)= p
w-0(.)
w-6(.)
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Results
Gains Losses
t = 0 t = T = 6 t = 0 t = T = 6
LSA
wt(1/6) - wt(0) vs. wt(3/6) - wt(2/6) 9.51 5.60 3.01 2.78
wt(1/6) - wt(0) vs. wt(4/6) - wt(3/6) 5.09 1.31 0.70 0.64
USA
wt(1) - wt(5/6) vs. wt(3/6) - wt(2/6) 15.84 11.66 11.15 7.86
wt(1) - wt(5/6) vs. wt(4/6) - wt(3/6) 10.94 8.02 8.45 6.19
NS
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Results
w0(p) - w0(p-(1/6)) vs. w6(p) – w6(p-(1/6))
Gains Losses
# : > 0 t test # : > 0 t test
w0(1/6) - w0( 0 ) vs. w6(1/6) – w6( 0 ) 49 10.043 24 0.609
w0(2/6) - w0(1/6) vs. w6(2/6) – w6(1/6) 37 0.381 32 0.828
w0(3/6) - w0(2/6) vs. w6(3/6) – w6(2/6) 18 -3.682 25 -1.619
w0(4/6) - w0(3/6) vs. w6(4/6) – w6(3/6) 9 -5.194 26 0.070
w0(5/6) - w0(4/6) vs. w6(5/6) – w6(4/6) 7 -6.133 20 -0.912
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Results
1/6 2/6 3/6 4/6 5/6-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Probability p
[w
0(p)
- w
0(p-1
/6)]
- [
w6(p
) -
w6(p
-1/6
)]
Gains
Losses
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Conclusions
• First individual elicitation of utility and pwf to understand the impact of delayed resolution: measured decision weights for immediate and delayed resolution of uncertainty
• Observed temporal dimension of the uncertainty; pwf depends on the timing of resolution of uncertainty
• Gains: detected difference for small probabilities• Losses: detected no significant difference• Found U for “more convex” (consistent with recent
study by Noussair & Wu)
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end
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Roadmap
• Research question + motivating examples
• Measurement: method and results
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Remark
• Transformation of probabilities is robust phenomenon in decision under risk– Kahneman & Tversky 1979
• Empirically: Inverse-S shape for probability weighting function – Abdellaoui 2000, – Bleichrodt & Pinto 2000, – Gonzalez & Wu 1999.