Ritov, I., & Baron, J. (1990). Reluctance to vaccinate:
omission bias and ambiguity. Journal of Behavioral Decision
Making, 3, 263-277.
Reluctance to vaccinate: omission bias and ambiguity
Ilana Ritov and Jonathan Baron1
1 Abstract
Subjects are reluctant to vaccinate a (hypothetical) child when
the vaccination itself can cause death, even when this is much
less likely than death from the disease prevented. This effect
is even greater when there is a `risk group' for death (with its
overall probability held constant), even though the test for
membership in the risk group is unavailable. This effect cannot
be explained in terms of a tendency to assume that the child is
in the risk group. A risk group for death from the disease has
no effect on reluctance to vaccinate. The reluctance is an
example of omission bias (Spranca, Minsk, & Baron, 1990), an
overgeneralization of a distinction between commissions and
omissions to a case in which it is irrelevant. Likewise, it
would ordinarily be prudent to find out whether a child is in a
risk group before acting, but in this case it is impossible, so
knowledge of the existence of the risk group is irrelevant. The
risk-group effect is consistent with Frisch and Baron's (1988)
interpretation of ambiguity.
The present study concerns the role of two biases in hypothetical
decisions about vaccinations. One bias is the tendency to favor
omissions over commissions, especially when either one might
cause harm. We show that some people think that it is worse to
vaccinate a child when the vaccination can cause harm than not to
vaccinate, even though vaccination reduces the risk of harm
overall. The other bias is the tendency to withhold action when
missing information about probabilities is salient - such as
whether the child is in a risk group susceptible to harm from the
vaccine - even though the missing information cannot be obtained.
We show that this bias is found even when the overall probability
of each outcome is clearly constant across the conditions
compared. We take both of these effects to be
overgeneralizations of principles or heuristics that are
generally useful to situations in which they are not useful.
Consider first what we shall call omission bias, the
tendency to favor omissions (such as letting someone die) over
otherwise equivalent commissions (such as killing someone
actively). In most cases, we have good reasons for the
distinction between omissions and commissions: omissions may
result from ignorance, and commissions usually do not;
commissions usually involve more malicious intentions than the
corresponding omissions; and commissions usually involve more
effort, itself a sign of stronger intentions. In addition, when
people know that harmful omissions are socially acceptable, they
look out for themselves; this self-help principle is, arguably,
sometimes the most efficient way to prevent harm.
In some cases, however, these relevant differences between
omissions and commissions seem to be absent. For example,
choices about euthanasia usually involve similar intentions
whether the euthanasia is active (e.g., from a lethal drug) or
passive (e.g., orders not to resuscitate). In such cases,
when knowledge and intentions are held constant, omissions and
commissions are morally equivalent (see Spranca, Minsk, & Baron,
1990, for discussion). Yet many people continue to treat them
differently - not everyone, to be sure, but enough people to
influence policy decisions. We suggest that these people are
often overgeneralizing the distinction to cases in which it does
not apply.
The intuition that commissions are worse, valid as it may be in
most cases, is no longer valid when knowledge and intention are
known to be the same for both omission and commission or when a
decision maker must choose between an omission and a commission,
knowing the consequences of both (as in the studies reported
here). If you have a choice of killing 5 or letting 10 people
die, assuming (for present purposes) that all are drawn at random
from the same population, you should kill the five. Each member
of the population has twice the chance of death from your
omission than from your commission, and each would therefore
prefer you to act. If you choose not to act, you are hurting all
by going against their preferences.
Any principle that tries to justify the omission here would have
to have a strong justification, for that principle will have a
price in lives. When attempts are made to formulate a principle
that can justify a bias toward omissions, the very distinction
between omission and commission becomes unclear, and the
distinctions that can be maintained have no clear moral
significance (Bennett, 1966, 1981 - for example, one distinction
is that there are more ways of not doing something than doing it,
yet the number of ways of doing something has no normative
significance). Arguments in favor of the distinction (e.g.,
Kagan, 1988; Kamm, 1986; Steinbock, 1980) fall back on intuitive
judgments about cases. The correctness of intuition, however, is
exactly what is at issue: to appeal to intuition is to beg the
question.
One might argue that intuition is relevant because it affects the
regret that people feel about different outcomes. Active killing
of one person might cause more regret than passive killing of
two. Our answer to this is that the regret is felt by the
decision maker, not those who die, so the use of this argument as
a justification is a kind of selfishness. In addition, the
difference in regret might not be inevitable; it might disappear
with a change in the person's view of omissions and commissions,
so a general change in this view might still be justified.
Spranca et al. (1990) found that many subjects considered
commissions that caused harm to be morally worse than omissions
that caused harm, even with intention held constant. For
example, active deception was considered morally worse than
withholding the truth, even when the actor's intention to deceive
was judged to be the same in the two cases. Spranca et al. also
asked subjects to evaluate two options from a decision maker's
point of view: a treatment that would cure a disease but cause
death with a .15 probability, or no treatment, with the disease
itself causing death with a .20 probability. In 13% of the
cases, subjects chose no treatment because (they said) they did
not want to be responsible for causing deaths through their
decision. (When the probabilities were reversed, subjects
preferred the treatment in only 2% of the cases, and, in general,
when subjects rated the desirability of both options, the
relative desirability of the lower death rate was higher when
that was associated with inaction than when it was associated
with action.) This result was equally strong whether the
decision was made from the point of view of a physician, a
patient (deciding for himself), or a public-health official
deciding for many patients. In the present study, we extend this
result, using different examples and a different method.
This omission bias is related to other phenomena. Kahneman and
Miller (1986) point out that commissions lead to greater regret
than omissions when a fortuitous bad outcome occurs.
Demonstrations of the status-quo bias and related biases
(Knetsch, Thaler, & Kahneman, 1988; Samuelson & Zeckhauser, 1988;
Viscusi, Huber, & Magat, 1987) usually confound the status quo
with an omission. For example, when the willingness to pay to
remove a risk is less than the willingness to accept payment to
bear the risk, changing the status quo requires an action
(accepting or paying) in both cases. Ritov and Baron (1990) have
unconfounded the status-quo effect from omission bias in both of
these contexts by asking subjects whether they would act in order
to prevent a change from the status quo or whether they would
feel worse when a bad outcome resulted from failure to take such
action (vs. acting to maintain the status quo). In both of these
situations, we found that the omission-commission distinction is
the critical one, not the preservation of the status quo.
We do not mean to suggest that people are always biased toward
omissions. Under some conditions, for example, when the decision
maker is in a position of responsibility, people show the
opposite bias (Ritov, Hodes, and Baron, 1989). Most subjects in
the studies of Spranca et al., and in the studies reported here,
show no bias. A substantial minority, however, can influence
public policy (e.g., on active vs. passive euthanasia) or can
affect overall rates of cooperation, as in a vaccination program.
The bias toward omissions does not seem to have a single
explanation (Spranca et al., 1990). Many subjects justify the
distinction by arguing that omissions are not causes (despite the
fact that they affect the probability of outcomes relative to the
alternative option). Some of these subjects do not hold
themselves responsible for outcomes that would have occurred if
they were absent or ignorant, despite the fact that they were not
absent and not ignorant. The use of omissions as a reference
point also seems to play a small role, so that harms caused by
omissions are seen as foregone gains, which are less aversive
than pure losses caused by commission (as is consistent with norm
theory, Kahneman & Miller, 1986, and the loss aversion assumption
of prospect theory, Kahneman & Tversky, 1984).
Consider next the effect of salient missing information. Frisch
and Baron (1988) have argued that the effects of `ambiguity' on
decision making can be described in terms of the salience of
missing information. For example, in a situation first described
by Ellsberg (1961), people told that they will win a prize if a
red ball is drawn will prefer to draw from an urn with 50 red
balls and 50 blue balls, rather than an urn with an unknown
proportion of red and blue balls. Here, the proportion of red
balls is a salient piece of missing information in the second
case. Subjects do not think about other missing information that
would be just as useful, for example, information about the
proportion of red balls in the region of the first urn from which
the ball will be drawn. The perception of missing information
can incline people toward inaction because they feel a desire to
seek the information before doing anything else. When the
information is not available, however, this desire must be left
unsatisfied.
Frisch and Baron argue that the tendency to withhold action when
information is missing can account for other effects of
ambiguity, such as the effects of conflict among experts who
estimate probabilities (Kunreuther and Hogarth, 1989). Brun and
Teigen (1990) recently provided some evidence consistent with
this view: subjects prefer guessing the outcome of an uncertain
event before it has occurred to guessing after it occurred but
before they know it.
In the present experiments, we test the effects of missing
information directly by holding constant the probability of the
outcome, a vaccine-related injury. We simply call attention to
one factor that can influence the probability of such an injury,
membership in a `risk group' for the injury. `Ambiguity' is
therefore manipulated even though the probability of the outcome
in question remains exactly known.
Previous studies of the effect of ambiguity on decision making
have often failed to inform subjects explicitly that the
probability of the outcome was unaffected by the ambiguity
manipulation. Frisch (1988) has found that subjects in
experiments such as Ellsberg's often do not know that the
expected probability is constant across the conditions being
compared. In the present experiments, we test the effects of
missing information directly by holding constant the probability
of the outcome, a vaccine-related injury. We simply call
attention to one factor that can influence the probability of
such an injury, membership in a `risk group' for the injury.
`Ambiguity' is therefore manipulated even though the probability
of the outcome in question remains exactly known. We therefore
test the role of ambiguity itself, unconfounded by subjects'
beliefs about the effects of ambiguity on probability.
Both omission-commission and missing information are involved in
public policy. A classic case in which the bias toward omissions
affected policy was the argument that not seeding a hurricane
could be justified, even though seeding would lead to less
damage, because the damage would be felt by different people, to
whom the decision makers would be `responsible' (Howard,
Matheson, & North, 1972). Our legal system, as well, honors the
distinction even when it seems irrelevant: we hold manufacturers
strictly liable for damages that result from their decisions to
make certain products, but we do not hold them liable at all for
decisions not to produce the products (e.g., new vaccines).
Similarly, we seem to put more effort into reducing risk when the
risk is not well known but small (e.g., products of genetic
engineering) than when the risk is well known and large (e.g.,
radon).
In all the experiments reported here, the basic task was the
following: Subjects were presented with a hypothetical situation
in which they had to make one of two decisions, either whether to
vaccinate their child against an epidemic disease, or whether to
support a law, requiring that all children be vaccinated.
Naturally, the vaccine itself carries some risk.
The vaccination problems we present are modeled on the real case
of DPT vaccine (diphtheria, pertussis, tetanus), which causes a
serious, permanent neurological injury in 1 dose out of 310,000,
far less than the damage formerly caused by pertussis (whooping
cough) alone in infants. In 1987, the only manufacturer of DPT
vaccine in the United States (Lederle) set aside 70% of the price
of the vaccine as a reserve against tort claims (Inglehart,
1987). Likewise, the Sabin vaccine occasionally causes polio,
although it is on the whole safer than the Salk vaccine, which
sometimes fails to prevent polio. The producer of the Sabin
vaccine has been held liable for such cases (Inglehart, 1987),
although no suits have been brought against the producer of the
Salk vaccine. More generally, manufacturers are liable for
harmful effects of their actions but not for harmful effects of
their inactions (Huber, 1988). Ambiguity about possible side
effects further reduces the willingness of companies and their
insurers to move forward with new products (Kunreuther and
Hogarth, 1989).
2 Experiment 1
2.1 Method
Subjects were 53 undergraduates recruited with a sign places on a
prominent campus walkway and paid $5 per hour.
Subjects were presented with a situation in which a disease kills
10 out of 10,000 children. A vaccine, which costs $2 per child,
can prevent the disease in everyone, but the vaccine itself has
side effects that kill some children. The children that die from
the side effects are not necessarily the same ones who would die
from the disease. Subjects were given a table of different
possible values of the risk of death from side effects, ranging
from 0 to 9 out of 10,000, the `net decrease in probability of
death' provided by each level of risk, and the `cost per life
saved.' The `net decrease' and cost per life, respectively,
ranged from 10/10,000 and $2,000, when the death rate from side
effects was 0, to 1/10,000 and $20,000, when the death rate from
side effects was 9. Subjects were asked the maximum level of
risk that should be tolerated by the government in order to
institute a compulsory vaccination program.
In a `same children' condition, subjects were told,
Suppose it were discovered that the children who are susceptible
to death from flu are the same ones who are susceptible to death
from the side effects of the vaccine. Thus, ... the `net
decrease' would represent actual lives saved, children who would
have died from flu if they had not been given the vaccine. There
would be no children who would die from the vaccine who would not
have died anyway.
Two other items were identical except that the cost was $10
instead of $2 per child, with corresponding increases in the cost
per life. Additional items addressed other issues that are not
relevant to this paper.
2.2 Results
In the basic condition, in which the children who died from the
vaccine were not necessarily the same as those who would die from
the disease, 57% of the answers ranged between 1 and 8 per
10,000; 23% thought that no risk should be tolerated; and 9% gave
answers of 9 (or 10, the maximum possible risk). (The remaining
6 gave uninterpretable answers or failed to answer this item.)
In the `same children' condition, 47% of the subjects (in
contrast to 9% in the basic condition) said that the vaccine
should be given at the maximum risk (9 per 10,000). In all, 68%
tolerated higher risk in this same-children conditions than in
the condition with the same cost, and only 4% tolerated lower
risk in the same-children condition than the control (p < .001).
Several subjects pointed out that the difference between these
conditions was in whether the vaccine killed children who would
not die from the disease in any event. Subjects who tolerated
very little or no risk in the basic condition, or who said (in
answer to another question) that they thought that the government
had no right to compel anyone to have the vaccine, often
commented that giving the vaccine on a large scale would involve
causing the deaths of some children, which was wrong even if it
meant that a greater number of children would be saved. For
example, `You can't force parents to give their kid a drug or
vaccine that could cause the kid to die!' Subjects apparently
are not inclined to consider deaths cause be failure to vaccinate
(that would not occur in any case) as results of a decision,
although they do consider deaths caused by vaccination as results
of the decision, if they would not occur anyway.
Price had no effect on the results despite a fivefold change in
cost per life. Four out of 53 subjects were less willing to
vaccinate when the price was high, three were more willing, and
the rest were equally willing. The bias toward omission
therefore cannot be explained in terms of vaccination being more
costly.
3 Experiment 2
The present experiment adds ambiguous situations in which the
final outcome of the vaccination is dependent upon an unknown
intermediate state. We expect that the subjective feeling of
missing information will be more salient in such a situation,
leading, in turn, to a stronger omission bias.
As before, subjects were presented with a hypothetical situation,
in which they had to decide whether to vaccinate their child
against an epidemic flu. Several conditions were described,
differing in the presence (or absence) of `risk groups' in the
population, with regard either to the flu or to the vaccine. In
all cases, however, the information whether the child belongs to
any of the risk groups was not available to the decision maker.
We predicted that the presence of risk groups would make subjects
less inclined to vaccinate, in spite of our emphasizing the fact
that the overall probability of death is identical in all
conditions.
We also compared the effect of ambiguity (missing information)
with regard to risk of death from the vaccine to ambiguity with
regard to death from the flu. To that end, we included two
conditions: one condition with information only about a risk
group for the vaccine and another condition with information only
about a risk group for the flu.
Finally, we compared decisions for a hypothetical child of one's
own with decision concerning a hypothetical law requiring
vaccination for all children.
3.1 Method
Twenty-eight students were solicited as in Experiment 1.
Subjects went through all cases twice: once to make a personal
decision whether they would vaccinate their child, and once to
indicate their support of a law requiring vaccination. Half the
subjects did the policy decision before the personal one.
The instructions to the questionnaire read:
In the state you live in, there had been several epidemics of a
certain kind of flu, which can be fatal to children under 3. The
probability of each child getting the flu is 1 in 10, but only 1
in 100 children who get the flu will die from it. This means
that 10 out of 10,000 children will die.
A vaccine for this kind of flu has been developed and tested.
The vaccine eliminates the probability of getting the flu. The
vaccine, however, might cause side effects that are also
sometimes fatal.
In the personal decision, subjects were instructed: `Imagine that
you are married and have one child, a one-year old. You wonder
whether you should vaccinate your child. Your child will have a
10 in 10,000 chance of dying from the flu without the
vaccination.' In the policy decision, they were instructed:
`Suppose that the state government is thinking of passing a law
to require vaccination for all children, unless a physician
thinks it is dangerous to the child's health. (Such laws exist
in Pennsylvania and other states.) The question now is whether
you would support such a law. If the law is not passed, the
vaccine will not be offered, and 10 out of 10,000 children will
die from the flu.'
For each case, subjects were asked to indicate the maximum
overall death rate for vaccinated children for which they would
be willing to vaccinate their child (or to support the law),
using the following scale:
Would you vaccinate your child [support a law requiring
vaccination if the overall death rate for vaccinated children
were (check those in which you would vaccinate [support the
law]):
0 in 10,000
1 in 10,000
2 in 10,000
3 in 10,000
4 in 10,000
5 in 10,000
6 in 10,000
7 in 10,000
8 in 10,000
9 in 10,000
10 in 10,000
Eight different version of the vaccination problem were used
(with titles included):
1. Basic case. `The children who die from the side
effects of the vaccination are not necessarily the same ones who
would die from the flu.'
2. Risk group for flu. `Suppose it were discovered that
100 out of every 10,000 children were susceptible to death from
the flu. Children who were not susceptible do not experience any
adverse effects. The test to determine who is susceptible is not
generally available and cannot be given. The overall probability
of death from the flu is still 10 out of 10,000 (with all 10
included in the 100 who are susceptible).'
3. Risk group for vaccine. `Suppose it were discovered
that 100 out of every 10,000 children were susceptible to death
from the side effects of the vaccine (if any such deaths occur).
Children who were not susceptible do not experience any adverse
effects. The test to determine who is susceptible is not
generally available and cannot be given. The overall probability
of death from the flu is still 10 out of 10,000.'
4. Same risk group for flu and vaccine. `Suppose it were
discovered that 100 out of every 10,000 children were susceptible
to death from the flu, and the same children were susceptible to
death from side effects (if any such deaths occur). Children who
were not susceptible do not experience any adverse effects. The
test to determine who is susceptible is not generally available
and cannot be given. This, of course, does not mean that a child
who died from side effects of the vaccination would have died
anyway, since he may not have contracted the flu at all. The
overall probability of death from the flu is still 10 out of
10,000 (with all 10 included in the 100 who are susceptible).'
5. Different risk groups for flu and vaccine. `Suppose it
were discovered that 100 out of every 10,000 children were
susceptible to death from the flu, and 100 out of every 10,000
children were susceptible to death from side effects, but they
were not necessarily the same children. Children who were not
susceptible to death from the flu never die from the flu, and
children who are not susceptible to death from side effects never
die from side effects. The tests to determine susceptibility are
not generally available and cannot be given. The overall
probability of death from the flu is still 10 out of 10,000 (with
all 10 included in the 100 who are susceptible to death from the
flu).
6. Chemical risk group for vaccine. 'Suppose it were
discovered that death from the vaccine is caused by the
interaction of the vaccine with a certain chemical normally
produced by the body. The interaction can occur when the level
of this chemical goes above a certain point. 100 out of every
10,000 children have a chemical above this point. These children
are at risk of death from the vaccine. No other children are at
risk. The test for the chemical is not generally available and
cannot be given. The overall probability of death from the flu
is still 10 out of 10,000.
7. Vaccine might cause flu. `Suppose that the vaccine
consists of a certain dose of weakened bacteria, which would
encourage the body to produce specific antibodies to combat the
flu. The dosage given in the vaccine is the minimum that would
activate the production mechanism of the antibodies. What is
considered death from 'side effects` in item A is actually death
from the flu, caused by the vaccine. The overall probability of
death from the flu is still 10 out of 10,000.
8. Vaccine failure. 'Suppose that an alternative vaccine
were developed. In this case, the vaccine causes no deaths, but
it could be less effective. Of children who are given this
vaccine, some could die from the flu, because the vaccine will
fail. The death rate from the flu is still 10 out of 10,000, as
in case A. In this case, of course, there are no side effects,
but vaccinated children can die from the flu if the vaccine
fails.`
3.2 Results
Four subjects out of 28 were excluded from the analysis because
their answers indicated that they have not understood the task
(e.g., they were more willing to vaccinate when the death rate
from the vaccine was high than when it was low). Eight subjects
gave the same response for all cases. Table 1 shows the means
across subjects of the maximal death rate for which subjects
still decide to vaccinate and of the maximal death rate for which
subjects still support the law requiring vaccination.
Table 1
{\it Mean maximal death rate (out of 10,000) for vaccinated
children (N=20)}
Case Personal decision Support of law
1 Basic case 5.458 5.750
2 Risk group for flu 5.458 6.250
3 Risk group for vaccine 4.350 4.625
4 Same risk group 4.000 3.917
5 Different risk groups 3.667 3.500
6 Chemical risk group for vaccine 3.208 3.125
7 Vaccine might cause flu 5.167 5.542
8 Vaccine failure 7.125 6.833
The results for policy decisions were the same as those for
personal decisions. We found no significant difference in
overall level of vaccination for policy vs. personal decisions
(t=.431, p=.67). The ordering of the cases in the personal
decisions did not differ from their ordering in the policy
decisions either: Friedman analysis of variance of the
differences in ranking of cases for the two types of decisions
did not yield a significant result (Friedman test
statistic=6.698, p=.461). We therefore averaged the results for
the two types of decisions.
To test the hypothesis that presence of risk group for vaccinated
children affects the decision, we compared the cases with a risk
group for the vaccine (case 3 and case 5) to the otherwise
equivalent cases without a risk group for vaccine (cases 1 and
2). Indeed, the presence of a risk group for the vaccine
significantly reduced subjects' willingness to vaccinate: 12
subjects were less willing to vaccinate in case 3 than in case 1,
and only 2 subjects went the other way (p < .02); 15 subjects were
less willing to vaccinate in case 5 than in case 2, and none went
the other way (p < .001).
Our description of the risk group might have aroused suspicion.
Subjects might have thought that sufficient effort would yield
the missing information. To test this possibility, we ran an
additional experiment in which we made clear why the information
was unavailable. Twenty-two subjects were given the basic case
plus a new vaccine risk-group version, identical to version 3,
except that subjects were told: 'A test to determine who is
susceptible is available now, but it must be done when the child
is born, and it was not available when your child was born.`
Results were essentially unchanged. Eight subjects were less
willing to vaccinate in the risk-group condition than in the
basic case versus one who went the other way (p < .02), six gave
the maximum rating (9 or 10) to both versions, and seven gave
equal ratings, less than 9, to the two versions.
Returning to the main experiment, the decisions in cases with
risk group for the flu (case 2 and case 5) did not significantly
differ from the decisions in the matched cases without it (case 1
and case 3). Four subjects were more willing to vaccinate in
case 2 than in case 1, while 4 other subjects were less willing
to vaccinate in this case (p=1); 2 subjects were more willing to
vaccinate in case 5 than in case 3, 9 subjects went the other way
(p=.065, but here the risk group increases rather than decreases
willingness to vaccinate). In sum, a risk group for the vaccine
makes subjects reluctant to vaccinate, but a risk group for the
flu has no significant effect.
The difference between case 4 (same risk group for flu and
vaccine) and case 5 (different risk groups) is not significant: 7
subjects were less inclined to vaccinate in case 5 than in case
4, 2 subjects went the other way (p=.180). This is consistent
with the general lack of effect of the risk group for flu.
Making the missing information more salient caused subjects to be
still less willing to vaccinate. Comparing case 3 (risk group
for the vaccine) with case 6 (chemical risk group for the
vaccine), we find that the additional information given in case 6
caused 10 subjects to give a lower answer in this case than in
case 3. Non of the subjects went the other way (p < .01).
Information concerning the mechanism by which the vaccine causes
death does not seem to matter (7 subjects gave a higher response
in case 1 than in case 7, 3 subjects went the other way. p=.344).
Finally, subjects are willing to accept a higher death rate of
vaccinated children when the death is a result of vaccine failure
rather than vaccine side effects (10 subjects were more willing
to vaccinate in case 8 than in case 1, only one subject went the
other way. p < .02). When death results from vaccine failure, the
decision to vaccinate does not cause death: this condition is
analogous to the 'same children` in Experiment 1. This finding
is consistent with the finding of Spranca et al. (1989) that
omission bias is determined by belief that the commission itself
causes a bad outcome, which would not have occurred if the
decision maker were unaware of the possibility of making a
decision.
4 Experiment 3
The lack of flu risk group effect in the previous experiment
suggests that missing information is weighed differently when it
concerns the effects of omission or the effects of commission.
However, it could also result from the correspondence between the
missing information and the response mode: subjects responded in
terms of number of deaths from the vaccine, so they may have been
more sensitive to information concerning death from the vaccine.
To test this alternative hypothesis, we designed the following
experiment.
As before, subjects were presented with a hypothetical situation,
in which they had to decide whether to vaccinate their child
against an epidemic flu. They were first asked how willing they
were to vaccinate their child in the basic case, in which no risk
group was mentioned. Then they were asked whether they would be
more (or less) inclined to vaccinate their child if they knew of
a risk group for the vaccine, and whether they would be more
inclined to vaccinate if they knew of a risk group for the flu.
Two basic cases were used. In one case the death rate from the
vaccine was given, and the subjects were asked to give the
minimal death rate from the flu that will make them decide to
vaccinate their child. In the second case the death rate from
the flu was given, and subjects were asked for the maximal death
rate from the vaccine that would still make them decide to
vaccinate. For each of these basic cases two versions of risk
group questions, for the vaccine and for the flu, followed.
4.1 Method
Thirty students were solicited as in previous experiments.
The instructions to the questionnaire repeated almost exactly the
description of the hypothetical situation given in Experiment 2,
except that the probability of death from the flu was not given.
Then subjects were told, 'In each of the following cases you are
given some information concerning the vaccine or the flu.` The
cases were as follows:
Case 1: Basic case, flu response mode. Subjects were given
the information that 10 out of 100,000 children will die from the
vaccine, and they were asked to complete the following sentence:
'I will vaccinate my child if more than ... out of 100,000
children will die from the flu.`
Case 2: Vaccine risk group, flu response mode. Subjects
were asked to assume that a risk group for the vaccine has been
discovered, although the tests to determine susceptibility are
not generally available and cannot be given. They were asked
whether, in this case, they would be more or less likely to
vaccinate their child than in Case 1.
Case 3: Flu risk group, flu response mode. This case was
parallel to Case 2 with a risk group for the flu instead of the
vaccine.
Case 4: Basic case, vaccine response mode. This was like
Case 1, except that the available information was death rate from
the flu (40 out of 100,000 children will die from the flu).
Subjects were asked to complete the following sentence: 'I will
vaccinate my child if no more than ... out of 100,000 children
will die from the vaccine.`
Case 5: Flu risk group, vaccine response mode. This was
like Case 3 except that subjects were asked to refer their
decision to Case 4.
Case 6: Vaccine risk group, vaccine response mode. This was
like Case 2 except that subjects were asked to refer their
decision to Case 4.
4.2 Results
Table 2 shows, for each relevant case, the number of subjects who
would be affected by the knowledge of a risk group and the
direction of change in their decision (relative to the
corresponding basic case).
Table 2
{\it Number of subjects affected by presence of risk groups in
Experiment 3 (N=30).}
Response mode Less likely More likely
Risk group in basic case to vaccinate to vaccinate
Vaccine Death rate from 19 5
flu (Case 2)
Death rate from 21 5
vaccine (Case 6)
Flu Death rate from 12 13
flu (Case 3)
Death rate from 13 13
vaccine (Case 5)
Note first that knowledge of the risk group had nearly identical
effects in the two response modes. This was true for both the
risk group for vaccine and the risk group for flu.
To examine the effect of risk group, we averaged the numerical
responses across the two cases with the risk group and the two
cases without it. For the vaccine risk group (Cases 2 and 6), we
find that 18 subjects were less likely to vaccinate when they
knew of a risk group for the vaccine (relative to the basic case,
without mention of a risk group), and only 3 subjects were more
likely to vaccinate (p < .001). The presence of risk group for the
flu did not have a systematic effect on subjects' decisions: 8
subjects were less inclined to vaccinate when they knew of a risk
group for the flu (Case 3 and 5), and 10 subjects were more
inclined to vaccinate (p=.815).
We have therefore replicated the result of Experiment 2 and
eliminated an alternative explanation of this result. Subjects
are less inclined to vaccinate their child when they know of a
risk group for vaccinated children but are not similarly affected
by existence of a risk group for children who get the flu. This
finding does not seem to result from having the information
concerning death rates from the vaccine made more salient by the
response mode.
5 Experiment 4
It is evident from the previous experiments that presence of a
risk group for vaccinated children decreases the willingness to
vaccinate when there is no way of knowing who belongs to the risk
group, even when the overall risk from the vaccine is kept
constant. One possible reason for this effect may be that when
faced with ambiguity, people tend to assume the worst. That is,
if they know of the existence of a risk group, and have no way of
finding out whether they belong to this group, they will be
inclined to assume that they do. If this is true then we should
expect people to be less inclined to vaccinate as the death rate
within the risk group increases.
Experiment 4 tested this hypothesis by varying the size of the
risk group relative to the population and the vaccine caused
death rates of children who are in the risk group. Specifically,
the risk group was either small (1000 out of 100,000) or large
(10,000 out of 100,000), and the death rate for children in the
risk group was either high (10%) or low (1%). Accordingly, we
had three levels of overall death rate in the population: high
(1%), medium (.1%), or low (.01%). Obviously, the probability of
death from the vaccine in the population as a whole is equal to
the product of the relative size of the risk group and the death
rate within this group.
As in the previous experiments subjects were told that it is not
possible to determine in advance who belongs to the risk group.
Subjects were asked to indicate the minimal number of deaths from
the flu in the population that will make them decide to vaccinate
their child. As the death rate from the vaccine was not kept
constant across the different conditions, we introduced three
control conditions with the corresponding overall vaccine-caused
death rates, but without risk groups. Thus, the risk group
effect can be determined at each level of overall death rate.
5.1 Method
Forty subjects were solicited as in previous experiments.
Subjects went through all the cases. To control for order
effects, two different orders of the cases were used, with about
half of the subjects assigned to each of them. However, in both
orders the control conditions preceded the risk group conditions.
The instructions to the questionnaire were identical to the
instructions in Experiment 2, except for the description of the
task, which read: 'In each of the following cases you are given
some information concerning the vaccine of the flu, and you are
asked to decide what is the minimal death rate from the flu (how
many children out of every 100,000 will die) that will make you
decide to vaccinate.` The subjects had to fill in the blank in
the following sentence: 'I will vaccinate my child if more than
... out of 100,000 will die from the flu.`
Seven different versions of the vaccination problem were used.
In the three control cases, 10 (in Case 1), 100 (Case 2), or 1000
(Case 3) 'out of 100,000 children will die from the vaccine.`
The risk group cases were:
Case 4. 1000 children are at risk, 10 of them will die from the
vaccine.
Case 5. 1000 children are at risk, 100 of them will die.
Case 6. 10,000 children are at risk, 100 of them will die.
Case 7. 10,000 children are at risk, 1000 of them will die.
The exact wording of the risk group cases was (we give case 4 as
an example): 'Suppose it were discovered that 1000 out of
100,000 children are susceptible to death from the vaccine. This
means that outside of these 1000 children no one is in danger of
death from the vaccine. Of the children in the risk group, 10
out of 1000 will die from the vaccine. The tests to determine
susceptibility are not generally available and cannot be given.`
5.2 Results
Table 3 shows the mean response for each version. Examining the
control conditions first, we find an immense omission bias: the
minimal number of deaths from the flu that would cause subjects
to vaccinate their child is at least ten times the number of
deaths from the vaccine. This result extends the results of
previous experiments through the use of a free-response format.
A comparison between the three control conditions shows that as
the number of deaths caused by vaccination increases, the ratio
between the number of deaths from the flu and the number of
deaths from the vaccine decreases.
Table 3
{\it Mean minimal death rates from flu for giving the vaccine
(N=40).}
Mean minimal
Risk group size Vaccine deaths flu death rate
Case (out of 100,000) (out of 100,000) (out of 100,000)
1 --- 10 2,584
2 --- 100 4,316
3 --- 1,000 10,693
4 1,000 10 8,139
5 1,000 100 7,739
6 10,000 100 7,287
7 10,000 1,000 14,435
For the risk group versions we computed the log of the ratio
between the response to each version and the response to the
corresponding control version. (Positive logs indicate less
willingness to vaccinate with the risk group.) The means of
those logs, across all subjects are reported in Table 4. Each of
the cells in Table 4 is significantly larger than zero. Thus, we
replicated here the 'vaccine risk group` effect found in earlier
experiments: the presence of a risk group for the vaccine results
in an augmentation of the omission bias. Other things being
equal, subjects are even less inclined to vaccinate when they
know the risk is 'unevenly` spread across the population.
Table 4
{\it Mean log of the ratio: response to risk group case over
response to corresponding control case (N=40).}
Risk group size Vaccine deaths Control Log of
Case (out of 100,000) (out of 100,000) case ratio
4 1,000 10 1 1.465
5 1,000 100 2 0.848
6 10,000 100 2 1.073
7 10,000 1,000 3 0.702
A multivariate test of the differences between the cells yielded
a significant result (F(4,36)=4.61, p < .01). However, the only
significant pairwise comparison is the comparison between the two
extreme cases, Case 4 and Case 7 (F(1,39)=11.34, p < .01):
Willingness to vaccinate was affected more by a small risk group
with low risk than by a large risk group with high risk. It is
likely that this distinction is due to the difference in overall
death rates rather than the independent effect of risk group size
or death rate within this group. Indeed, a comparison of the
large risk group with low risk and the small risk group with high
risk cases did not show a significant difference (F(1,39)=.25,
p=.61).
These results suggest that as the overall risk gets higher,
subjects are less sensitive to the presence of risk group. The
results do not support the 'worst case` hypothesis, which holds
that subjects who do not know whether they belong to the risk
group tend to assume that they do.
6 General Discussion
Subjects are reluctant to vaccinate when the vaccine can cause
bad outcomes, even if the outcomes of not vaccinating are worse.
This is true regardless of whether the outcomes concern an
individual child, in which case the difference is expressed in
probabilities, or whether they concern a large population, in
which case the outcomes differ in the number of children
affected. Some subjects make an absolute rule, and will accept
no risk whatsoever that they will 'cause` a death even in return
for complete elimination of the risk of death from other causes
(see Baron, 1986). These findings show a strong bias toward
omissions of the sort found by Spranca et al. (1990), who discuss
at greater length the determinants of this bias.
In a pilot study we asked subjects to explain their reasons for
deciding not to vaccinate at the optimal level. Many subjects
did not write any arguments, hence we cannot subject the list of
arguments to a quantitative analysis. However, it is worth
noting that many of the arguments that were given revolved around
the issue of responsibility.
One subjects wrote, 'I feel that if I vaccinated my kid and he
died I would be more responsible for his death than if I hadn't
vaccinated him and he died - sounds strange, I know. So I would
not be willing to take as high a risk with the vaccine as I would
with the flu.` Another subject wrote, 'I'd rather take my chance
that the child will not catch the flu than to be responsible for
giving my child a vaccine which could be fatal.` A third subject
wrote, '...I did not want to risk killing the child with a
vaccine that is optional. It would have been my fault if the
child died from the vaccine.` These arguments illustrate the
main concern of subjects regarding the vaccine: One is perceived
to be more responsible for outcomes of commissions than for
outcomes of omissions.
Reluctance increases when we call to subjects' attention a piece
of missing information about the existence of a risk group for
death from the vaccine. This finding supports the proposal of
Frisch and Baron (1988) that the perception of missing
information can make people reluctant to act, even when the
information is unobtainable. In the present experiments, unlike
previous experiments on ambiguity effects, the salience of
missing information is varied independently of subjects'
knowledge of the final probability. Frisch (1988) has found that
subjects in experiments such as Ellsberg's often do not know that
the expected probability is constant across the conditions being
compared. We tell subjects this explicitly.
It is interesting to note that the missing information is, in a
sense, nothing new. If subjects thought about it, they could
easily imagine that death from vaccine is predicted by a great
many factors. Subjects have typically heard about risk factors
for most diseases. If the information is unavailable, knowing of
its potential existence cannot affect action. We therefore
suggest that the ambiguity effect we have found is a kind of
framing effect (Frisch & Baron, 1988), a result of our calling
the information to subjects' attention rather than a result of
the existence of the information itself (which subjects might
well imagine).
The effect of ambiguity does not appear to result from a tendency
to assume that the missing information is necessarily bad. In
the vaccination problems we used, a tendency to assume the worst
would mean presuming that one's child belongs to the relevant
risk group. This would imply greater willingness to vaccinate
when subjects know of a risk group for the flu, and a decreasing
willingness to vaccinate as the risk for the children in the
vaccine risk group increases. We found no support for either of
those predictions.
Ambiguity (salient missing information) is considered relevant
only in the case of commissions. Consistent with the view that
one feels more responsible for results of commission that for
results of omission, subjects seem to think of the effect of
missing information on the consequences of their action
(vaccinating), not the consequences of their inaction (not
vaccinating). Ambiguity concerning the consequences of action
increases the reluctance to act, but there is no corresponding
effect of the omission option. A possible explanation of this
result is that ambiguity increases the feeling of responsibility
for a bad outcome that a decision maker causes. Ambiguity
therefore has no effect on commissions because those subjects who
are affected by feelings of responsibility do not feel
responsible for the results of omissions.
Our findings were obtained in certain hypothetical situations, so
their generality is unclear. They do show that the patterns of
inference we have found are fairly easy to detect, and it
therefore seems likely that these patterns are found elsewhere,
including some real situations. Parallels with real cases, such
as pertussis vaccine, reinforce our findings. We and our
colleagues have begun to study real decisions about vaccination
as well, and these results will be reported separately.
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Footnotes:
1This work was
supported by grants from the National Institute of Mental
Health (MH-37241) and from the National Science Foundation
(SES-8509807 and SES-8809299). We thank John C. Hershey and
Howard Kunreuther for comments.
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