a stochastic expected utility theory pavlo r. blavatskyy june 2007
TRANSCRIPT
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A Stochastic Expected Utility Theory
Pavlo R. Blavatskyy
June 2007
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Presentation overview
• Why another decision theory?
• Description of StEUT
• How StEUT explains empirical facts– The Allais Paradox– The fourfold pattern of risk attitudes– Violation of betweenness
• Fit to empirical data
• Conclusions & extensions
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Introduction
• Expected utility theory:– Normative theory (e.g. von Neumann &
Morgenstern, 1944)– Persistent violations (e.g. Allais, 1953)– No clear alternative (e.g. Harless and
Camerer, 1944; Hey and Orme, 1994)– Cumulative prospect theory as the most
successful competitor (e.g. Tversky and Kahneman, 1992)
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Introduction continued
• The stochastic nature of choice under risk:– Experimental evidence — average consistency
rate is 75% (e.g. Camerer, 1989; Starmer & Sugden, 1989; Wu, 1994)
– Variability of responses is higher than the predictive error of various theories (e.g. Hey, 2001)
– Little emphasis on noise in the existing models (e.g. Harless and Camerer, 1994; Hey and Orme, 1994)
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StEUT
• Four assumptions:1. Stochastic expected utility of lottery
is
– Utility function u:R→R is defined over changes in wealth (e.g. Markowitz, 1952)
– Error term ξL is independently and normally distributed with zero mean
nn pxpxL ,;..., 11
L
n
iii xupLU
1
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StEUT continued
2. Stochastic expected utility of a lottery:– Cannot be less than the utility of the lowest
possible outcome– Cannot exceed the utility of the highest
possible outcome
The normal distribution of an error term is truncated
nL
n
iii xuxupxu
1
1
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StEUT continued
3. The standard deviation of random errors is higher for lotteries with a wider range of possible outcomes (ceteris paribus)
4. The standard deviation of random errors converges to zero for lotteries converging to a degenerate lottery
niLpi
,...,1,0lim1
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Explanation of the stylized facts
• The Allais paradox
• The fourfold pattern of risk attitudes
• The generalized common consequence effect
• The common ratio effect
• Violations of betweenness
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The Allais paradox
• The choice pattern
– frequently found in experiments (e.g. Slovic and Tversky, 1974)
– Not explainable by deterministic EUT
1.0,105;89.0,10;01.0,01,10 662
61 LL
11.0,10;89.0,01.0,105;9.0,0 61
62 LL
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The Allais paradox continued
PDF of U(L2)
PDF of U(L1')
PDF of U(L2')
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The fourfold pattern of risk attitudes
• Individuals often exhibit risk aversion over:– Probable gains– Improbable losses
• The same individuals often exhibit risk seeking over:
– Improbable gains– Probable losses
• Simultaneous purchase of insurance and lotto tickets (e.g. Friedman and Savage, 1948)
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The fourfold pattern of risk attitudes continued
• Calculate the certainty equivalent CE
• According to StEUT: LUECEu
LLn
xuxu
LL xuxu
eeuCE
L
Ln
L
L
1
221
2
2
2
21
2
n
iiiL xup
1
Φ(.) is c.d.f. of the normal distribution with zero mean and standard deviation σL
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Fit to experimental data
• Parametric fitting of StEUT to ten datasets:– Tversky and Kahneman (1992)– Gonzalez and Wu (1999)– Wu and Gonzalez (1996)– Camerer and Ho (1994)– Bernasconi (1994)– Camerer (1992)– Camerer (1989)– Conlisk (1989)– Loomes and Sugden (1998)– Hey and Orme (1994)
Aggregate choice pattern
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Fit to experimental data continued
• Utility function defined exactly as the value function of CPT:
• Standard deviation of random errors
• Minimize the weighted sum of squared errors
0,
0,
xx
xxxu
n
iinL pxuxu
11 1
n
ii
StEUTi CECEWSSE
1
21
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Fit to experimental data continuedExperimental study WSSE, CPT WSSE, StEUTTversky and Kahneman (1992) 0.5092
0.6601
0.6672
0.4889
Gonzalez and Wu (1999) 17.4612 15.4721
Wu and Gonzalez (1996) 0.2419 0.2183
Camerer and Ho (1994) 0.1895 0.1860
Bernasconi (1994) 1.3609 1.1452
Camerer (1992) large gains
Camerer (1992) small gains
Camerer (1992) small losses
0.0122
0.0122
0.0416
0.0207
0.0115
0.0262
Camerer (1989) large gains
Camerer (1989) small gains
Camerer (1989) small losses
0.1996
0.1871
0.2170
0.2359
0.1639
0.1281
Conlisk (1989) 0.0196 0.0195
Loomes and Sugden (1998) 5.6009 2.2116
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The effect of monetary incentives
Experimental study Type of incentives
Best fitting parameters of StEUT
Power of utility function
Standard deviation of random errors
Tversky and Kahneman (1992) hypothetical0.7750
(0.7621)0.77110.6075
Gonzalez and Wu (1999) hypothetical + auction 0.4416 1.4028
Wu and Gonzalez (1996) hypothetical 0.1720 0.8185
Camerer and Ho (1994)a randomly chosen subject plays lottery
0.5215 0.1243
Bernasconi (1994)random lottery incentive scheme
0.2094 0.2766
Camerer (1992) hypothetical0.58710.9123
(0.5182)
0.08680.09140.2299
Camerer (1989)
hypothetical 0.3037 0.4816
random lottery incentive scheme
0.6830(0.6207)
0.28970.2252
Conlisk (1989) hypothetical 0.5049 1.8580
Loomes and Sugden (1998)random lottery incentive scheme
0.3513 0.1382
Hey and Orme (1994)random lottery incentive scheme
0.7144 0.4789
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StEUT in a nutshell
• An individual maximizes expected utility distorted by random errors:– Error term additive on utility scale – Errors are normally distributed, internality axiom holds– Variance ↑ for lotteries with a wider range of outcomes– No error in choice between “sure things”
• StEUT explains all major empirical facts• StEUT fits data at least as good as CPTDescriptive decision theory can be constructed by
modeling the structure of an error term
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Extensions
• StEUT (and CPT) does not explain satisfactorily all available experimental evidence:– Gambling on unlikely gains
(e.g. Neilson and Stowe, 2002)– Violation of betweenness when modal choice is
inconsistent with betwenness axiom– Predicts too many violations of dominance
(e.g. Loomes and Sugden, 1998)• There is a potential for even better descriptive
decision theory• Stochastic models make clear prediction about
consistency rates