Since Ellsberg (1961), there have been important research developments in the economic and decision sciences literature on the impact that ambiguity—that is, uncertainty about probability—can have on how individuals make their decisions (Camerer and Weber 1992). Recent papers demonstrate a growing interest in better understanding how ambiguity affects choices in the experimental literature on decision-making (Viscusi and Magat 1992; Du and Budescu 2005; Hey et al. 2010; Rubaltelli et al. 2010), the formal decision science literature (Abdellaoui et al. 2011; Gajdos et al. 2008; Ghirardato and Marinacci 2002; Klibanoff et al. 2005; Machina 2009; Mukerji 2003; Neilson 2010; Seo 2009; Snow 2010), and neuro-economics (Chew et al. 2008; Levy et al. 2010).

This research reveals that attitudes toward ambiguity are more complex than originally conjectured by Ellsberg (1961) and that the domain of outcomes (loss or gain) and the level of probability influence individuals’ choices under ambiguity (Camerer and Weber 1992; Hogarth and Einhorn 1990; Viscusi and Chesson 1999). In the domain of insurance on which this paper focuses, previous surveys of underwriters and actuaries indicate that insurers are ambiguity-averse for low-probability, high-consequence negative events. In other words, they will want to charge higher premiums when there is ambiguity than when the probabilities and losses are well-specified (Kunreuther et al. 1995; Cabantous 2007).

What is less known, however, is whether the nature of the ambiguity also matters. Research on decision-making under uncertainty has recently opened this “black box” in order to study the impact of various sources of uncertainty on choices. For instance, several empirical papers have focused on the impact of a specific type of ambiguity, namely disagreement or conflict among experts (Baillon et al. 2011; Budescu et al. 2003; Cabantous 2007; Cameron 2005; Dean and Shepherd 2007; Smithson 1999; Viscusi 1997; Viscusi and Chesson 1999). This research reveals that when seeking advice from multiple advisors, individuals are sensitive to whether these experts agree or disagree with each other with respect to a specific forecast and/or in their recommendations for actions.Footnote 1

This paper builds on this emerging literature to investigate the effects of the two different contexts of ambiguity on insurance pricing decisions by sophisticated agents (insurers): imprecise (but consensual) ambiguity and conflict ambiguity. To illustrate these two conditions, assume that two advisors, A1 and A2, are asked to provide estimates about the probability of a given scenario, for instance a Category 3 hurricane hitting the city of New Orleans in the next 50 years. Under a risk situation, they both agree that the probability is, say, 1/2 so there is consensus on a precise probability. Formally, negative risky prospects with two outcomes yield outcome x with probability p and outcome y (with 0 ≥ y ≥ x) with probability (1 − p).

Now let us discuss the following two contexts of ambiguity. The first one occurs when the two advisors A1 and A2 end up with a probability interval rather than a precise estimate. Furthermore, their two intervals are identical. For instance, they both think the probability interval is [1/4; 3/4]. This is a case of consensus, but where there is an imprecise estimate. We call such a situation imprecise ambiguity. Formally, imprecise ambiguity prospects give x with a probability that belongs to the interval [p − r, p + r] with r ≤ p ≤ 1 − r and y (with 0 ≥ y ≥ x) otherwise. Conflict ambiguity occurs when both advisors A1 and A2 provide a precise point estimate but the two probabilities differ from each other (this could also be two different ranges of probabilities, but we will not discuss this case in this paper). For instance, A1 strongly believes that the hurricane will occur with probability 1/4 and A2 strongly believes it will happen with a probability 3/4.Footnote 2 Formally, conflict ambiguity prospects give x with a probability of either (p − r) or (p + r) and y (with 0 ≥ y ≥ x) otherwise (r is fixed and strictly positive). Table 1 summarizes these three cases for the above example.

Table 1 Differences between risk, imprecise ambiguity and conflict ambiguity
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In this paper, we compare insurance pricing in these three contexts in which information structure differs. Our focus is on insurers confronted with events that have low probabilities but which can generate catastrophic losses. We study decision contexts where actuaries and underwriters in insurance companies seek advice and request probability forecasts from different groups of experts. Are the insurance prices that insurers would like to charge different under these three contexts? Is imprecise knowledge better than conflicting expertise in that insurers will ask a lower price for the former than they would for the latter?

We are also interested in the effect of these two sources of ambiguity on cognition. To understand how cognition impacts attitudes towards ambiguous risks and actual choices we use insights from attribution theory (Hilton and Slugoski 1986; Hilton et al. 1995). Although several authors have highlighted the role of attributional explanations in attitudes toward ambiguity (Einhorn and Hogarth 1985; Heath and Tversky 1991; Taylor 1995), to our knowledge no study has explored how causal attribution for analysts’ expressions of uncertainty (consensual or conflicting) is utilized by expert insurers to make decisions.

Our experiment shows that risk professionals (here, insurers) behave differently when the probability of the loss is well specified (risk), versus under different types of ambiguity. Specifically, we find that insurers charge higher premiums when faced with ambiguity than when faced with risk. Across three hazards (floods, hurricanes, house fires), we find that on average, insurers report that for a one-year contract for ambiguous damages, they would charge premiums between 25 percent and 30 percent higher than the premiums they would charge for risky damages. Furthermore, they would likely charge more for conflict ambiguity than imprecise ambiguity for flood and hurricane hazards, but less in the case of fire, probably because they see much less ambiguity in probabilities concerning typical house fires. Normally, they have considerable data on this risk so the probability is well-specified. We also find that the type of ambiguity impacts on the nature of the causal inferences insurers make to reduce their uncertainty.

1 Predictions and literature review

In this section we specify a set of hypotheses (H) and provide support for each of them by reviewing the relevant literature.

1.1 Insurers are ambiguity-averse for low-probability, high-consequence events (H1)

If insurers are averse to ambiguity with respect to low-probability, high-consequence events whose occurrence is external to the insurers’ actions, they will want to charge higher premiums when there is uncertainty about the probability of a loss than when the probability is well-specified. This prediction is consistent with past studies on ambiguity avoidance (Camerer and Weber 1992; Hogarth and Einhorn 1990; Viscusi and Chesson 1999), including studies of how insurance underwriters and actuaries make decisions about the price they will charge for providing insurance coverage. Kunreuther et al. (1995) show that underwriters report they would charge higher premiums to insure against damages with ambiguous probabilities than for damages with precisely-known probabilities (see also Hogarth and Kunreuther 1989; Cabantous 2007).

An explanation for this ambiguity aversion is that individuals avoid situations where they do not have information they think others might have (Frisch and Baron 1988). In a similar vein, Heath and Tversky (1991) show that ambiguity avoidance comes from a “feeling of incompetence” when decision-makers perceive that they have insufficient knowledge about a specific event. Below, we use models of attribution to explore the kinds of inferences insurers make by proposing an extension and test of Smithson’s (1999) cognitive explanation of conflict aversion. Attribution theory has been applied to understanding how people cope with uncertainty (e.g., McClure et al. 2001) but few studies have used it to understand people’s attitudes toward ambiguity. Heath and Tversky (1991) and Taylor (1995), for example, link ambiguity aversion to attributions of credit and blame, but they do not study the causal attributions individuals make when they face uncertain events.

1.2 Insurers prefer imprecise ambiguity over conflict ambiguity (H2)

Our second hypothesis is that insurers will want to charge a higher premium under conflict ambiguity than under imprecise ambiguity. Smithson (1999) shows that the preference for imprecise ambiguity over conflict ambiguity comes from a cognitive heuristic that leads decision-makers to think that conflicting advisors are less credible and trustworthy than consensual (yet imprecise) advisors. This prediction is also consistent with Cabantous (2007) which studies conflict aversion of French actuaries. One of the reasons that insurers would prefer imprecise ambiguity over situations of conflict ambiguity is that conflict is likely to be seen as an indicator of lack of competence on the part of at least one of the advisors. This leads us to want to test two other hypotheses, H3 and H4.

1.3 Insurers normally expect convergent and precise estimates from their advisors (H3)

Attribution theory has shown that people often make causal inferences by contrasting the current situation to their “world knowledge about the normal state of affairs holding in the world” (Hilton and Slugoski 1986). This means that individuals are more likely to engage in attributional thinking when a situation departs from what they expected to face (Weiner 1985). We expect that insurers are accustomed to the standard case where relevant actuarial data exists on the event they cover, and that two expert advisors would most likely agree on a point prediction. This is known as the “experts-should-converge” hypothesis (Shanteau 2001). Consequently, they will find both kinds of ambiguity in predictions less normal than the standard risk case of perfect convergence of precise estimates.

1.4 Insurers will attribute conflicting estimates to less credible and trustworthy advisors (internal factors) but consensual imprecision to task difficulty (external factors) (H4)

In the framework of classic attribution theory, an event is said to be “explained” when individuals have identified a characteristic of some involved person (internal factor), situation or occasion (external factors) which has produced it (Kelley 1973). Attributing an event to factors of some person, situation or occasion depends on the configuration of consensus, distinctiveness and consistency of information available (see Hilton 2007 for a review).

Applying standard attributional logic to the case of insurance professionals results in the following predictions. First, in the case of conflicting advice from experts, the low consensus between experts will prompt the attributional inference that at least one of the advisors is wrong and is thus perceived as being “incompetent” (Hilton et al. 1995). This is precisely the basis for testing the first prediction of hypothesis H4, which states that under conflict ambiguity, the responders will attribute the conflicting forecasts to the incompetence of (at least one of) their advisors as compared to the standard risk case where the experts’ point predictions converge. The second prediction of H4 is that in the case of imprecise ambiguity, compared to the standard risk case, insurers are more likely to attribute the ambiguity not to incompetence, but to an external effect such as the difficulty of the judgmental task. This is because the high consensus between expert advisors implies that insurers, who receive imprecise but consensual forecasts, are more likely to identify something unusual about the task in question, such as the inherent difficulty of modeling catastrophic risks for which reliable large-scale actuarial data might not exist.

2 An experiment studying U.S. underwriters’ and actuaries’ behavior under risk, imprecise ambiguity, and conflict ambiguity

We tested these predictions in an experiment using a non-standard participant pool (insurers who are experts in decision-making under uncertainty) with a field context (insurance pricing task) involving three hazards (flood, hurricane, fire). Specifically, we created a web-based questionnaire asking insurers what premiums they would charge a representative client under different situations of uncertainty (namely risk, imprecise ambiguity and conflict ambiguity) and their causal understanding of the situation (i.e., reasons why the probability is not well specified by the experts to whom they have turned for advice).

2.1 Stimulus

The three different kinds of hazards were crossed with three sources of uncertainty: risk, imprecise ambiguity and conflict ambiguity, leading to nine possible scenarios. The responders were given probability estimates from two different risk modeling companies (“advisors” hereafter) to estimate the probability of each one of these three hazards.

As discussed in the introduction, in the case of risk, both advisors agreed on the same probability. In the imprecise ambiguity case, neither of the advisors provided a precise probability estimate but both converged on the exact same range of probabilities. In the conflict ambiguity case, each advisor provided a point estimate of the probability of the pre-defined damage and amount of insurance claims, but the two likelihood estimates were different. Table 2 depicts the scenarios utilized in the experiment.

Table 2 Scenarios: The three sources of uncertainty
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These scenarios are similar to the ones used in previous studies on insurers’ attitudes toward ambiguity (Cabantous 2007; De Marcellis 2000; Kunreuther et al. 1995). All the insurers who participated in the experiment were asked to imagine that they were employed by an insurance company that “provides coverage to 1,000 homeowners in an area that has the possibility of [flood/hurricane/fire] damage.” They were also told that “The value of each home in this area is $200,000. If a [flood/hurricane/fire] occurs and severely damages a home it will cause $100,000 in insurance claims (above the deductible).” It is therefore known that the amount of the payment the insurance company will have to make if the event occurs is $100,000 per house. In the case of flood coverage, which is provided in the United States mainly by the government-run National Flood Insurance Program, we also told the insurers to “Imagine that the current federal National Flood Insurance Program (NFIP) no longer exists and that flood insurance is offered to homeowners in the private market.”Footnote 3 In this context, their company would also be paying for losses associated with the flood scenario.

Insurers who participated in the survey were asked to base their estimates of the probability of damage on the figures provided by their advisors, the two modeling firms with whom they usually work.Footnote 4 The probability of damage was set at 1 percent in the risky case, and the range between 0.5 percent and 2 percent in the imprecise ambiguity case. The probability estimate in the risky context was thus the geometric mean of the two bounds of the probability range. In the conflict ambiguity case, one risk-modeling firm estimated that the probability of the damage was 0.5 percent whereas the other estimated it was 2 percent.Footnote 5 Figure 1 provides a graphical representation of the three cases.

Fig. 1
figure 1

Graphical representation of experts’ judgments in the experiment

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2.2 Experiment questions

As we are interested in pricing behavior (see H1 and H2), we asked participants to provide the “pure premiums” they would charge. These pure premiums exclude the other costs the insurance company would incur and want to pass on to its policyholders, such as administrative and marketing costs, loss assessment costs and the opportunity cost associated with capital that insurers need to hold to satisfy rating agencies’ and regulatory solvency requirements. Insurers were asked to indicate the minimum pure premium they would charge to provide a 1-year full insurance coverage contract against the specific untoward event, and the annual premium for a 20-year full insurance coverage contract.

We were interested in how these insurers would react to a multi-year contract because there have been recent proposals to modify insurance contracts in that direction so as to provide more stability to the policyholders over time and reduce administrative cost for the insurer.Footnote 6 Here, multi-year insurance keeps the annual insurance premium the same over a fixed time horizon. To test hypothesis H3 we included a question about insurers’ perceptions of the degree of “unusualness” of the probability estimates that they were given (see question 2 in Appendix 1).

To test hypothesis H4, two causal attributions were linked to the advisors (person causal attribution) and one to the task performed by the advisors (situation causal attribution). One question on causal attribution was positive (“Both modeling firms did their work very well.”; see question 3 in Appendix 1) and another was negative, implying incompetence (“At least one of the modeling firms did not do its work very well.”; see question 4 in Appendix 1). Another question also concerned the perception of the competence of the advisors (question 6: “To what extent do you have the impression that the two modeling firms are both competent in estimating the probability of the [flood/hurricane/fire] damage in this case?”). Question 5 concerned the difficulty of the task: How strongly do you agree with the following statement? “Estimating the probability of the [flood/hurricane/fire] damage in this case is a highly difficult task.”

After the participants had read the three scenarios and completed the series of questions, we collected socio-demographic information (sex, age, training, and experience) and queried about the insurance company they worked for (number of employees, surplus/capital and type of the company). Appendix 1 provides the full list of questions from the web-based questionnaire, and Appendix 2 provides socio-demographics of participants and their company.

2.3 Sampling plan

To reduce the number of scenarios given to each participant, we used a Latin-square design and participants were randomly assigned to three of the nine scenarios. The computer program ensured that each participant was exposed to only one hazard (flood, hurricane or fire) that was associated with only one source of uncertainty (risk, conflict ambiguity or imprecise ambiguity). For example a participant could be exposed to “Fire damage in the conflict ambiguity context,” “Flood damage in the imprecise ambiguity context” and “Hurricane damage in the risky context.” The order of presentation of the scenarios was randomized.

2.4 Insurers participating in the study

The survey was available online on a dedicated website and required a password. Participants in a pilot study reported that the instrument was user-friendly and that the survey did not take them more than fifteen minutes to complete.Footnote 7

All the responders were from insurance companies operating in the United States. Nearly two-thirds of them were actuaries and the rest either underwriters, risk managers, or at other management positions. The computer treatment of the data assured the anonymity of the answers. We obtained 84 responses, four of which were eliminated because the individuals did not fully complete the questionnaire. The number of responses is consistent with other studies.Footnote 8 Of the 80 participants, 58 (72.5%) were males and 22 (22.5%) females. The majority of participants were in their 20s and 30s (27% and 35% respectively); one-fourth were in their 40s (24%) and 14% in their 50s. A majority of answers came from publicly-traded insurers (56%) and mutual insurance companies (34%). More than half of the participants were working for large companies, those with the policyholders’ surplus in the $5 billion and $10 billion range and with the number of employees ranging from 5,000 to 20,000 (see Appendix 2 for more details).

3 Results and discussion

Table 3 reports the geometric meansFootnote 9 and median values of pure premiums for the main experimental conditions. Mean premiums are always higher for all three hazards than the expected loss of $1,000 (i.e., 1% annual chance of losing $100,000). This is consistent with findings from previous studies that show that insurers are risk averse.

Table 3 Geometric mean and median pure premiums in $/Year
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3.1 Ambiguity aversion hypothesis (H1)

To test hypothesis H1, we compared the premiums under risk with those under both types of ambiguity. Table 3 shows that, on average across natural hazards, the mean premiums for 1-year contracts for imprecise ambiguity are 25 percent higher than the mean premiums for risky damages; they are 30 percent higher for conflict ambiguity than for risky damages.Footnote 10 (Median premiums for ambiguous damages under 1-year contracts ranged from 50 percent to 92.5 percent higher than for risky damages). This suggests that insurers are averse to both types of ambiguity.

To formally test this ambiguity aversion we undertook a Multivariate Analysis of Variance (MANOVA) on the log premiums charged for the 1-year and the 20-year contracts, and determined the main effects of each of three fixed factors: Source of Uncertainty, Natural Hazard and Participant ID.Footnote 11 We found that the premiums under imprecise ambiguity are significantly higher than premiums under risk (F = 14.62, p = 0.000 and F = 10.74, p = 0.002 for 1-year contracts and 20-year contracts respectively). In other words, imprecise ambiguity significantly increases the premiums insurers indicated they would charge to insure against the damage. We also found that premiums under conflict ambiguity are significantly higher than premiums under risk (F = 22.45, p = 0.000 and F = 16.29, p = 0.000 for the 1-year and the 20-year contracts respectively). These results indicate that H1 holds, i.e., insurers are indeed averse to both types of ambiguity.

Although we computed our results using the geometric mean, we are also aware that some insurers might have considered using the arithmetic mean of the two expert estimates rather than the geometric mean. We thus also computed the risk premium for each response under risk, imprecise ambiguity and conflict ambiguity (Table 4). In the case of risk, the risk premium (RP) is the difference between the pure premium and the expected loss (i.e., $1,000). For the two types of ambiguity, we calculated the risk premium (RP) as the difference between the pure premium and the arithmetic mean of the expected losses; that is $1,250.

Table 4 Mean and median risk premiums in $/Year
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Table 4 presents the means and medians of the risk premium (RP) distributions, by hazard and by source of uncertainty. We can see that mean risk premiums under imprecise ambiguity and conflict ambiguity are higher than risk premiums under risk when insurers sell the standard one-year contracts. We ran similar statistical analysis on the risk premiums distributions (mean) as we did on the mean log premiums in Table 3. These tests show that risk premiums are significantly higher under imprecise ambiguity than under risk for 1-year contracts but not for 20-year contracts (F = 4.769, p = 0.032 for one-year contracts; F = 2.388, p = 0.126 for 20-year contracts); and under conflict ambiguity than under risk (F = 4.435, p = 0.038 for one-year contracts; F = 1.265, p = 0.264 for 20-year contracts). These results confirm that insurers are ambiguity averse, and that H1 is supported in the standard 1-year contract case.

3.2 Conflict aversion hypothesis (H2)

To test hypothesis H2 we restricted our analysis to the imprecise ambiguity and conflict ambiguity contexts and performed a MANOVA on the log premiums with Source of Uncertainty, Natural Hazard and Participant ID as fixed factors. Looking at all three hazards combined, participants said they would charge premiums between 2.7 percent and 4.5 percent higher under conflict ambiguity than under imprecise ambiguity (for the 20-year and 1-year contracts, respectively; Table 3). This suggests a tendency for conflict aversion, but this difference was not large enough to be statistically significant (F = 0.58, p = 0.45 and F = 0.19, p = 0.66 for 1-year and 20-year contracts respectively) so that H2 was not supported.Footnote 12

We also examined whether insurers assessed the three types of hazard differently. To do so, we ran three MANOVAs (one for each hazard), with Source of Uncertainty as a fixed factor, and asked for simple contrasts in order to compare the premiums charged under imprecise ambiguity with those charged under conflict ambiguity. When the data were disaggregated, we found that, contrary to what we predicted, for the fire hazard, insurers charged smaller premiums under conflict ambiguity than under imprecise ambiguity (8.3 percent and 29.4 percent smaller for the 1-year and 20-year contracts, respectively; Table 3). These contrasts are significant for both the 1-year contracts (p = 0.049) and the 20-year contracts (p = 0.013). For the two other hazards however, we observed the predicted trend. Insurers charged, on average, more under conflict ambiguity than under imprecise ambiguity for flood (8.5 percent and 30.4 percent higher for the 1-year and 20-year contracts, respectively), and for hurricane (13.9 percent and 16.3 percent higher for the 1-year and 20-year contracts, respectively) but none of these contrasts are statistically significant (Table 3). These results suggest that the nature of the hazard matters, even though the expected loss is the same for each one of these three hazard scenarios.

There might be several reasons for this behavior. It might be due to the potential for truly catastrophic losses from hurricanes and floods. Of the twenty-five most costly insured disasters that occurred in the world between 1970 and 2010, twenty-two of them were hurricanes and floods. Moreover, we ran this experiment in early 2008, not long after seven major hurricanes had made landfall in the U.S. in 2004 and 2005, including Hurricane Katrina which inflicted over $150 billion in economic losses, $48 billion of which was borne by private insurers (2008 prices) (Kunreuther and Michel-Kerjan 2009). In contrast, with the exception of large-scale wild fires such as those in Russia during the summer of 2010, events resulting in insurance losses for house fires tend to be relatively small in size.

Another explanation relates to the available data for estimating the likelihood of these three different hazards. After the seven major hurricanes of 2004 and 2005, some risk modeling firms and insurers revised their catastrophe models to reflect a potential increase in climate-related risk. In contrast, insurers typically have a large historical database for house fires from their own claims and from engineering studies to improve building safety.

3.3 Insurers normally expect convergent and precise estimates from their advisors (H3)

We now turn to testing hypothesis H3 which predicts that insurers expect a priori the two risk-modeling firms to provide the same precise probability (normal condition). To test this prediction, we asked the surveyed insurers “To what extent do you have the impression that there is something unusual about the estimates of the probability of the damage you have been given?” (question 2 in Appendix 1). Answers were given on a 7-point scale ranging from −3 “nothing unusual” to +3 “extremely unusual.” This scale captures the degree of “unusualness” of the decision context. We transformed this scale into a 3-point scale ranging from 1 “nothing unusual” (old scores −3, −2, −1), to 2 “neutral” (old score 0), and 3 “something unusual” (old scores +1, +2, +3). Table 5 gives the frequencies of answers (and percentage) to the unusualness question by type of hazards.

Table 5 Distribution of answers (%) to the unusualness question
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We can also look at the results by type of hazards. For each hazard, we ran a series of two-way Chi-square tests to determine whether the distribution of answers to the “unusualness” question under risk was different from the distributions of answers under imprecise ambiguity, and under conflict ambiguity. For fire, we found that these differences were highly significant both for the comparison between risk and imprecise ambiguity (Cramer’s V = 0.428, p = 0.007) and for the comparison between risk and conflict ambiguity (Cramer’s V = 0.576, p = 0.000). Specifically, Table 5 shows that under risk, a large majority of insurers (70 percent) said that there was “nothing unusual” about the estimates of the probability of the damage they were given, whereas only a minority of insurers exposed to the imprecise ambiguity context (28 percent) and to the conflict ambiguity context (20 percent) considered this to be the case. In other words, insurers exposed to the fire scenario said that they were expecting the two risk modeling firms to come up with the same precise probability, as H3 predicts.

For flood, we did not find any significant difference between the distributions of answers to the unusualness question under risk and imprecise ambiguity (Cramer’s V = 0.042, p = 1), and under risk and conflict ambiguity (Cramer’s V = 0.177, p = 0.426). This means that H3 is not supported. Yet, Table 5 shows that the distribution of the perception of unusualness under risk and imprecise ambiguity are highly similar (52 percent, 16 percent and 32 percent; and 50 percent, 19 percent and 31 percent, respectively). Although perceptions of unusualness under risk and conflict ambiguity are not significantly different, Table 5 shows that we observed the expected trend. The proportion of “neutral” answers is similar under risk and conflict ambiguity (16 percent and 21 percent, respectively) but, under risk, a large proportion of insurers (52 percent) considered that there was “nothing unusual” about the probability estimates they were given; whereas under conflict ambiguity, a large proportion of insurers (45 percent) said that there was “something unusual” about the probability estimates. Taken all together, these results suggest that insurers exposed to the flood scenario tended to expect the two modeling firms to come up with the same precise probability estimate (or the same imprecise probability) and did not expect them to disagree on the probability of the damage.

For hurricane, we also found that H3 was not supported by the data. The series of 2-way Chi-square tests showed that the distributions of answers to the unusualness question under risk did not differ from the distributions of answers under imprecise ambiguity (Cramer’s V = 0.199, p = 0.36), and under conflict ambiguity (Cramer’s V = 0.120, p = 0.72).

3.4 Conflict imprecision leads to person attribution whereas imprecise ambiguity leads to task attribution (H4)

Finally, we test the H4 predictions. The abnormal conditions focus model of causal attribution (Hilton and Slugoski 1986) contends that due to low consensus (disagreement between advisors), insurers will attribute conflict ambiguity to the incompetence of their advisors and perceive their advisors to be less credible and trustworthy than in cases of high consensus where the advisors agree. On the other hand, the same causal attribution model contends that when expert advisors provide similar but imprecise estimates of the probability of an event, insurers will attribute the imprecise ambiguity to the difficulty of the task.

Based on this causal attribution model, we made the following two predictions (H4). First, we predicted that under the conflict ambiguity case, the insurers will attribute the uncertainty they face to the incompetence of (at least one of) their advisors compared to the standard risk case. Second, we predicted that in the case of imprecise ambiguity, compared to the standard risk case, insurers will be more likely to attribute the uncertainty they face to the difficulty of the task.

First, to test whether conflict ambiguity generates doubt about the advisors’ competence compared to risk, we focused on the comparison between the two. For each hazard, we ran a series of two-way Chi-square tests, one for each of the two person attribution questions (questions 3 and 4 in Appendix 1) and one for the competence question (question 6 in Appendix 1). Table 6 shows that for all three hazards, as hypothesized, the proportion of insurers considering their advisors to be “competent” was consistently higher in the risk context than in the conflict ambiguity context, as evidenced by the percentages that are systematically higher under risk—100 percent, 88 percent and 66 percent—than under conflict ambiguity—76 percent, 69 percent, 60 percent (scores are given for flood, hurricane and fire, respectively). A statistical test showed that the distribution of answers to the competence question under risk was significantly different from the distribution of answers under conflict ambiguity across the three hazards (Cramer’s V = 0.190, p = 0.025; one-sided test). This effect, however, was mainly due to results from the fire scenario where the difference is significant (Cramer’s V = 0.504, p = 0.000) whereas the effect is not significant for flood (Cramer’s V = 0.178, p = 0.58) and hurricane (Cramer’s V = 0.051, p = 1) scenarios.

Table 6 Distribution of answers (%) to the competence question and attribution questionsa
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We then looked at the Positive Source attribution question. As predicted, across hazards, insurers were more likely to agree with the statement that their advisors “did their work very well” (Positive Source) under risk than under conflict ambiguity (Cramer’s V = 0.222, p = 0.022). This global effect was significant mainly because of the fire scenario, where we found that the distribution of answers to the positive source question under risk was significantly different from the distribution of answers under conflict ambiguity (Cramer’s V = 0.511, p = 0.001). Thus, in the fire scenario, Table 6 shows, for instance, that more insurers agreed with the positive source statement under risk (57.7 percent) than under conflict ambiguity (20 percent). Conversely, more insurers disagreed with the statement under conflict ambiguity (52 percent) than under risk (7.7 percent). In the flood and hurricane scenarios, however, even though a larger number of insurers disagreed with the positive statement under conflict ambiguity than under risk (17.2 percent versus 8 percent for flood; 31 percent versus 24 percent for hurricane), a large proportion of insurers opted for the “neutral” answer under both conflict ambiguity and risk. Statistical tests confirmed that the distributions of answers to the positive source question under risk and under conflict ambiguity were not statistically different (Cramer’s V = 0.175, p = 0.437; Cramer’s V = 0.102, p = 0.786 for flood and hurricane respectively).

When looking at the Negative Source question, across hazards, we found no significant difference between the distributions of answers under risk and conflict ambiguity (Cramer’s V = 0.166, p = 0.115). This aggregated result, however, hides the fact that in the fire scenario, the difference was significant, and was in the predicted direction (Cramer’s V = 0.434, p = 0.007). For instance, Table 6 shows that in the fire scenario, more insurers agreed with the statement that “at least one firm did not do its work well” under conflict ambiguity (44 percent) than under risk (15 percent). In the flood scenario, although Table 6 shows that the distributions of answers exhibit the predicted pattern—more agreement under conflict ambiguity (17 percent) than under risk (4 percent), and more disagreement under risk (48 percent) than under conflict ambiguity (31 percent)—the effect was not significant (Cramer’s V = 0.242, p = 0.244). This might be due to the fact that in this case, a large proportion of insurers were actually “neutral” under both conflict ambiguity (52 percent) and risk (48 percent). In the hurricane scenario, we found no significant difference between the distributions of answers to the negative source attribution question under conflict ambiguity and risk (Cramer’s V = 0.243, p = 0.205).

We then tested for the second part of H4, which predicts that insurers will be more likely to attribute the uncertainty they face to the difficulty of the task under imprecise ambiguity than under risk. To do so, we compared the distributions of answers to the task attribution question under imprecise ambiguity and risk. As predicted, across hazards, we found that the two distributions of answers were different (Cramer’s V = 0.417, p = 0.000). Table 6 shows indeed that more insurers agreed with the statement “estimating the probability is a highly difficult task” under imprecise ambiguity (69 percent, 85 percent, 92 percent for fire, flood and hurricane, respectively) than under risk (38 percent, 8 percent, 83 percent for fire, flood and hurricane, respectively). The difference between the two distributions was significant for fire (Cramer’s V = 0.346, p = 0.041) and for flood (Cramer’s V = 0.836, p = 0.000), but not for hurricane (Cramer’s V = 0.140, p = 0.707).

In summary, both predictions of H4 were supported for the fire scenario, but not for the flood and hurricane scenarios. This raises questions about expert insurers’ differing expectations concerning these scenarios, which we discuss below.

4 Summary and conclusion

Our results provide additional evidence that sophisticated subjects—insurers are experts in decision-making under uncertainty—behave as if they are ambiguity-averse in the loss domain when faced with the task of pricing risks having a low probability of occurrence but potentially catastrophic effects (H1).

Furthermore, our results show that the source of ambiguity can have an important impact on choices. When all hazards are combined, our prediction that insurance professionals would be more concerned with conflict ambiguity than imprecise ambiguity was not confirmed (H2). But when the data were disaggregated, we found that on average, insurers tended to charge higher premiums under conflict ambiguity than under imprecise ambiguity for hazards perceived as potentially catastrophic such as floods and hurricanes, but lower premiums for non-catastrophic hazards such as house fires.

We then asked whether this tendency for aversion to conflict came from a cognitive heuristic that leads individuals to attribute the cause of conflicting uncertainty to the incompetence of their advisors. If they doubted the quality of their advisors’ estimates, they might want to increase the price of coverage by assigning a larger weight to the highest probability estimate from the two advisors. To answer this question, we used attribution theory (Hilton and Slugoski 1986; Hilton et al. 1995). We reasoned that insurers would normally expect risk-modeling firms to be in agreement and to communicate a precise probability (H3).

We found that the risky context was perceived as the most usual context for fire, whereas conflict ambiguity was rated as the most unusual context. For hurricane, imprecise ambiguity was rated the most usual context, and risk the most unusual. We believe this is due to the nature of hurricane assessment which requires one to use climate models to project losses in the future. The choice of different climate models and slightly different assumptions or other elements of the selected model will generate different outcomes. We assumed that insurers will expect consensual and precise probability forecasts from their advisors. We thus predicted that disagreeing advisors will be considered as less credible and competent than advisors converging on the same precise estimate. We also predicted that imprecise but consensual advisors will not be considered as less credible and competent than advisors converging on the same precise estimate, and that imprecise ambiguity will be attributed to the difficulty of the judgmental task (H4). In the fire scenario in particular, we found that insurers indeed perceived the risk modeling firms that provided the estimates as being less competent under conflicting uncertainty than under risk; and that they were more likely to attribute the uncertainty they face to the difficulty of the task under imprecise ambiguity than under risk.

Our data also suggest that expert insurers have strong a priori expectations associated with different kinds of hazards which influence their judgments. Indeed, their responses differ between a hazard where losses are potentially catastrophic (flood and hurricane) and cases where the losses are non-catastrophic (fire) (where the expected loss is the same for each hazard). These systematic differences suggest that future research should address the correspondence between risk and ambiguity domains, availability of actuarial estimates, and insurers’ expectations about risk modelers’ predictions. For example, the expectation that experts should converge to precise point estimates may hold only in cases where there is enough relevant actuarial data. If we assume that the experienced insurers in our sample know that such actuarial data exists for fire but not for flood and hurricane, this could explain why consensus over precise estimates would be seen as a cue to competence only for the fire hazard.

In future research it would also be useful to test whether individuals consider that they are less informed when their advisors exhibit conflict ambiguity than imprecise ambiguity. In other words, one could test whether individuals would treat conflict ambiguity as a form of “epistemic uncertainty” due to lack of knowledge that could be reduced and imprecise ambiguity as “aleatory uncertainty” due to randomness. In the former case, individuals would be better informed if they rely on more competent advisors, whereas in the latter case they could not reduce the uncertainty by simply requesting the estimates of more advisors.