A shocking experiment: New evidence on probability
weighting and common ratio violations
Gregory S. Berns1, C. Monica
Capra21 , Sara Moore1, and Charles Noussair3
1 Department of Psychiatry and Behavioral Sciences, Emory
University School of
Medicine
2 Department of Economics,
Emory University
3 Department of Economics, Emory University and Tilburg
University
Judgment and Decision Making, vol. 2, no. 3, August 2007, pp. 234-242.
Abstract
We study whether probability weighting is observed when individuals are
presented with a series of choices between lotteries consisting of real
non-monetary adverse outcomes, electric shocks. Our estimation of the parameters of the
probability weighting function proposed by Tversky and Kahneman (1992)
are similar to those obtained in previous studies of lottery choice for
negative monetary payoffs and negative hypothetical payoffs. In
addition, common ratio violations in choice behavior are widespread.
Our results provide evidence that probability weighting is a general
phenomenon, independent of the source of disutility.
Keywords: individual choice experiments, choice under risk, non-monetary
losses, probability weighting function.
1 Introduction
Expected utility theory (EUT) is the standard theoretical model of
choice under risk used in economic analysis. EUT posits that the
utility assigned to a lottery or prospect is linear in the probability
of each possible outcome of the lottery. While EUT is an appealing
formulation for economic modeling, a number of experiments have called
it into question as a descriptive model of choice under risk (see
Starmer (2000) for a review of the literature). On the other hand,
specifications allowing probabilities to be weighted by a function
pi(p), where pi(p) has an
inverted S-shape, provide a good empirical fit to the available
experimental data (see for example Prelec, 1998, Wu and Gonzalez, 1996,
Camerer and Ho, 1994, or Gonzalez and Wu, 1999). The inverted S-shape
corresponds to an overweighting of low probabilities and an
underweighting of high probabilities. In recognition of this empirical
support, probability weighting is incorporated as a key assumption of
several theories of choice under risk, including prospect theory
(Kahneman and Tversky, 1979), rank dependent expected utility theory
(Quiggin, 1993), and cumulative prospect theory (Tversky and Kahneman,
1992).
A particularly striking phenomenon that can arise as a consequence of
probability weighting is the common ratio violation.2 Consider
two lotteries and an individual with a utility function
U(x). The first yields a payoff of
xi not-equals 0 with probability
pi and a zero payoff with probability
1- pi. The second lottery yields
xj not-equals 0 with probability
pj and zero otherwise. The linearity
assumption of expected utility theory implies that an individual who
chooses the first lottery over the second one must also choose a
lottery that delivers xi
with probability q*pi over
a lottery that yields xj with
probability q*pj. Clearly,
if
piU(xi)
is greater than or equal to ,
pjU(xj)),
then
q*piU(xi)
is greater than or equal to
q*pjU(xj)).
As originally conjectured by Allais (1953), common ratio violations
which result in
indifference curves in the probability triangle (explained later) that fan out or fan in,
have been found to be widespread in the domain of positive payoffs for
lotteries involving monetary outcomes (see for example Starmer and
Sugden, 1989).
The empirical support underlying probability weighting and common ratio
violations comes primarily from experimental studies in which all
outcomes involve non-negative monetary payments (see for example
Loomes, 1991; Hey and Orme, 1994; or Harless and Camerer,
1994).3 However, many economic
decisions involve the possibility of losses. Examples include a
decision to invest in a stock, to choose among alternative medical
procedures, or to trust another person or institution in a business or
personal transaction. A few studies have explored decisions in the
domain of losses, and they have used one of two techniques to induce
negative payoffs. In some studies, researchers use hypothetical
payoffs; examples include Kahneman and Tversky (1979) and Abdellaoui
(2000). In other studies, participants are given a real cash endowment
at the beginning of the experiment and real losses are deducted from
this initial balance; examples include Holt and Laury (2004) and Mason
et al. (2005).
However, many real life decisions involve negative outcomes that are
not monetary. Consider a cancer patient who is asked to make
a choice between two uncomfortable medical treatments that involve
tradeoffs between probabilities and utilities of different prospective
states of health (e.g., radiation therapy versus extensive surgery).
Another example is the decision of a defendant in a criminal case to
accept or reject a plea bargain for a reduced sentence in prison. The
defendant faces a choice between lotteries over the time of
incarceration.
However, a methodological challenge exists when studying decisions over
non-monetary adverse outcomes: how do we induce real outcomes
of this type4 in the laboratory? Some authors have
used aversive stimuli to investigate other principles of decision
making. For example, Ariely et al. (2003) used annoying sounds as well
as having subjects place their fingers inside a tightening vice to
study the effects of anchoring on preferences. Coursey et al. (1987)
required individuals to drink sucrose octa-acetate, an unpleasant
tasting liquid, to study willingness-to-pay and willingness-to-accept
decisions for a "bad", that is, a
good with negative value. In this paper, we use painful electric
shocks to induce negative payoffs. Pain is a good measure of
disutility as almost everyone would rather avoid it. In addition, as a
means of inducing disutility, the use of electric shocks satisfies
Smith's (1982) precepts pertaining to the
appropriateness of a reward medium for an experiment: monotonicity and
dominance. We can presume that a larger shock (in either magnitude or
duration) is worse than a smaller one.5 For a
fixed duration, the disutility of a shock is monotonic in the current,
and therefore it is monotonic in its voltage. Furthermore, for our
simple decision task, which is described below, and given the voltage
levels applied in our experiment, it is quite reasonable to presume
that the differences between voltage levels from the alternative
choices are large enough to dominate the costs of deciding between
alternatives. Physical pain, unlike cash payments, also has other
advantages in inducing individual incentives, in that the recipient
consumes it instantly and cannot transfer it to other individuals.
In this paper, we consider whether the phenomenon of probability
weighting, and in particular the inverted S-shaped pattern of
probability distortion, is observed when people face lotteries that
involve painful shocks, and whether common ratio violations, which have
been observed for lotteries involving positive monetary payments, also
appear in our setting.
In addition, although probability
weighting can predict a complex pattern of fanning in and fanning out
inside the probability triangle, for the particular gambles that we
consider, we expect to see more risk aversion when gambles get better
(i.e., when there is a lower overall chance of a shock); which means
that indifference curves would tend to fan-out.
We find that both probability weighting and
common ratio violations are prominent features of our data. The median
probability weighting parameter we estimate is very similar to those
observed in decisions over negative hypothetical monetary payoffs
(Tversky and Kahneman, 1992; Abdellaoui, 2000). The results suggest
that a similar process of probability weighting characterizes lottery
choice for both monetary and non-monetary outcomes when payoffs are
negative.
Figure 1: Display on subjects' screens and timing of activity during
the passive phase of the experiment
2 Experimental design and procedures
Figure 2: Display on subjects' screens and timing of activity during
the active phase of the experiment
A total of 37 subjects participated in the study. Seventeen were male
and 20 were female. The average age of participants was 25 years, and
17 of the 37 subjects were students. Each individual participated in
the experiment at a separate time, and thus during each session only
one participant was present. Sessions were conducted at the Emory
University Hospital in Atlanta, Georgia, USA. Each session lasted an
average of two hours and each participant was awarded $40 at the end
of his session. Each session consisted of a series of 180 trials, in
which in each trial subjects had the possibility of receiving an
electric shock. Shocks were delivered using a Grass SD-9
stimulator6 through shielded, gold electrodes placed 2-4 cm apart on
the dorsum of the left foot. Each shock was a monophasic pulse of 10-20
ms duration. During their session, individuals were lying down in an
MRI scanner.7 While in the scanner, the participant observed a computer
screen and used a handheld device to submit their decisions.
At the beginning of the session, the voltage range was titrated for each
participant. The detection threshold was determined by delivering
pulses starting at zero volts and increasing the voltage until the
individual indicated that he could feel them. The voltage was increased
further, while each participant was instructed,
"When you feel that you absolutely cannot
bear any stronger shock, let us know - this will be set as your
maximum; we will not use this value for the experiment, but rather to
establish a scale. You will never receive a shock of maximum
value." The purpose of this procedure was to
control for the heterogeneity of the skin resistance between subjects
and to administer a range of potentially painful and salient stimuli in
an ethical manner. We measured the strength of the shock administered
to an individual by the parameter s, where the associated
voltage for an individual was V = s(Vmin-Vmin) + Vmin,
where Vmin is the detection threshold (not
painful, but just noticeable) and Vmax
is the maximum value for the
individual. For the remainder of the experiment, s took on
values of 0.1, 0.3, 0.6, and 0.9.
After the voltage titration, the first phase of the experiment, which we
call the passive phase, began. The software package, COGENT
2000 (FIL, University College London), was used for stimulus
presentation and response acquisition. The passive phase consisted of
120 trials. The sequence of activity in each trial is illustrated in
Figure 1. The upper part of the figure illustrates the displays that
subjects observed on their computer screen. The lower part of the
figure shows the timing of events within each trial. At the beginning
of each trial, each participant was presented with a pie chart, called
the cue, which conveyed both the magnitude of the potential impending
shock and the probability with which it would be received. The
magnitude of the shock was indicated by the size of an inner circle
relative to an outer gray circle. This outer circle corresponded to the
individual's maximum tolerable voltage,
Vmax. The area of the inner circle
was Vs, where s equaled 0.1,
0.3, 0.6, or 0.9, depending on the trial. The probability was indicated
by the proportion of the inner circle colored in red on
participants' computer screens, which is shown in
Figure 1 as the percentage of the inner circle shaded in the darker
color. The five possible probabilities were 1/6, 1/3, 2/3, 5/6, and 1.
The particular example shown on the left part of Figure 1 depicts a
voltage level with the value of s = 0.6, and a 1/3 probability
of the shock being applied. The four possible voltage levels and five
possible probabilities yielded 20 voltage-probability combinations,
each of which was presented 6 times in the 120 trials that constituted
the passive phase of the experiment.
After the cue was presented for 8 seconds, the shock was then delivered
with the appropriate probability.8 The word,
"outcome," as shown in the second
image located in the upper middle of Figure 1, was presented
concurrently with the delivery of the shock on those trials in which a
shock occurred. It also appeared at the same point in time on those
trials in which a shock was not delivered, providing an indicator to
the participant that the trial was over. It remained on display for
one second9, after
which a display consisting of a visual analog scale appeared on the
participant's screen. The display is shown in the
upper-right part of Figure 1. The subject was then required to rate the
experience of the trial in a range between "very
unpleasant" and "very
pleasant." To indicate his rating, he marked a
location on the scale, using a cursor operated by hand. The process
then continued to the next trial.
The next phase of the session, which we call the Active Phase,
consisted of 60 trials. In each trial, individuals were required to
choose between two of the probability/shock combinations that were
presented in the passive phase. The available choices differed from one
trial to the next. Figure 2 shows an example of the displays that
appeared on participants' screens during a typical
trial as well as the timing of a trial. At the beginning of each round,
a display similar to the one shown in the upper left of the figure
appeared. The figure shows two lotteries presented side by side, and
subjects were required to choose one of the two using the keypad
provided to them. The two options available in a given trial always had
the property that one alternative specified both a higher voltage shock
as well as lower probability than the other alternative. For instance,
the example shown in Figure 2 represents a choice between 1/6 chance of
a shock with s = 0.9 vs. a 1/3 chance of a shock with
s = 0.6. The larger inner circle represents the larger shock
(i.e., s = 0.9); the probability is represented by the proportion of the
area of the inner circle that is colored in red.
After the participant made his choice, there was an 8 second interval,
in which the display was augmented by an arrow indicating the lottery
chosen. After the 8 seconds had elapsed, the outcome was realized and
the word "outcome" was included on
the display for 1 second, as shown in the upper right part of Figure 2.
Then, the current trial ended and the next of the 60 trials that made
up the active phase began. The experimental session ended after the
active phase was completed.
3 Results
The results show that the data are consistent with probability
weighting, and that the sample parameter value of the particular
probability weighting function we estimate is very close to the values
reported in previous studies. We first tested the hypothesis that
expected utility theory can explain our data. To do so, we estimated
the value of a prospect or lottery,
V(L)=sumipi(pi)U(xi),
where pi is the probability of
outcome xi. using the specification
for probability weighting of Tversky and Kahneman (1992).10 Under
this specification, the value of a prospect that yields non-positive
payoffs under all possible realizations is given by:
(1)
The expected utility hypothesis is consistent only with
gamma = 1. In contrast, previous estimates of the median
value of gamma for samples of experimental participants incurring
hypothetical monetary losses are .69 obtained by Tversky and Kahneman
(1992) and .70 observed by Abdellaoui (2000). All values gamma
in (0,1) imply an inverted S-shape probability weighting function,
in which relatively low probabilities are over weighted, and relatively
high probabilities are underweighted (probabilities of 0 and 1 receive
accurate weight for all gamma in (0,1].) The parameter
lambda is a scaling factor. The parameter alpha
measures the convexity (or concavity) of the utility function. The
variable xi is the voltage of the
shocks administered. There is no guide from prior research about the
appropriate level of lambda or alpha because there
is no reason to believe that the scale or curvature of the utility
function would be the same for electric shocks as for the real and
hypothetical monetary payments previous authors have investigated,
though there is evidence (Stevens, 1961) that the psychological
reaction to the intensity of electric shocks applied to the fingers
follows a power function with an exponent of approximately 3.5.
Because we did not elicit certainty equivalents in our experiments, we
used a ranking procedure to derive a measure of the relative value of
each of the 20 lotteries to each subject. There were 190 possible
lottery pairs, but we only observed actual choices for 60 pairs for
each individual (i.e., all those pairs for which there was a tradeoff
between higher probability and higher pain determined by the value of
s). For these sixty pairs, individuals'
choices yielded a revealed preference between the two lotteries. To
construct "revealed" preferences
for the other 130 pairs (those that were not presented to the subject),
we applied a strict dominance criterion. We assumed that individual
i would have always chosen a lottery with a lower probability
and lower pain over a higher pain, higher probability option.11 We determined the rank or
"relative preference" of each of
the 20 lotteries based on the percentage of times that it was
"revealed preferred" to other
lotteries. This procedure yields a complete and transitive preference
ordering of the 20 lotteries. We then used the ranked lotteries,
determined separately for each individual participant, as data to fit
the specification in (1). The parameters, alpha,
lambda, and gamma, were estimated jointly
using nonlinear least squares regression with normally distributed
errors.12
The mean estimated value of the probability weighting parameter
gamma for the 37 subjects was 0.659, with a standard deviation
among the 37 individual estimates of 0.218. The median estimated value
was 0.685.13 Females tended to
have higher estimates (median=0.769 vs. 0.570 for males), but the
difference between the two genders was not significant. We then
classified our subjects into groups based on whether (a) the EUT value
of 1 or (b) the value of gamma estimated in
Tversky and Kahneman (1992) of 0.69, fell within the 95% confidence
interval of the estimated individual gamma (individual estimated
values and standard errors can be found in the Appendix). Forty-six
percent (17 out of 37) of our subjects' estimated probability
weighting parameters were consistent with Tversky-Kahneman but not
with EUT, whereas 14% (5 out of 37) were consistent with EUT but not
with Tversky-Kahneman. The rest of the subjects were consistent with
both values (24%) or with neither (16%).14 Figure 3 shows the proportion of estimated values
that fell into the four categories listed above.
Figure 3: Percentage of subjects for whom the
gamma values of 1 or 0.69 fell within the 95% confidence
interval of their individual gamma estimate
We now consider the incidence of common ratio violations in the data.
Let sh be the more painful and
sl be the less painful of two
alternative potential shocks presented as a pair-wise choice, and let
ph and
pl be the higher and lower
probabilities among the two alternatives, respectively. In our
experiment, common ratio violations were observed when the lottery
(sh ,
pl = 1/6) was chosen over
(sl ,
ph = 2/6), but
(sl ,
ph = 4/6) was chosen over
(sh,
pl = 2/6), or alternatively, vice
versa. Within the context of the Marschak-Machina triangle and under
the assumption that the indifference curves are linear15,
the first sequence of decisions corresponds to
"fanning in" of indifference
curves; the second sequence, on the other hand, is consistent with
"fanning out" of indifference
curves. Figure 4 depicts the Marschak-Machina (M-M) triangle with
indifference lines. An individual who is indifferent between x
and y, and indifferent between z and w will
have parallel indifference lines passing through each of the two pairs
of points. However, if the individual prefers x to
y, then her indifference line will have a higher slope as
shown by the darker dashed line passing through x in the
figure. Under EUT, if x is preferred to y, then
z is preferred to w (shown by the lighter dashed line
passing through z). A choice of w would constitute a
common ratio violation, and would imply that the indifference line
passing through w has a smaller slope than the one passing
through x, as depicted by the darker solid line in the figure.
Note that the choice of x and w means that the
indifference curves are "fanning
out". Similarly, Figure 5 depicts
"fanning in." In the active phase
of the experiment, there were a total of six instances in which common
ratio violations could occur. The observed type and number of
violations per subject can be found in the Appendix.
The results show that the average number of common ratio violations by
individual was 1.95 with a standard deviation of 1.39, which is clearly
different from the prediction of EUT.16 There is a tendency to commit more fanning
out violations than fanning in violations. This is not surprising, as
indifference curves that fan out are consistent with overweighting of
small probabilities. About 68% of the subjects (25 out of 37)
committed one or more fanning out violations, whereas about 46% (17
out of 37) committed one or more fanning in violations. The Fisher
exact test shows that the null hypothesis that the proportion of
subjects who commit at least one fanning in and fanning out violations
are the same is rejected in favor of the alternative of more people
committing fanning out violations (p = 0.049; one-tailed). Five
subjects committed fanning in violations only; in contrast, seventeen
subjects committed uniquely fanning out violations. Finally, there
were small differences between the average number of violations
committed by females (2.10; std = 1.37) as compared to males (1.76, std
= 1.43).
We also considered whether there was consistency between our above
classification of subjects (Figure 3) and the observed number of common
ratio violations. None of the five subjects whose estimated
gamma value was consistent with only EUT committed more
than two common ratio violations. In contrast, the number of
violations by subjects with estimated gamma
significantly less than 1 ranged from zero to five. Overall, however,
there are no statistically significant differences in the median number
of violations between these two groups (p > 0.188,
one-tailed).
Although the overall fanning effect was as predicted, there were large
individual differences. Some participants showed no fanning; whereas
others showed fanning in the opposite directions. Were these
consistent differences, or just random variation? We found that,
within an individual, the direction of violations was consistent, in a
specific sense, with their overall choice behavior in the 60 trials of
the active phase. To study this consistency, we computed a "mean
fanning effect" for each subject from the six instances where
individuals could switch preferences, and we asked whether this effect
could be predicted from an index of fanning computed from the
remaining 54 cases. Fanning out is indicated by a lower tendency to
choose the low-probability-high-shock option when overall shock
probabilities are smaller (i.e., closer to the top of the M-M triangle
in Figure 4). Using the 54 decisions between which common ratio
violations cannot be detected from decisions, we conducted a
regression with the chosen lottery as the dependent variable. The
independent variables were the difference in the logs of the shock
intensity of the two options, the difference in the logs of the
probabilities of a shock under the two options, and the sum of the two
probabilities of receiving a shock under the two options. The
regression coefficient for this last variable, which is a measure of
the distance from the top of the M-M triangle, was our index of the
fanning effect. The coefficient takes on a smaller value, the more
the tendency toward fanning out. We found a positive correlation
between the individual estimated coefficients of the fanning index,
and whether they committed more fanning out than fanning in violations
(r=.31, t(35)=1.95, p=.0293, one-tailed).17 Thus, individuals differ in
a consistent manner in the direction and magnitude of this effect.
4 Discussion
In this paper we provide evidence that non-linear probability weighting,
which has been observed when prospective losses are framed in terms of
money, also occur in lottery choices when real adverse outcomes are
induced with a non-monetary medium. As in previous studies, our
estimated values of the probability weighting parameters provide little
support for EUT. We find that about 14% of our
subjects' estimated probability weighting parameters is
consistent with EUT. In contrast, about 46% of the subjects
overweight small probabilities and underweight large probabilities,
exhibiting a typical inverted S-shape probability weighting function.
Furthermore, the estimated sample median probability weighting
parameter we obtain is closely in line with values reported in previous
studies. This suggests that probability weighting acts in a similar
manner for lottery choice when outcomes are measured in terms of
physical pain as well as for hypothetical monetary transfers. This
result is consistent with the conjecture that probability weighting is
a general phenomenon, and independent of the source of disutility.
We also find that common ratio violations, which are inconsistent with
EUT, are widespread. A greater proportion of subjects commit
violations consistent with indifference curves that fan out than with
fanning in. However, there are large individual differences in the
direction and incidence of the violations. Despite these differences,
we were able to determine that the direction of
subjects' violations is largely consistent with their
overall choice behavior, and not random.
In our view, the results we obtain are encouraging evidence that
traditional methodologies used in economics and psychology to study
decisions in the domain of negative payoffs lead to the same principles
of decision making as those applied in decisions over non-monetary
media with real losses. Indeed, we reach almost identical conclusions
to previous studies. Our results also suggest a conjecture that in the
domain of positive payoffs, the probability weighting parameters
estimated for monetary payments would carry over to non-monetary media.
In the future, we believe that the methodology of applying physical
pain to study decision making may be used to explore the robustness of
other behavioral anomalies observed in the laboratory that occur when
payoffs are negative.
References
Abdellaoui, M. (2000). Parameter-free elicitation of
the probability weighting function.
Management Science, 46, 1497-1512.
Allais, M. (1953). Le comportement de l'homme rationnel devant
le risque: Critique des postulats et axioms de l'école
américaine. Econometrica, 21, 503-546.
Ariely, D., Loewenstein, G., & Prelec, D. (2003).
Coherent arbitrariness: Stable demand curves without stable
preferences, Quarterly Journal of
Economics, 118, 73-105.
Baltussen, G., Post, T., Van den Assem, M. J., & Thaler, R. (2007).
Reference-dependent risk attitudes: Evidence from
versions of Deal or No Deal with different stakes.
Tinbergen Institute/Erasmus University working paper.
Berns, G., Capra, M., Chappelow, J., Moore, S., & Noussair, C. (2006).
Neurobiological probability weighting functions over
losses. Emory University working paper.
Bleichrodt, H. & Pinto, J. (2000). A Parameter-free elicitation of
the probability weighting function in medical decision analysis.
Management Science, 46, 1485-1496.
Camerer, C. & Ho, T.-H. (1994). Violations of betweenness axiom and
nonlinearity in probability. Journal of Risk and Uncertainty,
8, 167-196.
Coursey, D. L., Hovis, J. L., & Schulze, W. D. (1987). The
disparity between willingness to accept and willingness to pay
measures of value. Quarterly Journal of Economics, 102,
678-690.
De Roos, N., & Sarafidis, Y. (2006). Decision making
under risk in Deal or No Deal. University of Sydney
and CRA International working paper.
Gonzalez, R. and G. Wu (1999). On the Shape of the
Probability Weighting Function, Cognitive
Psychology, 38, 129-166.
Harless D. & Camerer, C. (1994). The Predictive Utility of Generalized
Expected Utility Theories. Econometrica, 62, 1251-1289.
Hey, J. & Orme, C. (1994). Investigating Generalizations of Expected Utility
Theory Using Experimental Data. Econometrica, 62, 1291-1326.
Holt, C. & Laury, S. (2005). Further reflections on prospect theory.,
Georgia State University working paper.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An
analysis of decision under risk. Econometrica, 47,
263-291.
Loomes, G. (1991). Evidence of a new violation of the independence
axiom. Journal of Risk and Uncertainty, 4, 91-108.
Malinvaud, E. (1952). Note on von-Neumann-Morgenstern's strong independence
axiom. Econometrica, 20, 679.
Machina, M. (1982). Expected Utility Analysis without the Independence
Axiom. Econometrica, 50, 277-323.
Mason, C., Shogren, J., Settle, C., & List, J. (2005). Investigating risky
choices over losses using experimental data. Journal of Risk and
Uncertainty, 31, 187-215.
Prelec, D. (1998). The probability weighting function. Econometrica, 66,
497-527.
Quiggin, J. (1993). Generalized expected utility theory: The rank dependent
model. Heidelberg, Germany: Springer Verlag Publishers.
Smith, V. (1982). Microeconomic systems as an experimental science.
American Economic Review, 72, 923-955.
Starmer, C., (2000). Developments in non-expected utility theory: The Hunt
for a descriptive theory of choice under risk. Journal of Economic
Literature, 38, 332-382.
Starmer C. & Sugden, R. (1989). Violations of the independence axiom in
common ratio problems: An experimental test of some competing hypotheses.
Annals of Operations Research, 19, 79-102.
Tversky, A. & Fox, C. (1995). Weighing risk and uncertainty.
Psychological Review, 102, 269-323.
Tversky, A., & Kahneman, D. (1992). Advances in prospect
theory: cumulative representations of uncertainty.
Journal of Risk and Uncertainty, 5, 297-323.
Wakker, P. & Deneffe, D. (1996). Eliciting von Neumann-Morgenstern
utilities when probabilities are distorted or unknown. Management
Science, 42, 1676-1690.
Wu G. & Gonzalez, R. (1996). Curvature of the probability weighting
function, Management Science. 42, 1676-1690.
Appendix.
Estimates of individual gamma parameters, and type and
number of fanning violations
Subject | Sex | Est. gamma | Std. err. | Fan out | Fan in |
1 | F | 1.0127890 | 0.0796662 | 2 | 0 |
2 | F | 0.3915420 | 0.1450381 | 0 | 0 |
3 | M | 0.5452798 | 0.1469481 | 1 | 0 |
4 | F | 0.8426407 | 0.0907564 | 3 | 0 |
5 | F | 0.7069833 | 0.1078082 | 2 | 0 |
6 | M | 0.7641525 | 0.1032662 | 1 | 2 |
7 | M | 0.5423165 | 0.1307505 | 1 | 0 |
8 | M | 0.9610685 | 0.0648182 | 1 | 0 |
9 | F | 0.4574296 | 0.1382386 | 1 | 0 |
10 | M | 0.4818398 | 0.1515036 | 0 | 1 |
11 | M | 0.8285788 | 0.1330844 | 1 | 1 |
12 | F | 0.2973379 | 0.1289225 | 0 | 0 |
13 | M | 0.3323089 | 0.1426116 | 0 | 0 |
14 | M | 0.6006711 | 0.1175138 | 0 | 0 |
15 | F | 0.2707158 | 0.1300445 | 1 | 0 |
16 | F | 0.4599991 | 0.0932652 | 1 | 2 |
17 | M | 0.6856989 | 0.1550794 | 0 | 5 |
18 | F | 0.7670457 | 0.1119126 | 3 | 1 |
19 | F | 0.7078283 | 0.1031506 | 4 | 0 |
20 | M | 0.4624952 | 0.1245585 | 0 | 0 |
21 | F | 1.0176710 | 0.0874302 | 1 | 0 |
22 | M | 0.8409525 | 0.0796012 | 1 | 1 |
23 | M | 0.5703915 | 0.1138435 | 0 | 1 |
24 | F | 0.9963527 | 0.1260562 | 2 | 0 |
25 | M | 0.6664429 | 0.1116457 | 1 | 2 |
26 | F | 0.4039314 | 0.1187758 | 0 | 0 |
27 | F | 1.0344640 | 0.0756161 | 0 | 1 |
28 | F | 0.7988346 | 0.1216859 | 1 | 1 |
29 | M | 0.5665003 | 0.1288915 | 2 | 0 |
30 | F | 0.7718875 | 0.1614382 | 0 | 4 |
31 | M | 0.5567840 | 0.1339346 | 2 | 2 |
32 | M | 0.3608489 | 0.1084658 | 0 | 1 |
33 | F | 0.7960892 | 0.1137703 | 2 | 2 |
34 | F | 0.9279254 | 0.1309254 | 3 | 0 |
35 | M | 0.7009950 | 0.1272274 | 2 | 1 |
36 | F | 0.7771528 | 0.0873558 | 1 | 2 |
37 | F | 0.4700133 | 0.1200721 | 2 | 0 |
|
Footnotes:
1Corresponding author, Department of Economics,
Emory University, 1602 Fishburne Drive, Atlanta, GA 30322.
Email: mcapra@emory.edu.
Supported by grants from the National Institute on Drug Abuse
(DA016434 and DA20116). We would like to thank an anonymous
referee and the editor of this journal, Jonathan Baron, for
working out the intricacies of individual differences and other
invaluable suggestions. All errors are
ours.
2Common
ratio violations also result when the independence axiom on preferences
is relaxed or violated. See Malinvaud (1952) for a discussion of the
relationship between the independence axiom and expected utility
theory. See Machina (1982) for an analysis of the implications of
relaxing the assumption that the independence axiom holds.
3Some recent studies using real decisions of
participants in the TV game show "Deal or No
Deal" also find support for generalized expected
utility models when decisions are over possible large sums of money.
De Roos and Sarafidis (2006) find that rank dependent utility models
are better at describing decisions. Using cross-country data from the
same game, Baltusen et al. (2007) find that theories that include
reference dependence, an assumption of Prospect Theory, but not EUT,
are better at describing observed decisions.
4 Bleichrodt and Pinto (2000) also study choices
under risk over non-monetary losses, but their outcomes are
hypothetical medical maladies.
5 Indeed, self-reports
of participants, who evaluated the experience after the shocks, show
that shocks were perceived negatively and shocks of different voltages
were preceived differently from each other. As described in the
procedures, participants were required to rate the experience of each
trial of the experiment on a scale, which ranged from
"very unpleasant" to "very pleasant". We find that the
stronger the shock was, the more unpleasant the experience was.
6 The Grass stimulator (West Warwick, RI) was
modified by attaching a servo-controlled motor to the voltage
potentiometer. The motor allowed for computer control of the voltage
level without comprising the safety of the electrical isolation in the
stimulator. The motor was controlled by a laptop through a serial
interface.
7 Skin conductance responses to the shocks were
also registered. The analysis of the fMRI and the skin conductance
response data are reported in a companion paper (see Berns et al,
2006).
8 The outcomes were
predetermined (although unknown beforehand to participants) to ensure
that there would be at least one trial under each of the 20 conditions
(4 different voltages times 5 different probabilities) in each of the
three 60-trial fMRI scan runs. Although the outcomes were
predetermined, the total number of shocks received in each of the
conditions reflected the actual probabilities.
9Following the shock, the cue remained visible for
another 1 second to prevent conditioning to the cue offset.
10 Our
parametric estimations of the probability weighting function using
alternative functional forms such as those proposed in Tversky and Fox
(1995), and Wu and Gonzalez (1996) result in similar conclusion.
11
Implicit is the assumption that in decisions in which there are no
tradeoffs between probability and pain (where one alternative has both
higher voltage and probability than the other), the subject would not
make common ratio violations.
12Other authors such as Tversky and Kahneman, (1992),
Camerer and Ho (1994) and Tversky and Fox (1995) have also made
parametric estimates of the probability weighting function and the
utility function. Some authors such as Abdellaoui (2000) and Bleichrodt
and Pinto (2000), have used parameter free estimations employing a
trade-off technique of Wakker and Deneffe (1996). Overall, however,
there seems to be little difference in the median values of the
estimated gamma parameters using either method.
13The estimated parameters were robust to a wide
range of economically relevant initial values.
14 Using the
trade-off technique, Bleichrodt and Pinto (2000) found that over
80% of their subjects exhibited a probability weighting function
with lower and upper subadditivity (i.e, the inverted S-shape).
Other authors do not report individual data for their entire sample.
In line with Bleichrodt and Pinto's results, we find that about 84%
of or subjects exhibit probability weighting consistent with
subadditivity.
15We are
aware that with only two points in the border of the MM triangle, we
cannot make inferences about the shapes of the indifference curves.
We use linear indifference curves in the
figures for the specific purpose of illustrating the fanning effects in
an easy manner.
16 The Friedman test for
no differences between the observe number of violations versus the
number that would happen randomly can be rejected (Chi-square 10.80; df = 1; p < 0.01).
17With respect
to the six critical cases where preference reversals can be
observed, a reliability test also shows that decisions are
consistent (Cronbach's alpha = 0.45).
File translated from
TEX
by
TTH,
version 3.74.
On 25 Aug 2007, 11:52.