How Near-Miss Events Amplify or Attenuate Risky Decision Making
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CREATE Research Archive Published Articles & Papers 2012 How Near-Miss Events Amplify or Attenuate Risky Decision Making Robin Dillon-Merrill Georgetown University, rld9@georgetown.edu Catherine H. Tinsley Georgetown University, tinsleyc@georgetown.edu Matthew A. Cronin George Mason University, mcronin@gmu.edu Follow this and additional works at: http://research.create.usc.edu/published_papers Recommended Citation Dillon-Merrill, Robin; Tinsley, Catherine H.; and Cronin, Matthew A., "How Near-Miss Events Amplify or Attenuate Risky Decision Making" (2012). Published Articles & Papers. Paper 93. http://research.create.usc.edu/published_papers/93 This Article is brought to you for free and open access by CREATE Research Archive. It has been accepted for inclusion in Published Articles & Papers by an authorized administrator of CREATE Research Archive. For more information, please contact gribben@usc.edu.
Published online ahead of print April 18, 2012
MANAGEMENT SCIENCE
Articles in Advance, pp. 1–18
ISSN 0025-1909 (print) ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.1120.1517
© 2012 INFORMS
posted on any other website, including the author’s site. Please send any questions regarding this policy to permissions@informs.org.
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How Near-Miss Events Amplify or
Attenuate Risky Decision Making
Catherine H. Tinsley, Robin L. Dillon
McDonough School of Business, Georgetown University, Washington, DC 20057
{tinsleyc@georgetown.edu, rld9@georgetown.edu}
Matthew A. Cronin
School of Management, George Mason University, Fairfax, Virginia 22030, mcronin@gmu.edu
I n the aftermath of many natural and man-made disasters, people often wonder why those affected were
underprepared, especially when the disaster was the result of known or regularly occurring hazards (e.g.,
hurricanes). We study one contributing factor: prior near-miss experiences. Near misses are events that have
some nontrivial expectation of ending in disaster but, by chance, do not. We demonstrate that when near misses
are interpreted as disasters that did not occur, people illegitimately underestimate the danger of subsequent
hazardous situations and make riskier decisions (e.g., choosing not to engage in mitigation activities for the
potential hazard). On the other hand, if near misses can be recognized and interpreted as disasters that almost
happened, this will counter the basic “near-miss” effect and encourage more mitigation. We illustrate the robust-
ness of this pattern across populations with varying levels of real expertise with hazards and different hazard
contexts (household evacuation for a hurricane, Caribbean cruises during hurricane season, and deep-water oil
drilling). We conclude with ideas to help people manage and communicate about risk.
Key words: near miss; risk; decision making; natural disasters; organizational hazards; hurricanes; oil spills
History: Received June 29, 2010; accepted November 27, 2011, by Teck Ho, decision analysis. Published online
in Articles in Advance.
Introduction When people escape an impending disaster by
In the aftermath of Hurricane Katrina, the public and chance, they have experienced a “near miss.” More
media alike questioned why so many people failed precisely, a near miss is an event that has some non-
to evacuate the Gulf Coast and why the government trivial expectation of ending in disaster but because of
and first-responder organizations were so appallingly luck did not (Reason 1997, Dillon and Tinsley 2008).1
underprepared (Glasser and Grunwald 2005). The rea- Our natural environment produces many examples
sons for these failures are often rooted in experiences of near misses: a random tree pattern saves a house
with previous hurricanes. In the lead-up to the storm, from a mud slide or a hurricane weakens right before
Governor Haley Barbour of Mississippi warned of it hits a city. Organizations experience near misses
“hurricane fatigue”—the possibility that his constit- as well. For example, in the deep-sea oil drilling
uents would not evacuate because they had success- industry, dozens of Gulf of Mexico wells in the past
fully weathered earlier storms; similarly, one former
two decades suffered minor blowouts during cement-
Federal Emergency Management Agency official said
ing; however, in each case chance factors (e.g., favor-
people in the agency unfortunately approached the
able wind direction, no one welding near the leak at
Katrina response as it had other responses, though the
aftermath of Katrina was clearly “unusual” (Glasser the time, etc.) helped prevent an explosion (Tinsley
and Grunwald 2005). Such complacency is not exclu- et al. 2011).
sive to hurricanes. Citizens who survive natural disas- We study how prior near misses influence peoples’
ters in one season often fail to take actions that would interpretation of similar hazards and thus influence
mitigate their risk in future seasons (e.g., moving off future mitigation decisions. We do this in multiple
a Midwestern flood plain or clearing brush to prevent contexts: a single household threatened by hurricane,
wildfires in the West; see Lindell and Perry 2000). Our
research demonstrates that people may be complacent 1
Other events have been labeled “near misses” such as last minute
because prior experience with a hazard can subcon-
heroic efforts to avert crisis or interventions of chance that cause
sciously bias their mental representation of the hazard bad rather than good outcomes (e.g., narrowly missing an air-
in a way that often (but not always) promotes unre- plane departure). Thus we specify our focus here on near misses
alistic reassurance. as chance-dependent good outcomes.
1Tinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
2 Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS
a planned Caribbean cruise threatened by a hurri- causing major flooding and wind damage. This infor-
cane, and oil-rig operations threatened by a danger- mation provides input to assessing probabilities and
posted on any other website, including the author’s site. Please send any questions regarding this policy to permissions@informs.org.
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ous storm. We explain why and when a near miss outcomes, but it also cues the retrieval of other
produces complacency versus action and offer pre- information that will be used to refine these esti-
scriptions for risk communication strategies based on mates and their relationship to each other. Krizan and
how different types of near misses operate. Windschitl (2007) provide a useful conceptualization
of this knowledge retrieval process: given a situation
How Near Misses Influence involving risk, people must assess what this infor-
mation means in light of what they already know.
Cognitive Processes “What they already know” is the domain knowl-
When facing an imminent hazard, people should
edge Gonzalez and Wu (1999) spoke of as modify-
assess the risk, which is technically a function of the
ing assessments of probabilities and outcomes and
probability of the event occurring and the harm that
their combination. To select which domain of knowl-
results from the event if it occurs (Kaplan and Garrick
edge to use, people can use the cognitive category to
1981, von Neumann and Morgenstern 1944). This is
the classic subjective expected utility (SEU) model. which a hazard event belongs (Kahneman and Miller
For example, to decide whether or not to evacuate 1986). “Hurricane” represents a category of events
for an impending hurricane, people should combine that guides the retrieval of relevant knowledge from
assessments of the likelihood of their location being memory. So although an avalanche is also a hazard,
hit by the hurricane and how bad the damage could knowledge about avalanches would not be retrieved
be. Such assessments make use of the information based on the hurricane category (although knowledge
at hand, but people also bring past personal experi- of flooding, which is related to hurricanes, might).
ences into their evaluation of the risk (Fishbein and Near misses come into play in that they can modify
Azjen 2010, Tierney et al. 2001). We show that a par- the hazard category, because prior experiences with
ticular type of personal experience, near misses, have an event can alter the cognitive category for that event
an undue influence on how people evaluate risk and (Kahneman and Miller 1986). Thus, after a near miss,
can lead to questionable choices when people face an the knowledge people will use in assessing SEU com-
impending hazard with which they have had prior ponents for a future hazard will change. This explains
near-miss experience. We show that this near-miss why prior outcomes can strongly influence future
effect is robust because it seems to implicitly influ- decisions and realized outcomes tend to be seen as
ence the thoughts people use as inputs to their deci- deterministic (Hastie and Dawes 2001).
sion making. This near-miss effect can be countered, The chain of events we posit is as follows:
but doing so needs to use the same kind of implicit (1) upon encountering a hazard, people retrieve rel-
mechanism. evant knowledge from memory about that hazard,
Although an SEU model provides a strong basis for a process that is largely implicit (Anderson 1983, 1993;
characterizing how people decide to respond to haz- Kahneman and Miller 1986) but results in assessments
ards, past research (Gonzalez and Wu 1999, Tversky of probabilities, outcomes, and how they will be com-
and Fox 1995) has shown that the model compo- bined; (2) an explicit evaluation of the risk of the haz-
nents (including the likelihood estimates for probabil- ard is made largely using an SEU framework; and
ity, (un)attractiveness estimates for outcomes, and the (3) once the risk is evaluated, people must explicitly
ways in which these can be combined into an eval- choose what behavior to engage. In the next section,
uation of risk) can vary based on characteristics of we hypothesize how near misses influence this chain
the situation such as whether the likelihood estimates of events.
are very large, moderate, or very small (Tversky and
Fox 1995). More importantly for the present work, Hypotheses
Gonzalez and Wu (1999) demonstrated that SEU can Dillon and Tinsley (2008) found that near misses
vary both between and within individuals (i.e., the in completing a space project encouraged people to
same person may be risk averse in one situation and choose a riskier strategy when faced with a future
risk seeking in another) because the components are hazard threat to the mission. Although highly contex-
all sensitive to the domain knowledge people use tualized and specific, their research showed that near
when evaluating the risky event. misses are events that alter evaluations of risk, and
We argue that near misses change the domain thus a near-miss bias should generalize to many kinds
knowledge (or cognitive category) that people use in of hazards and be relevant to a large array of natu-
their assessment of the SEU components, and thus ral and man-made hazard environments. Near-miss
can bias the judgments people make about risky sit- events in the hazard context often highlight resiliency
uations. For example, people may learn that a hur- because people escape harm. For example, imagine
ricane has a 50% chance of striking their town and that a hurricane is being tracked in the Caribbean andTinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS 3
is a concern to two neighborhoods, A and B. Peo- to our scenario, imagine there is another neighbor-
ple in both neighborhoods rely on existing domain hood C next to neighborhood B who also experienced
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knowledge to direct their thinking about the risk the hurricane. Because neighborhood C was closer to
of this situation and whether or not to take protec- the center of the storm, neighborhood C was hit with
tive action. Assume that as the storm grows nearer, a stronger force, and their sandbag levees collapsed,
it becomes clear that the hurricane will miss neigh- resulting in significant flooding. For the people of
borhood A, but neighborhood B is still in danger, neighborhood B, seeing damage to neighborhood C
and they create a sandbag levee around the main further modifies the basic near-miss information (“we
city buildings. Fortunately, when the hurricane makes were ok, but look what happened to them”) to alter
landfall, the storm surge subsides before overtopping the hurricane category away from resilience. With the
B’s makeshift levee, and the town suffers no damage. stimulus of damage to a neighboring town, they may
In this illustration, neighborhood A did not experi- encode that the disaster almost caused harm, and that
ence a near miss because there was no expectation they were vulnerable to possible damage.
of harm. Neighborhood B experiences a near miss If the near-miss effect operates through mostly
because there was a nontrivial expectation that the implicit processes, then we expect that counteracting
flooding could occur, but for good fortune (i.e., chance the near-miss effect will require further modification
storm characteristics) it did not. We argue that the of the hazard category (Kahneman and Miller 1986).
near-miss event experienced by people of neighbor- When the near-miss experience also highlights the
hood B will change the hurricane category knowledge harm the event could have caused, it adds informa-
in a way that when facing a new hazard warning, tion to counteract the basic resilient near-miss effect;
the domain knowledge retrieved will make the haz- that is, the near miss (no harm done) can alter the cat-
ard seems less threatening, leading to complacency. egory to make the hazard seem less threatening, but
Thus, near misses that emphasize resiliency will lead new harm information counteracts this with associa-
to riskier behavior. tions of vulnerability. In our illustration, the people
Hypothesis 1 (H1). People with near-miss information from neighborhood B would now encode informa-
that highlights how a disaster did not happen will be less tion about both resilience (from the absence of dam-
likely to take mitigating action for an impending hazard age) and potential harm (a neighboring town was
than people without this information. severely flooded). When facing a warning about a
future impending hurricane, people from neighbor-
The process we have described for how near misses hood B should now be less swayed by the fact that
work (change to the category knowledge) does not they escaped harm.
restrict the direction in which the category may be
modified. Near-miss experiences do have some plas- Hypothesis 2 (H2). People with vulnerable near-miss
ticity in their interpretation. For example, in their dis- information (that highlights how an event almost caused
cussion of aviation near misses, March et al. (1991, harm) will be more likely to take mitigating action for
p. 10) essentially argue that near collisions can pro- an impending hazard than people with resilient near-miss
duce two different types of salient associations. They information.
describe: How the new hazard is evaluated will depend on
Every time a pilot avoids a collision, the event provides the category knowledge retrieved, which in turn is
evidence both for the threat [of a collision] and for its dependent on how the prior near miss modified the
irrelevance. It is not clear whether the 0 0 0 organization hazard category. Moreover, this modification could
came [close] to a disaster 0 0 0 or that the disaster was produce different assessments of probabilities (P ),
avoided. outcomes (O), or risk (R) because the information
If people experience the near miss as a disas- about the particular hazard that is embedded in the
ter that almost happened rather than a disaster that warning will be integrated with domain knowledge
was avoided, then their hazard category should be about the hazard category, thereby influencing some
associated with vulnerability. We distinguish these part of the SEU model (P , O, and/or R).
“vulnerable” near misses (wherein a disaster almost We predict that near misses change the negativ-
happened and results in the perceived vulnerability ity associated with a bad event rather than chang-
of the system) from “resilient” near misses (wherein ing probability assessments. This is consistent with
a disaster could have but did not happen and results Windschitl and Chambers’ (2004) finding that people
in the perceived resilience of the system).2 Returning are more likely to change their feelings about a choice
than their explicit beliefs about the probabilities. Fur-
2
See also Kahneman and Varey (1990) for arguments on the critical thermore, in the domain of near misses, Dillon and
distinction between an event that did not occur and an event that Tinsley (2008) showed that people changed their per-
did not but almost occurred. ceptions of risk without changing their probabilities.Tinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
4 Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS
Depending on the type of near miss (resilient or to justify their choice (i.e., reverse causality). Study 6
vulnerable), the valence of the information retrieved corroborated the findings of Studies 2–5 with actual
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should change, influencing risk estimates. As the risk behavior by having participants’ decisions regarding
estimates change, so should the resulting judgments a risky situation have financial consequences for their
about what to do for an impending hazard. compensation.
Hypothesis 3 (H3). Resilient near misses will decrease
Study 1
one’s feelings of risk more than vulnerable near misses
This study provides evidence of the near-miss effect
without changing perceived probabilities, and these feelings
in actual hazard situations. It is well established that
of risk will mediate the corresponding behavioral response.
people’s mitigation decisions, evacuation in the case
of hurricanes, are influenced by what relevant oth-
Overview of Studies ers do (Tierney et al. 2001). We tested whether or not
Our hypotheses were tested across multiple stud- prior near-miss experiences reduce evacuation behav-
ies, where we sought different types of respondents ior beyond what is due to social cues and a house-
and used different threats and contexts to demon- hold’s specific geographic location (proximity to coast
strate that our effects are robust across various pop- and waterways). This speaks to the importance of the
ulations and decisions. Study 1 looked for evidence effect (i.e., it is not overwhelmed by people’s incli-
of the near-miss effect using a field survey of house- nation to do what their neighbors do), and why it
holds in coastal counties of Louisiana and Texas warrants further study.
who experienced Hurricane Lili.3 We examined how Participants and Procedure. In the spring of 2003,
previous storm experience as well as prior near- six months after Hurricane Lili hit the Louisiana
miss experiences (in the form of unnecessary evac- coastline, 1,000 households from five affected areas
uations) influenced whether or not the individuals (200 each area: Vermilion and Cameron Parishes in
surveyed evacuated for Hurricane Lili. Studies 2–6 Louisiana and Orange, Jefferson, and Chambers coun-
used the laboratory to discover how the near-miss ties in Texas) were randomly selected and mailed a
phenomenon operates. Study 2 examined how encod- survey by the Hazard Reduction and Recovery Cen-
ing near misses as resilient or vulnerable led to dif- ter at Texas A&M asking whether they had evacu-
ferent evacuation rates for a hypothetical hurricane ated. For the storm, the National Hurricane Center
and demonstrated that the addition of vulnerability had issued a hurricane warning, and local officials
information to the near-miss stimulus can counteract had issued an early evacuation advisory in these five
the complacency effect. Study 3 examined the compo- areas. A total of 507 usable surveys were returned for
nents of people’s SEU assessments. It probed people’s a response rate of 50.7%, which exceeds similar hur-
assessments of probabilities (P ), outcome attractive- ricane studies (Prater et al. 2000, Lindell et al. 2001).4
ness (O), and their ultimate judgments of risk versus To obtain this response rate, nonresponsive house-
safety (R) to test our hypothesized mediation. Study holds were sent a follow-up survey every three weeks
4 generalizes our basic finding by changing the con- (until a total of three surveys had been sent).
text from a house to a cruise ship; in doing so we Variables. Respondents were asked (on a 1–5 scale,
address a concern that participants may be updating where 1 equaled “not at all” and 5 equaled “very
their calculations of the risk after a resilient near miss. great extent”) whether or not they had “previous
Additionally, in Study 4, we examine the role coun- experience with an unnecessary evacuation.” This
terfactuals have in the risky decision. Study 5 offered was our proxy for a resilient near miss (i.e., where
evidence that near misses do in fact change the hazard a disaster did not happen), which was treated as
category, and hence the knowledge associated with
the independent variable. For the dependent variable,
a hazard, by examining what participants’ thought
respondents were asked whether or not they evacu-
about a hazardous situation. This study removed the
ated. For control variables, respondents were asked
need to make a decision, thereby (a) providing evi-
(on a 1–5 scale, where 1 equaled “not at all” and 5
dence for the first (implicit) step in our sequence of
equaled “very great extent”) about individual geo-
how near misses affect cognitive processes and (b)
graphic proximity including how close they lived to
discounting a concern that people first chose what to
the coast and how close they lived to inland water
do and then, when forced to answer questions, gen-
such as bays, bayous, or rivers. Also on the same
erate assessments of probabilities, outcomes, and risk
1–5 scale, respondents were asked about social cues
including whether they saw businesses closing; saw
3
The survey was conducted six months after Hurricane Lili by the
Hazard Reduction and Recovery Center at Texas A&M. Hurricane
4
Lili was the deadliest and costliest hurricane of the 2002 Atlantic See Lindell et al. (2005) for more details of the original survey
hurricane season. collection.Tinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS 5
friends, relatives, neighbors, or coworkers evacuat- this study was to provide empirical evidence beyond
ing; heard announcement of a hurricane warning; the post hoc evaluations of highly visible disasters
posted on any other website, including the author’s site. Please send any questions regarding this policy to permissions@informs.org.
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and heard local authorities issue a recommendation like Katrina that the near-miss effect happens and
to evacuate. They were also asked whether or not merits further study. To understand the mechanics
they saw storm conditions such as high wind, rain, or of the near-miss phenomena in detail, we examine it
flooding, and whether they had personal experience using a series of laboratory studies.
with hurricane storm conditions.
Analysis and Results. Factor analysis revealed that Study 2
some control variables could be averaged into scales. We argue that near-miss information encourages
We created the “individual geographic proximity” riskier behavior (H1), but that this effect can be coun-
(alpha = 0074) scale by averaging the first two control teracted when the near miss includes information
variables and the “social cue” (alpha = 0081) scale by that highlights vulnerability (H2). We tested for the
averaging the next four control variables. difference between a resilient near miss and a vul-
Binary logistic regression was used to test H1, with nerable near miss by giving participants informa-
evacuation (yes/no) as the dependent variable. Four tion about an impending hurricane and asking them
control variables were entered in the first step (geo- whether or not they would evacuate. We have also
graphic proximity, social cues, see storm conditions, and said that this process operates automatically, and that
prior hurricane experience); our independent variable such automaticity makes the effect robust even in the
(prior unnecessary evacuation) was entered in the sec- face of experience. In this study, we verify our basic
ond step. Regression results, displayed in Table 1, hypotheses and test the robustness of the effect across
show that geographic proximity, social cues, and see- populations with varying levels of experience and
ing storm conditions all have a positive influence on expertise.
evacuation behavior, whereas prior unnecessary evac- Participants. For Study 2, we collected data from
uations has a negative influence. Thus, controlling for four different samples. Participants were (1) 352
geographic proximity, social cues, and seeing storm undergraduate and 47 graduate business students
conditions, prior near-miss experiences in the sense from a large, private university in the eastern United
of having evacuated when later deemed unnecessary States who completed a number of exercises, includ-
lead to less protective action in the form of evacuation ing ours, in return for class participation points;
in the face of an impending hurricane, supporting H1. (2) 82 upperclass undergraduate students at Tulane
Discussion. This study shows that prior near-miss University in New Orleans (two-thirds of whom evac-
experiences influence the behavior of people facing uated for Hurricane Katrina) who completed the short
similar subsequent threats, even amid all the con- exercise at the end of a regularly scheduled lec-
current forces that affect such behavior. Those with ture session; (3) 187 undergraduate business students
resilient near-miss experiences were significantly less from the same university as sample 1 who com-
likely to evacuate than those without this experi- pleted a number of exercises online, of which ours
ence, supporting H1. We recognize that an unneces- was one, in return for class participation points; and
sary evacuation is an imperfect proxy for a resilient (4) 102 emergency managers who averaged 13.6 years
near-miss experience, although we think there is cor- of experience with natural disasters, whose participa-
respondence because unnecessary evacuations imply tion was solicited though email lists and newsletters
that a disaster did not happen. However, the point of associated with the Natural Hazard Center in Col-
orado, and who participated in exchange for entrance
in a lottery to win sweatshirts.
Table 1 Study 1—Logistic Regression Results for Procedure. Participants read that they lived in
Evacuation from Hurricane Lili by Near Miss
an area subject to hurricanes and that the National
Model 1 Weather Service was tracking a hurricane that had a
Odds ratio 30% chance of hitting their community with moder-
Control variables
ate force within 36 hours. They were also told that
geographic proximity 1038∗∗ they lived alone, had no pets, and that evacuation
social cues 2022∗∗∗ would incur a sure loss of $2,000. However, if they
see storm conditions 0080∗ stayed and the hurricane hit, the collateral damage
prior hurricane experience 1000 (above and beyond damage to house, such as damage
Independent variable to one’s car, self, portable personal belongings, etc.)
prior unnecessary 0085∗
would add up to $10,000 (see Appendix A for the
evacuation (near miss)
full text). After reading the vignette, they answered
Nagelkerke R2 0023∗∗
whether or not they would evacuate. For the New
∗
p < 0005; ∗∗ p < 0001; ∗∗∗ p < 00001. Orleans sample, participants were also asked whetherTinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
6 Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS
or not they had evacuated for Hurricane Katrina, and Figure 1 Study 2—Evacuation Rate by Condition for Different Sample
two-thirds reported that they had.5 Collections (See Table 2 for 2 Test Results)
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Variables. We had three conditions: whether 100
90 Control
participants had resilient or vulnerable near-miss Resilient NM
80
information or no near-miss information (control).
% Evacuation
70 Vulnerable NM
Participants in the no near-miss information (control) 60
condition read, “You have no specific data regarding 50
40
past hurricane impacts to your property.” Participants 30
in the resilient near-miss condition read, “You have 20
lived in this house through three prior storms similar 10
0
to that forecasted, and you and your neighbors have General New Orleans General Expert
never had any property damage.” Participants in the students students students emergency
(Collection 1) (Collection 2) managers
vulnerable near-miss condition for Collections 1 and 2
Collection
read the resilient near-miss condition plus “In the last
storm, however, a tree fell on your neighbor’s house, Note. NM, near miss.
completely destroying the second story. If anyone had
been inside, they would have been seriously hurt.” study and the previous field study show the near-
Participants in the vulnerable near-miss condition miss effect, neither provides evidence of the mecha-
for Collections 3 and 4 read the resilient near-miss nism by which near-misses operate. The next study
condition plus “In the last storm, however, a tree fell examines whether people’s evaluations of the nature
on your neighbor’s car and completely destroyed it.” 6 of the hazard mediate their decision to evacuate.
The dependent variable was whether or not partic-
ipants would evacuate. Study 3
Analysis and Results. Figure 1 shows the per- This study looked into the hypothesized mechanisms
centage of participants in each condition for each through which near-miss information works. If differ-
collection who chose to evacuate. For all four sam- ent types of near misses cause changes to the hazard
ples, participants with resilient near-miss information category, then we would expect the different types
chose to evacuate significantly less than those with of near misses to change the assessments of risk (R)
but not the assessments of probability (P ). Moreover,
no near-miss information, supporting H1, and partic-
assessments of R should mediate observed mitigation
ipants with vulnerable near-miss information chose
choices (H3).
to evacuate more than those with resilient near-miss
Participants and Procedure. Participants in Study 3
information, supporting H2 (see Table 2 for 2 tests).
were 236 undergraduate and graduate business stu-
Discussion. Study 2 found that people with
dents who completed a number of exercises online, of
resilient near-miss information that highlights how a
which ours was one, in return for class participation
disaster did not happen were less likely to evacu-
points. Participants read the same story as in Study 2,
ate for an impending hurricane than people without
about living in a hurricane area, yet this time they
near-miss information (supporting H1). On the other answered questions about the thoughts and feelings
hand, people with vulnerable near-miss information associated with the impending hazard.
that highlights how a disaster almost happened were
more likely to evacuate than people with resilient
near-miss information (supporting H2). And these Table 2 2 Results for Study 2
results were robust across participants representative 2 (1): Control vs. 2 (1): Resilient vs.
of the general population (Collections 1 and 3), those resilient near miss vulnerable near miss
who live in a culture highly sensitive to hurricanes Data collection (Hypothesis 1) (Hypothesis 2)
(i.e., New Orleans), many of whom had prior evacu- 1 = General 2 415 = 20063, p < 00001 2 415 = 11098, p < 00001
ation experience (Collection 2), and emergency man- population
agement practitioners (Collection 4). Although this students
2 = Tulane 2 415 = 8002, p < 0001 2 415 = 6096, p < 0001
students
5
Note that given the timing of the data collection, these students (experienced)
would still have been in high school during Hurricane Katrina and 3 = General 2 415 = 3031, p < 0005 2 415 = 10086, p < 0001
not matriculated students at Tulane. population
6 students
We altered the wording for Studies 3 and 4 to test whether the
4 = Emergency ( 2 415 = 2085, p < 0005 2 415 = 6068, p < 0001
effect was robust to different types of harm information—bodily
managers
injury versus harm to property—which it was. We thank Howard
(experts)
Kunreuther for this suggestion.Tinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS 7
Variables. We used the same three conditions Figure 2 Study 3—Risk Judgments by Condition
from Study 2 for the independent variables: control,
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8
resilient near miss, and vulnerable near miss (with Perceived risk Likelihood hit Outcome unattractiveness
Scale from factor analysis
the wording from Collections 3 and 4). For dependent 7
variables, participants answered on a 10 point scale
6
(1 equaled “not at all” and 10 equaled “extremely”),
about the extent to which they felt worried, anxious, 5
vulnerable, distressed, dread, safe, and protected, and
whether the situation before evacuating was risky. 4
They also answered on a 10 point scale (1 equaled
3
“not at all” and 10 equaled “very much”) how much
they agreed with the following statements: the dam- 2
age will be bad, I could experience much harm, the Control Resilient NM Vulnerable NM
condition
damage will not be a big deal, my chances of being
hit are good, and I will likely suffer damage. Finally,
they were asked whether or not they would evacuate. Following James and Brett (1984), we assessed
Analysis and Results. We used a factor analysis (1) whether the mediators are a probabilistic func-
with varimax rotation to examine the associations tion of the independent variables, (2) whether the
people had with the hurricane warning and found dependent variable is a probabilistic function of the
three factors: (1) estimations of probability (of being independent variable, (3) whether the dependent vari-
hit, chances of being hit are good and I will likely able is a probabilistic function of the mediators, and
suffer damage; alpha = 0081); (2) estimations of out- (4) how the addition of the independent variable to
come (un)attractiveness (damage will be bad, harm step 3 changes the variance explained in the depen-
will be incurred, and damage will be no big deal dent variables. Full mediation occurs when the addi-
(reverse coded); alpha = 0086); and (3) perceptions of tion of the independent variables does not explain
any additional variance in the dependent variables
risk versus safety (worried, anxious, vulnerable, dis-
beyond what the mediators explained (i.e., the change
tressed, dread, risky, safe (reverse coded), and pro-
in R2 from step 3 to step 4 is not significant). If the
tected (reverse coded); alpha = 0093). Three scales
change in R2 from step 3 to 4 is significant, then par-
(probability of hit, outcome unattractiveness, and per-
tial mediation is a possibility.
ceived risk) were created for each factor averaging the
Step 1 was accomplished with the MANOVA
above described items, and were subject to a multi-
detailed above (again see Figure 2). For step 2, binary
variate analysis of variance (MANOVA) with condi-
logistic regression showed that evacuation decisions
tion (control, resilient near miss, and vulnerable near were significantly influenced by resilient near-miss
miss) as the independent variable. experiences (Table 3, model 1). For step 3, binary
The multivariate F was significant (Wilks Lambda logistic regression showed that evacuation decisions
F461 4625 = 2023, p < 0005), as were the univariate F val- were significantly influenced by perceived risk and
ues for perceived risk (F411 2335 = 3056, p = 0003) and outcome unattractiveness (Table 3, model 2). For
outcome unattractiveness (F411 2335 = 3004, p = 0005). The
means for the three scales by condition are plotted
Table 3 Study 3—Binary Logistic Regressions on Evacuation Behavior
in Figure 2. Planned contrasts (using Tukey’s hon-
est significant difference) showed that for perceived Model 1 Model 2 Model 3
risk, the significant difference across conditions was Odds ratio Odds ratio Odds ratio
driven by the resilient near-miss condition being sig- Independent variablea
nificantly lower than the vulnerable near-miss condi- Dummy resilient near miss 0056∗ 0065
tion (p < 0005), and marginally lower than the control Dummy vulnerable near miss 0067 0055
(p = 001). For outcome unattractiveness, the signifi- Mediators
cant difference across conditions was again driven by perceived risk (R) 1028∗∗ 1027∗∗
outcome unattractiveness (O) 1013+ 1014+
the resilient near-miss condition being significantly probability of hit (P ) 1001 1003
lower than the vulnerable near-miss condition (p <
Nagelkerke R2 0002∗ 0012∗∗ 0013∗∗
0005). Probability of hit was not significantly differ- Change in R2 0001
ent across conditions. To test for mediation we used (from models 2 to 3)
binary logistic regression on whether or not people a
For the condition variable, the control was chosen as the reference cat-
chose to evacuate. Dummy variables were created for egory; thus the regression weights for the first row, for example, show the
the resilient near-miss and vulnerable near-miss con- effect for having resilient near-miss information.
+
ditions, using control as the referent category. p < 0007; ∗ p < 0005; ∗∗ p < 0001.Tinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
8 Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS
step 4, when the condition variables were added to might produce more downward counterfactuals than
model 2 as additional explanatory variables, these resilient near misses, which might impel the protec-
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condition variables did not contribute any unique tive action we see participants take in this condition.
additional explanatory power (Table 3, model 3). The This might be particularly true if the severity of the
nonsignificant betas in model 3 for the condition vari- negative consequences in the near-miss information is
ables and lack of any measurable change in R2 sug- high (that someone could have been severely injured
gest full mediation. Near-miss information influences or even killed) rather than low (that someone could
perceptions about risk and consequences, which influ- have been inconvenienced). Thus, we test for counter-
ence whether or not participants evacuated in the face factual thinking in this study, and we vary the sever-
of a hurricane warning. ity of the negative consequences (high versus low).
Discussion. Study 3 found that near-miss informa- Another limitation of Studies 2 and 3 is that people
tion influenced the associations people had with the may be legitimately updating their beliefs about the
hazard, which mediated people’s subsequent mitiga- resilience of their house using Bayesian logic; that is,
tion behavior. The resilient near miss tends to be asso- perhaps people are processing the near-miss experi-
ciated with lower risk, which explains the consistent ence as data to recalculate the likelihood of hurricane
lack of protective action by these participants com- damage to their particular house, thereby reducing
pared to those in the other conditions. The vulnerable it from the stated 30%. Moreover, given that their
near misses (highlighting danger albeit to someone house is stationary, each hurricane threat is not truly
else) tend to counteract these reassurances. Study 3 an independent event, and participants could reason-
supports our theoretical model, that near misses are ably infer that their particular house is at less risk
stimuli that influence the general hazard category than what was previously calculated.
(Kahneman and Miller 1986) so that assessments of In Study 4, we asked the person to decide whether
risk are either raised or lowered (depending on type or not to go on a Caribbean cruise that is threat-
of near miss) to influence behavior. However, there ened to be interrupted by a hurricane. The survival
are still several alternative explanations for our find- of a prior cruise ship during a Caribbean hurricane is
ings that need to be examined, such as near misses completely independent from the chances of survival
encouraging counterfactual thought or prompting of their current Caribbean cruise ship given the con-
legitimate Bayesian updating. Our next studies test stantly changing ship location. We nonetheless tested
these alternatives, and in doing so demonstrate that for likelihood updating by asking participants what
our behavioral results generalize more broadly. they believe to be the percentage chance of being hit
by a storm. We also vary the severity descriptions
Study 4 of the possible consequences from high (warning of
An alternative to our proposed mechanism (that near severe injury or even death) to low (inconvenience)
misses implicitly influences the knowledge associated and explore the role of counterfactuals in their deci-
with the hazard category) is that near misses prompt sion process.
counterfactual thoughts. A counterfactual is an alter- Participants and Procedure. For Study 4, we col-
native to reality. Thus a counterfactual thought is lected data from 299 undergraduate business students
thinking explicitly about what could, should, or might who completed a number of exercises online, of which
have been (Kahneman and Tversky 1982). Upward ours was one, in return for class participation points.
counterfactuals are thoughts about how an alternative Participants read that they had nonrefundable tickets
could be better than the realized outcome; downward for a Caribbean cruise that is leaving the next day, but
counterfactuals are thoughts about how an alternative the National Weather Service is currently tracking a
could be worse than the realized outcome (Roese and hurricane in the Caribbean that they estimate has a
Olson 1995). Counterfactual thought is more likely to 30% chance of impacting the cruise. They were also
occur when people encounter a surprise outcome than provided costs associated with not going on the trip
when they encounter a routine outcome (Kahneman and with going on the trip if a hurricane diverts the
and Miller 1986, Miller et al. 1989), or when activated ship. The participant then decided whether or not to
by a problem that needs to be addressed (such as a go on the trip. For the full text of the exercise, see
bad outcome that someone wishes to avoid) (Epstude Appendix B.
and Roese 2008). Thus, if near misses either surprise Variables. Five conditions made up the indepen-
participants (as in the resilient near miss, that dan- dent variables: control and resilient near miss ver-
ger was avoided) or are represented as a problem sus vulnerable near miss crossed by strong versus
(as in the vulnerable near miss, that danger almost weak prime (see Appendix B for specific wording).
happened), one could argue that they evoke coun- To briefly summarize, in the control condition, partic-
terfactual thinking, and that this guides the mitiga- ipants were told that cruises can be diverted because
tion behavior. For example, vulnerable near misses of hurricanes but receive no information about priorTinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS 9
cruises being impacted by hurricanes. In the resilient conditions and compared these to the resilient near-
conditions, participants read that cruises can be miss conditions. Significantly more people with vul-
posted on any other website, including the author’s site. Please send any questions regarding this policy to permissions@informs.org.
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diverted by hurricanes but that they have been on nerable near-miss information were willing to forgo
three prior cruises and never experienced any prob- the cruise than people with resilient near-miss infor-
lems. In the vulnerable conditions, participants read mation ( 2 415 = 8014, p < 0001), supporting H2.
that cruises can be diverted by hurricanes but that To test whether severity of consequences had any
they have been on three prior cruises and never expe- influence on people’s travel decisions, we compared
rienced any problems; however, they know someone the weak versus strong severity within each type
else who has. In the weak conditions, participants of near miss. Although participants whose near-miss
were reminded that hurricane diversions can cause experiences included severe (strong) consequences
delay and cost money, and in the strong conditions were slightly less likely to go on the cruise than par-
participants were reminded that in addition to delays ticipants whose near-miss experiences included weak
and costs, people can be injured or even killed. consequences (50.8% versus 58.5% in the resilient
For the dependent variables, participants answered near-miss condition; 32.8% versus 39.0% in the vul-
whether or not they would go on the trip. Then, par- nerable near-miss condition), neither of these differ-
ticipants were asked: “Please answer the following ences were statistically significant (resilient, 2 415 =
statements as thoroughly as possible. ‘In making this 0067, p > 001; vulnerable, 2 415 = 00501 p > 001). Thus,
decision, I thought about if 0 0 0 1’ ‘I also thought about for the following analyses, we collapse data across the
if 0 0 0 1’ and ‘I also thought about if. 0 0 0’ ” Participants severity conditions.
then rated their belief that a hurricane would impact To test whether people with near misses are updat-
their ship (from 0%–100%). ing their calculation of the likelihood of harm, we
ran an ANOVA on participants’ belief that a hurri-
The open-ended responses were coded by two
cane would impact their ship (from 0%–100%). Across
research assistants blind to conditions and hypothe-
all conditions, participants slightly inflated their belief
ses. They coded whether the statement contained an
that a hurricane would impact their ship from the
upward counterfactual (e.g., “If I go, I will have the
given 30% (control mean, 35%; s.d., 18; resilient mean,
time of my life”), a downward counterfactual (e.g.,
33%; s.d., 17; vulnerable mean, 35%; s.d., 17), but there
“If I were to get diverted on the cruise, I would miss
were no significant differences across the conditions
work”), a neutral counterfactual (e.g., “If I could sell
(F421 2895 = 0032, p > 001).
the cruise tickets to another party”), or no counterfac-
To test whether near misses prompt counterfactual
tual (e.g., “$2,000 is a sunk cost and I should not make
thoughts, we looked at whether different near-miss
my decision based on it”) (Nasco and Marsh 1999). experiences produce different types of counterfactual
Analysis and Results. Results for participants’ thoughts. Figure 4 shows the percentage of partic-
decisions (to forgo the trip or not) are shown in ipants’ counterfactual thoughts by condition. Most
Figure 3. To test H1, we collapsed the two resilient of participants’ responses contained a downward
near-miss conditions and compared them to the no counterfactual thought, followed by no counterfac-
near-miss experience (control). Significantly fewer tual thought. Importantly, however, there were no
people with resilient near-miss information were will- systematic differences in types of thought across con-
ing to forgo the cruise than people without near-miss ditions ( 2 465 = 60451 p > 001). Thus, the explana-
information ( 2 415 = 5099, p < 0005) supporting H1. tion that near misses evoke a particular counterfactual
To test H2, we collapsed the two vulnerable near-miss thought to explain the observed differences in mitiga-
tion behavior fails the first test requirement to demon-
strate mediation (James and Brett 1984).
Figure 3 Study 4—Percentage of Participants Forgoing the Cruise,
by Condition
Figure 4 Study 4—Percentage of Participants’ Counterfactual (CF)
Percentage forgoing cruise
0.8
Thoughts, by Condition
0.7
Percentage of responses
0.6 0.6
0.5 Control
0.5
0.4 Resilient near miss
0.4
0.3 Vulnerable near miss
0.3
0.2
0.1 0.2
0 0.1
Resilient Resilient Vulnerable Vulnerable Control
0
NM weak NM strong NM weak NM strong
No Downward CF Neutral CF Upward CF
Note. NM, near miss. counterfactualTinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
10 Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS
Figure 5 Study 4—Percentage of Counterfactual (CF) Thoughts, responses (the mediators) occurred in the same exper-
by Cruise Decision imental period. Thus, there is the possibility that there
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0.6 is some reverse causality operating between how the
Percentage of responses
Forgo cruise hazard is described and the person’s decision; that is,
0.5 Go on cruise
one could decide to engage in a behavior and then use
0.4
that decision to shape how they characterize the situ-
0.3 ation, or these could coevolve. To discount this alter-
0.2 native, we examined the thoughts people had about a
0.1 hazardous situation (encoded under our different con-
ditions of near-miss information) absent any decision
0
No Downward CF Neutral CF Upward CF about what to do. By removing the need to make a
counterfactual choice, we removed any potential that the assessment
of the situation was based on the desire to justify a
particular decision. This task has the additional bene-
To discount the possibility that we miscoded
fit of providing evidence that the biasing effect of near
the various types of counterfactual thoughts, we
misses precedes the construction of an SEU evaluation
tested whether the different counterfactual thoughts
(something assumed via our theory but not tested in
as coded were associated with different mitigation
our context). We have argued that near-miss informa-
behaviors in reasonable ways and found that they
tion changes the valence of the knowledge associated
were. Figure 5 graphs the percentages of partici-
with a type of hazard; if that is so, then we should
pants’ counterfactual thoughts by cruise decision and
expect to see different kinds of thoughts retrieved
shows that counterfactual thoughts do influence the
depending on type of near miss presented.
decision. As would be expected, decisions to go
In Study 5, we gave participants a fictitious news-
on the cruise were associated with more upward
paper article to read about cruises during hurricane
counterfactual thoughts (upward counterfactuals ver- season and then asked them to describe thoughts and
sus other counterfactuals, 2 415 = 2700, p < 00001), feelings associated with the general category “cruises
whereas decisions to forgo the cruise were associated during hurricane season.” The task resembles Study 4,
with downward counterfactual thoughts (downward except that we removed the decision. We expected
counterfactuals versus other counterfactuals, 2 415 = resilient near misses to be associated with more
809, p < 0001). In sum, counterfactual thoughts, once positively valenced thoughts, and vulnerable near
evoked, can produce systematic differences in mitiga- misses to be associated with more negatively valenced
tion behavior, yet near misses do not systematically thoughts. We did not make predictions about spe-
activate any particular type of counterfactual thought. cific feelings (like harm, because the person reading a
Therefore, counterfactual thoughts do not provide a newspaper article has no reason to feel any danger) or
compelling explanation for why near misses influence beliefs (e.g., hurricanes will cause damage), but rather
mitigation decisions. tested changes in the overall evaluations of the sit-
Discussion. We showed that even when the situa- uation (which should be guided by the information
tion does not support updating one’s beliefs (because associated with the hurricane category).
the interaction of hurricanes and Caribbean cruises Participants and Procedure. For Study 5, we col-
are independent events), the near-miss effect still lected data from 229 undergraduate business students
operates. People who experience resilient near misses who completed a number of exercises online, of which
are more likely to ignore a hurricane warning and go ours was one, in return for class participation points.
on the cruise, whereas people who experience vulner- Participants read a news story about how Caribbean
able near misses are more likely to choose the miti- cruises are deeply discounted in October and Novem-
gation behavior, forgoing the cruise. We also showed ber because of hurricane season. The story closes by
that people with near-miss information are not revis- stating that the national weather service is tracking a
ing their calculations of the likelihood of the hazard in hurricane that could impact the ship that a fictitious
ways that might explain either their decision to go on Bill Thompson is currently boarding. For the full text
or to forgo the cruise. Finally, we showed that while of the exercise, see Appendix C.
counterfactuals are related to the ultimate choice peo- Variables. For the independent variable, we used
ple make, counterfactual thinking is not predicted by same five conditions for this study as for Study 4:
a near-miss experience. control, resilient near miss (strong), resilient near miss
(weak), vulnerable near miss (strong), and vulnera-
Study 5 ble near miss (weak). See Appendix C for the specific
A potential concern with the meditational analysis of wording. After reading the news article, participants
Study 3 is that peoples’ decisions and their survey wrote their general thoughts about whether or notTinsley, Dillon, and Cronin: How Near-Miss Events Amplify or Attenuate Risky Decision Making
Management Science, Articles in Advance, pp. 1–18, © 2012 INFORMS 11
they thought fall cruises were a good idea. Partici- Table 4 Counts of Each Thought Type Based on Topic, Valence, and
pants also gave two ratings on a 1–5 scale: (1) their Near-Miss Condition
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general impression of cruises and (2) whether or not Near-miss type
they thought that Bill Thompson (who loves cruising
during hurricane season) has the correct attitude. Thought No
type near miss Resilient Vulnerable
Written responses were unitized into thoughts
(subject–verb–object). Thus, a thought could be a sen- Fun Valence
tence (e.g., “Hurricanes are a big scare” has one unit). Negative
Count 11 17 30
There could also be multiple units in a sentence (e.g.,
% within near miss 4400 3708 6308
“You save money and have fun” has the two objects
Positive
and verbs with the same subject). These thoughts Count 14 28 17
were content coded by a research assistant blind to the % within near miss 5600 6202 3602
hypotheses based on issues people seemed to think Safety Valence
about when deciding about the cruise in the prior Negative
counterfactual study. The five basic issues are fun Count 7 13 23
(thoughts about how enjoyable or not the experience % within near miss 7708 7605 7607
Positive
would be; e.g., “it is not as crowded”), harm (thoughts
Count 2 4 7
about safety and getting hurt personally; e.g., “there % within near miss 2202 2305 2303
is little risk of injury”), monetary value (thoughts about Value Valence
the cost/benefit ratio of the cruise; e.g., “it’s a good Negative
value”), the likelihood of problems (thoughts about prob- Count 6 10 15
ability with respect to adverse events; e.g., “some- % within near miss 3000 2202 3805
thing could always go wrong”), and risk acceptance Positive
Count 14 35 24
(thoughts about whether, in general, the risk/reward % within near miss 7000 7708 6105
trade-off makes sense; e.g., “why put yourself at
Probability Valence
risk?”). Thoughts were also coded for whether it was Negative
attitudinally positive or negative; thus, in the same Count 4 3 2
category of thought about the likelihood of problems, % within near miss 10000 2000 1403
a statement could be for (e.g., “I don’t mind the risk”) Positive
Count 0 12 12
or against (e.g., “I would not want to take the risk”)
% within near miss 000 8000 8507
a cruise in hurricane season.
Risk Valence
Analysis and Results. We first looked to see how Negative
our 10 codes (five topics by two valences) differed Count 15 24 20
across conditions. As in Study 4, severity of conse- % within near miss 6802 7006 8303
quences had little effect. Only one of the 10 codes Positive
(specifically, monetary value negatively valenced) Count 7 10 4
% within near miss 3108 2904 1607
reached significance, in that participants who read
Total
about a strong consequence (people could have died)
Count 80 156 154
were more likely to generate negative monetary value % within near miss 10000 10000 10000
thoughts (e.g., this cruise would not be a good value)
than participants who read about a weak consequence
(people could be inconvenienced or injured; p < 0001). marginally significantly more negative-value-related
Given the general similarities in people’s thought pat- thoughts in the vulnerable near-miss condition than
terns across strong versus weak consequences, we in the resilient near-miss condition ( 2 415 = 2065,
collapsed across these conditions and looked at the p < 001, one tailed). Finally, they generated more
influence of near-miss type (resilient versus vulnera- negative- risk-related thoughts in the vulnerable near-
ble versus control). miss condition than in the resilient near-miss condi-
Table 4 shows the raw counts of each thought tion, although these differences were not significant.
type based on topic, valence, and near-miss condition. We further tested whether near-miss information
The overall 2 (2) for the table was significant at 7.69 and severity affected the thoughts people had by run-
(p = 0002), and this significance was primarily driven ning a logistic regression using contrast coding for
by different valence of thought across the near-miss conditions (Judd and McClelland 1989). As Table 5
conditions. Participants generated significantly more shows, the type of near miss affects the valence of the
negative-fun-related thoughts in the vulnerable near- thoughts associated with cruises; resilient near misses
miss condition than in the resilient near-miss con- decrease the number of negative thoughts about
dition ( 2 415 = 6024, p < 0005). They also generated cruises, as well as increasing the ratio of positiveYou can also read
