Amateur Decision-Making in Avalanche Terrain with and Without a Decision Aid: a Stated Choice Survey

Nat Hazards DOI 10.1007/s11069-009-9365-4

ORIGINAL PAPER

Amateur decision-making in avalanche terrain with and without a decision aid: a stated choice survey

Pascal Haegeli Æ Wolfgang Haider Æ Margo Longland Æ Ben Beardmore

Received: 6 July 2008 / Accepted: 10 February 2009 Ó Springer Science+Business Media B.V. 2009

Abstract Avalanches pose a serious threat to recreational backcountry travelers in mountainous terrain. This study explores how the three main amateur user groups of avalanche terrain in western Canada (backcountry skiers, out-of-bound skiers, and snowmobile riders) balance recreational goals with safety concerns when choosing backcountry destinations under varying avalanche conditions. Using a discrete choice experiment (DCE), a stated preference technique, the study ﬁrst examines the strengths and weaknesses in the decision process of the three amateur groups by comparing their responses with the choice patterns of professional mountain guides. The results show that the decision-making strategies employed by the respective amateur groups vary considerably in their level of complexity and the degree to which avalanche safety considerations are incorporated. Second, we examine the effects of a decision aid that preprocesses the most crucial pieces of avalanche hazard information on the decision preferences of the amateur groups in the DCE. The results show that a relatively simple decision aid can inﬂuence the decision-making process considerably and steer users towards more avalanche hazard sensitive behaviour.

Keywords Decision-making Avalanche safety Decision aid Discrete choice experiment Choice complexity Backcountry skiing Out-of-bounds skiing Snowmobile riding

1 Introduction

Traditionally, natural hazards have primarily been viewed as phenomena of the geological and biological domains. Related research attempted to enhance the physical understanding about natural hazards to better predict catastrophic events and to improve the managerial ability to control the hazards through technological means. Over the past two decades,

P. Haegeli (&) W. Haider M. Longland B. Beardmore School of Resource and Environmental Management, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada e-mail: [email protected] 123 Nat Hazards however, a paradigm shift has occurred towards a more comprehensive human–environment perspective that also integrates societal and human aspects into the assessment and mitigation of natural hazards, placing considerably more emphasis on social science contributions. Existing social science research in the field of natural hazards primarily focuses on assessing societal vulnerability and examining how interventions at the policy level can effectively decrease it through minimizing people’s exposure to natural hazards and maximizing their adaptive capacity (Haque and Etkin 2007). The study of decision-making by individuals facing natural hazards is a considerably smaller field and primarily focuses on evacuation behaviour prior to hurricanes (Whitehead et al. 2000; Gladwin et al. 2001; Lindell et al. 2005; Whitehead 2005) and wildfires (Mozumder et al. 2008). These studies have used a variety of survey techniques to examine the social, economic and perception factors that affect people’s decision to evacuate. Such research can help emergency planners design effective community evacuation strategies that encourage maximum compliance. Dash and Gladwin (2007) suggest that the next step in evacuation research should focus on developing a better understanding of how individuals process the information they use to assess the safety of their homes and to make evacuation decisions. The goal of the research presented in this article is to study the decision-making process of individuals with respect to avalanche hazard. With 329 avalanche fatalities over the past 30 years (1978–2007) and a recent avalanche fatality rate of 14 fatalities per year, snow avalanches appear to cause the greatest average loss of life due to any winter natural hazard in Canada (Campbell et al. 2007). However, compared to hurricanes or wildfires, the vast majority of avalanche accidents occur when people voluntarily expose themselves to avalanche hazard during recreation (Jamieson and Stethem 2002). Over the past several decades, winter backcountry1 recreation activities have steadily increased in popularity, with the most popular activities being backcountry skiing and snowboarding; out- of-bounds skiing and snowboarding; and snowmobile riding.2 Technical advances in skiing equipment and snowmobile gear allow increased numbers of people to venture farther into the backcountry. The combination of challenging terrain, rapidly changing environmental conditions and remote locations make the winter backcountry a hazardous place where perceptual errors and even minor decision mistakes can have serious consequences. In addition to fatalities, it is estimated that approximately 75 people are injured by avalanches every winter (Bhudak Consultants Ltd. 2003). While non-voluntary exposure to natural hazards is generally controlled through legislation, the mitigation of voluntary exposure to a hazard requires more active participation of the engaged individuals. In order to enable individuals to make appropriate personal choices, mitigation strategies need to focus on awareness education and timely delivery of relevant hazard information. Traditionally, avalanche awareness

1 In this study, the term backcountry refers to mountainous terrain where avalanche hazard is not actively controlled by professional avalanche technicians before recreationists enter the area. 2 Backcountry skiing refers to a type of downhill skiing or snowboarding that is practiced on ungroomed and uncontrolled slopes away from ski areas. Possible means of access to these slopes are climbing skins, snowshoes, snowmobiles or helicopters. Out-of-bounds skiing, on the other hand, describes skiing or snowboarding on ungroomed and uncontrolled slopes outside, but close to ski areas primarily using ski lifts and possibly short hikes to reach the top of the mountain. Snowmobile riders use a small motorized vehicle that is propelled by a rubber track and uses ski-like runners for steering to explore vast areas of mountainous terrain. 123 Nat Hazards education of amateur recreationists in Canada has been based on the assumption that a better understanding of the hazard would lead to better decisions and fewer accidents. Curricula of avalanche awareness courses have therefore focused on improving the recreationists’ scientific understanding of avalanches. Most backcountry recreationists, however, do not spend enough time in the backcountry to internalize the theoretical understanding of avalanches necessary for properly applying a knowledge-based decision approach in real-world situations. In order to address this problem, Munter pioneered a new approach in avalanche awareness education by introducing the Reduction Method (Munter 1997), a simplified decision aid for recreational backcountry skiers. Instead of training skiers to become avalanche forecasters, his goal was to reduce avalanche fatalities by providing users with easy to understand decision rules to help them avoid situations that had led to avalanche accidents in the past. This rule- based approach to avalanche education was quickly adopted in numerous European countries (Larcher 1999; Bolognesi 2000; Engler and Mersch 2000); however, it initially only received limited attention in North America (McCammon 2000, 2002). The tragic winter of 2003, when 29 recreationists perished in avalanches in western Canada, rekindled the North American interest in rule-based decision methods for amateur recreationists. However, considerable differences in backcountry activities and avalanche warning systems between Europe and Canada precluded a direct adoption of an existing decision aid. Detailed knowledge of the target audiences’ existing decision-making capabilities and deficiencies is a prerequisite for the development of an effective decision aid. In this study, we use a discrete choice experiment (DCE; Louviere et al. 2000), a stated preference technique, to examine how amateur recreationists choose backcountry trip destinations under varying avalanche conditions. We are particularly interested in how amateur recreationists interpret avalanche hazard information, how they combine this information with the characteristics of potential destinations and how they balance their recreational goals with avalanche safety concerns. While DCEs are commonly used to elicit choice preferences, the examination of decision skills is a novel application of this method. In order to validate the observed decision patterns, we included a sample of professional mountain guides in the study to elicit a standard for informed decision- making in avalanche terrain. In addition to the baseline assessment, we examine the effect of a hypothetical decision aid on the decision preferences of amateur recreationists. In summary, this research builds on existing research on individual behaviour regarding natural hazards by examining the following questions: a) Can a stated choice approach be used to effectively study the decision-making process of choosing a trip destination under varying avalanche conditions? b) What are the weaknesses in the decision-making process of the three primary amateur backcountry user groups? c) What are the effects of a decision aid that preprocesses the most crucial pieces of information on the decision preferences of the amateur user groups? A review of existing literature on decision-making in avalanche terrain and the stated preference method of DCE is followed by a complete description of the survey with a focus on the DCE and the integration of the decision aid. Next, the results of the amateur and professional surveys are presented and compared. Finally, we discuss the resulting implication on the development of a decision aid for choosing appropriate backcountry trip destinations and some methodological considerations around DCE.

123 Nat Hazards

2 Background

Most fatal avalanches in western Europe and North America are triggered by the victims themselves (McClung and Schaerer 2006), which means that the root cause of many avalanche accidents is a failure in the victim’s perception of the conditions (McClung 2002a) and/or the subsequent decision process. A better understanding of the decision process in avalanche terrain is therefore crucial for the success of future avalanche safety initiatives. While avalanche concerns are naturally at the forefront for avalanche educators, it is essential to view this decision process in its full recreational context. The challenge for recreationists who want to enjoy exciting skiing or snowmobile riding in avalanche terrain is to balance their desire for these activities with avalanche safety concerns. McClung (2002a) describes the goal of backcountry decision-making as maintaining avalanche risk at the level where the enjoyment is maximized, but just below the threshold of someone being caught or injured in an avalanche. Decision-making in avalanche terrain has traditionally been viewed through the eyes of the analytical rational choice paradigm where all relevant information is reviewed in detail and alternatives are weighed according to their pros and cons (McClung 2002a, 2002b). However, the avalanche system is highly complex and in many situations, a fully analytical evaluation would be too cumbersome and unpractical (McCammon 2001). Heuristics and naturalistic decision-making have been proposed as potential alternative decision approaches in avalanche terrain (McCammon 2002; Adams 2005). Heuristics refer to simple rules (Gigerenzer et al. 1999) that focus on a few pieces of key evidence when a comprehensive evaluation of the situation at hand is either impossible due to incomplete information or simply impractical. The theory of naturalistic decision-making (Klein 1998) has been used to examine the apparent ease of avalanche professionals to make effective decisions under time pressure and changing environmental conditions (Adams 2005). Obviously, the approaches to decision-making in such a complex environment are diverse and only a combination of theories will be able to capture the process in its entirety. Past studies on decision-making in avalanche terrain have used avalanche accident records (Atkins 2000; McCammon 2002), personal interviews (Adams 2005) and avalanche awareness surveys (Atkins and McCammon 2004; Pfeiffer and Foley 2006). While these approaches can produce useful insights about perceived issues in decision-making, they are not able to comprehensively capture the difﬁcult trade-off decisions between minimizing avalanche risk and maximizing recreational enjoyment. Backcountry decision- making would ideally be examined by directly monitoring recreationists traveling in avalanche terrain (i.e. revealed preferences), but the dispersed character of backcountry recreation and the high spatial and temporal variability of conditions make this approach difﬁcult.

2.1 Using discrete choice experiments for studying decision-making in avalanche terrain

For the present study, we chose a discrete choice experiment (DCE; Louviere et al. 2000), a stated preference technique, to quantitatively examine the decision process of backcountry travelers. The method of DCE originated in transportation research and has been applied extensively in the ﬁelds of market research and resource economics (Adamowicz et al. 1998). In a DCE, respondents are presented with a series of choice sets composed of two or more alternatives that are described by a common set of attributes. Each attribute is deﬁned by at least two distinct levels, which are varied systematically among choice sets 123 Nat Hazards according to an underlying statistical experimental design plan. For each choice set, the respondent evaluates the alternatives and chooses an option based on personal preferences in the trade-off situation. The theoretical basis for stated preference research lies in random utility theory, which is well documented (see McFadden 1974; Train 2003). The utility (U) gained by person n from experience i consists of an objective or deterministic and observable component (V) and a random, unobservable component (e), which is often referred to as the random error component (Louviere et al. 2000).

Uin ¼ Vin þ ein ð1Þ The fundamental assumption of choice theory is that people act rationally and presented with a choice situation (CSn) consisting of two or more alternatives, an individual n will only choose the alternative that provides the highest overall utility. While the assumption of individual choice behaviour is deterministic, the data can be analyzed stochastically in a standard multinomial logit model (MNL) for the aggregate sample population (McFadden 1974).

Vi Pexp Probi ¼ ð2Þ expVi j2CS The speciﬁc characteristics of the alternatives can be incorporated into the MNL equation (2) by expanding the observable component of the utility, Vi according to

Xk Vin ¼ b0in þ blinflðÞXln ð3Þ l¼1 where blin are the part-worth utility coefficients associated with the functional forms fl of the various attributes Xln that characterize the alternatives. The part-worth utility coefficients b0n and bln for the sample population can be calculated by fitting the expanded version of the choice model equation (2) to the observed aggregate stated choice proba- bilities following the experimental design of the DCE. The resulting part-worth utility values represent the overall importance or contribution of each attribute level to the choices made by the sample population. The stated preference approach has several decisive advantages for studying decision- making in avalanche terrain. The DCE technique allows a more realistic and complete characterization of the multivariate decision task compared to other survey techniques. Through careful study design, it is also possible to circumvent the natural collinearity between many of the relevant decision variables and to explore decision-making across the full range of potential avalanche conditions. As a result, the DCE technique holds promise to examine the most prominent trade-off behaviour of backcountry recreationists more comprehensively than any previous studies. Despite the numerous advantages, there are limitations to this approach, as the hypothetical decision situations in a DCE are unable to fully represent the physical and emotional complexity of real backcountry decision situations.

2.2 Using discrete choice experiments to assess decision-making skill

While the stated preference technique of DCE is commonly used to elicit personal opinions or choice preferences for the purpose of calculating market shares of a future product 123 Nat Hazards

(Louviere et al. 2000), examining policy support (Rasid et al. 2000; Arnberger and Haider 2007) or estimating the consumer surplus for a non-market public good (Adamowicz et al. 1998; Tsuge et al. 2005), the focus of the present study is the assessment of the decision- making process itself. Studies examining decision-making commonly use normative models (see, e.g. Payne et al. 1988) or external validation criteria (see, e.g. Czerlinski et al. 1999) to assess the quality of the decision in question. In this study, however, the complexity of the decision situation and the fact that there are often no obviously right or wrong decisions prevents the application of these approaches. Instead, we use the DCE decision patterns of professional mountain guides as a reference for identifying the strengths and weaknesses of amateur decision-making. While this approach is novel in the context of DCE, it has been used in the field of behavioural psychology (see Klein 2002, for discussion). Although individual decision makers may not always act fully rational in real-world situations, a detailed application of the rational decision approach to hypothetical but realistic backcountry situations can provide powerful insights about their decision skills. Rational choice theory assumes that individuals have the capacity to comprehensively evaluate and compare alternatives regardless of the size and structure of the information presented. However, due to the high complexity of the avalanche system, it is not realistic to assume that all recreationists are able to comprehensively examine all influencing factors. We hypothesize that the ability of backcountry travelers to comprehensively assess avalanche hazard will depend on their level of training and experience. The literature of behavioural economics and human decision science describes two possible mechanisms for how individuals might deal with task complexity that goes beyond their knowledge or cognitive capacity. De Palma et al. (1994) propose a passive bounded rationality model in which participants try to attend to all available information, but simply make more mistakes as the information becomes overwhelming. In a DCE, this coping strategy leads to less consistent choices and therefore increased error terms (DeShazo and Fermo 2002). However, since the behaviour of the sample as a whole still follows complete optimization, part-worth utility values are not expected to change. The alternative rationally adaptive model (Simon 1955; Heiner 1983) hypothesizes that decision makers revert to simpler decision strategies, such as heuristics, once the task reaches a level of complexity that exceeds their cognitive capabilities. Heiner (1983) argues that this could lead to different, but more systematic decision patterns. This approach would manifest itself in a DCE as different sets of part-worth utility coefficients. Various studies (see, e.g. Mazzotta and Opaluch 1995; DeShazo and Fermo 2002; Arentze et al. 2003; Hensher 2006) have examined the effect of choice complexity in DCE by varying the quantity or structure of information presented in an experiment. In our study, we expose all survey participants to the same DCE and use the results of an independent control group of avalanche professionals as a standard for evaluating amateur decision skills. Based on the training and experience of avalanche professionals, it can be assumed that they are able to objectively and completely assess the hypothetical decision situations and choose in a way that maximizes their utility as discussed by McClung (2002a). Differences in part-worth utilities and/or error terms will reveal whether the different amateur user groups follow an optimization strategy similar to avalanche professionals or use simplified decision approaches. The general goal of decision aids is to make appropriate choices more accessible and more likely for their users by reducing the complexity of decision situations. All of the existing decision aids for decision-making in avalanche terrain provide an automated integration of multiple terrain and snowpack parameters into a single decision variable 123 Nat Hazards

(McCammon and Haegeli 2007). For the purpose of this study, a hypothetical but realistic decision aid was developed, which is described in more detail in the next section of this paper. To our knowledge, no work exists that models the effect of a decision aid by incorporating it into a DCE. The use of a decision aid to combine multiple attribute levels in a DCE relates to the important topic of information presentation in stated preference experiments. While numerous studies have researched the effect of different amounts of information (see, e.g. Bergstrom and Stoll 1990; Ajzen et al. 1996; Hoehn and Randall 2002; Spash 2002) or different presentations of individual attributes (Corso et al. 2001; Arentze et al. 2003) on the consistency of survey results, the present study goes one step further by examining the changes in decision preferences that result from adding a com- bined representation of multiple attributes in a DCE.

3 Method

3.1 Online survey design

When a backcountry traveler is planning for a trip into avalanche terrain, one of the first crucial risk management tasks is to select an appropriate destination for the outing under the given avalanche and weather conditions. This choice includes the selection of the ultimate trip destination, such as a mountain peak, and the approximate route to reach that objective in generally unmarked terrain. Once on the trip, the exposure to avalanche hazard can be minimized further by smaller-scale adjustments to the final route according to the local conditions. To emulate this decision progression, we included two different DCEs in our online survey: The first DCE examined the destination choice, while the second experiment focused on the small-scale choice between individual slopes. In this paper, we exclusively focus on the analysis of the destination choice task. While the basic structure of the choice task was the same for all three user groups, the attributes in the DCE were adjusted for each group to ensure realistic decision situations. The destination choice for the backcountry skier group was framed as a ski route choice (Fig. 1), a ski area choice for out-of-bounds skiers and a snowmobile area choice for snowmobile riders. Table 1 pre- sents the attributes and their levels for the choice task for each of the three user groups. Decision attributes can be grouped into two different types. Context attributes apply to all alternatives of a given choice task and only change between choice sets, while destination-specific attributes describe the details of the individual choice alternatives. When planning a backcountry trip, the public avalanche bulletin and weather forecast serve as the primary sources for information on large-scale avalanche and weather conditions. Ava- lanche bulletins in western Canada typically include danger ratings for the three elevation bands, alpine, treeline and below treeline, and several short paragraphs of text. Avalanche danger is generally rated on an international five-point scale as either Low (1), Moderate (2), Considerable (3), High (4) or Extreme (5). (Canadian Avalanche Association [CAA] 2002). The attribute Expected Avalanche Conditions was implemented as a ten level variable with hypothetical avalanche bulletins representing typical avalanche conditions in western Canada. Each of the 10 bulletins was characterized by a unique triplet of danger ratings, which was presented to the survey participants together with the three weather- related attributes Cloud Cover, Temperature and Wind in a fashion similar to weather reports in newspapers or on the Internet. Since avalanche bulletins are generally published for large areas (Jamieson et al. 2008), it is realistic that avalanche conditions are similar for all destinations considered by backcountry travelers during their trip planning stage. 123 Nat Hazards

Fig. 1 Example of a choice set (version for backcountry skiers)

The destination-specific attributes can be divided into variables describing characteristics of the local terrain, and attributes relating to recreational enjoyment characteristics. The Terrain Rating attribute was modelled according to the avalanche terrain exposure scale (Statham et al. 2006), a three-point scale that expresses the general exposure of backcountry trips to avalanche hazard. The attribute Elevation Range/Terrain Type provides background information on the elevation bands where the trip is located. The remaining attributes focus on trip characteristics related to personal recreation enjoyment. Trip Character intends to cover a realistic range of possible activities for each of the three backcountry pursuits. The Encounter attribute represents the number of other recreationists that participants can expect to meet on this trip. The Guidebook Recom- mendation of the area is provided with a three-level star rating as frequently used in guide books and magazine articles. The Cost of Access attribute brings an additional aspect to the choice experiment by providing information on the required physical or financial effort to access the terrain. While the actual levels of the enjoyment-related attributes are different for the three user groups, their similarity should allow at least qualitative comparisons among the resulting choice models. Natural collinearities existed among several attributes, which needed to be addressed in the design by eliminating unrealistic attribute combinations. Furthermore, the attribute Elevation Range/Terrain Type was completely excluded from the statistical design and its value in the DCE was derived from the values of Terrain Rating and Cost of Access in the backcountry skiing survey or Terrain Character in the snowmobiling and out-of-bounds skiing surveys. The final design required 36 separate choice sets, each containing three destination alternatives and a base alternative of ‘None of these destinations/I would stay at home’ (Fig. 1). The hypothetical decision aid used in the study offered basic travel recommendations for each of the alternatives presented in the DCE (Fig. 2), based on their characteristics. The recommendations were computed from the attributes Expected Avalanche Conditions and Terrain Rating using the following simple rules:

123 a Hazards Nat

Table 1 Attributes and levels for destination choice Description Attribute name Levels

Context attributes Expected snow and avalanche Expected avalanche conditions (ExAv) 1. Early season conditions (Danger ratingsa: Mod – Low – n/a; Sumb:3) conditions for travel day 2. Spring conditions (Low – Low – Low, 3) 3. Well-settled mid-winter snowpack (Mod – Mod. – Low, 5) 4. Recent storm (Cons. – Mod. – Low, 6) 5. Deep slab instability (Cons. – Mod. – Mod; 7) 6. Reactive surface hoar layer weakness at treeline (Mod. – Cons. – Mod.; 7) 7. Shallow slab instability (Cons. – Cons. – Mod; 8) 8. Recent storm with significant wind influence (High – Cons. – Mod.; 9) 9. Storm with wind on existing weakness (High – High – Cons.; 11) 10. Significant loading on existing weakness (High – High – High; 12) Expected weather conditions Cloud cover (ClCo) 1. Sunny for travel day 2. Partly cloudy 3. Overcast and snow or rain Temperature (Temp) 1. Very cold (-15°C) 2. Moderately cold (-10°C) 3. Warm (0–5°C) Wind (Wind) 1. Calm 2. Moderate 3. Strong 123 123 Table 1 continued Description Attribute name Levels

Destination-speciﬁc Terrain Terrain rating (TrRt) 1. Simple attributes characterisation 2. Challenging 3. Complex Elevation range/ Backcountry skier Out-of-bounds skier Snowmobile rider Terrain type (Elev) 1. 90% – 10% – 0%c 1. Below treeline 1. Below treeline 2. 50% – 50% – 0% 2. Treeline 2. Treeline 3. 30% – 50% – 20% 3. Alpine 3. Alpine 4. 30% – 20% – 50% 5. 20% – 15% – 65% 6. 10% – 10% – 80% Motivational factors Trip character (Char) 1. Steady climb and continuous run 1. Ridges with steep chutes 1. Touring/Long distance riding 2. Yo-yo skiing 2. Open bowls 2. High marking/Cliff jumping 3. Short climbs and runs 3. Tree skiing 3. Exploring/Climbing Encounters (Enc) 1. Less than 3 1. Less than 3 1. Less than 5 2. 3 to 6 2. 3 to 6 2. 5 to 25 3. More than 6 3. More than 6 3. More than 25 Guidebook 1. 1 Star recommendation 2. 2 Stars (Rec) 3. 3 Stars Cost or difﬁculty Cost of access Backcountry skier: Out-of-bounds skier: Snowmobile rider: to gain access (Cost) Overall elevation gain Lift ticket price Access trail conditions 1. 600 m 1. $20 1. Generally groomed 2. 1,200 m 2. $45 2. Some groomed

3. 1,800 m 3. $65 3. None groomed Hazards Nat

a Danger ratings are provided for the three elevation bands alpine, treeline and below treeline b Sum of the numerical values of the danger ratings of the three elevation bands c Number triplets represent percentage of time spent below treeline, at treeline and in alpine Nat Hazards

Fig. 2 Example of a choice set with decision aid recommendation (version snowmobilers)

– ‘Normal caution’ (color code: green) for all terrain choices under low and moderate danger ratings; – ‘Extra caution’ (yellow) in complex terrain under moderate and in simple and challenging terrain under considerable danger ratings; and – ‘Not recommended’ (red) for complex terrain under considerable and all terrain under high avalanche danger ratings. The attribute Elevation Range/Terrain Type was used to choose the most relevant of the three danger ratings given in the bulletin. Although the apparent number of attributes increased with the introduction of the decision aid, the choice complexity actually decreased, because the resulting recommendation summarized three independent attributes. While the recommendation of the decision aid does not explicitly make a decision for the user, the wording and the color coding of the recommendations can certainly be interpreted in such a way.

3.2 Survey implementation

In addition to the choice experiments, the online survey also included a comprehensive set of questions about avalanche hazard perceptions and backcountry behaviour. The sections most relevant for the present analysis included questions on (a) personal motivation factors for recreating in avalanche terrain; (b) avalanche and backcountry training and experience and (c) general socio-demographics. The amateur survey for this study was conducted during the early summer of 2005. The link for the online survey was emailed to individuals who had provided their email addresses in several intercept surveys in the Golden area of south-western British Columbia during the previous winter. Due to a low initial response rate, the link was also sent to a convenience sample, which consisted of personal acquaintances who backcountry ski and snowmobile rider contacts from a snowmobile avalanche awareness course pro- vider. Since out-of-bounds skiers and snowboarders are generally not organized in clubs, it was not possible to acquire additional email addresses for this user group. 123 Nat Hazards

Any survey participants who had professional affiliations in the avalanche community (e.g. CAA, Association of Canadian Mountain Guides (ACMG)) or were professionally engaged in avalanche related work such as ski patrolling or guiding were excluded from this analysis to ensure that the samples only represented amateur backcountry users. This resulted in final sample populations of 136 amateur backcountry skiers (BC), 55 amateur out-of-bounds skiers (OB) and 104 amateur snowmobile riders (SM) for the analysis (Table 2). It is important to recognize that due to the small sample size and the recruiting strategy, our sample of the respective user groups cannot be viewed as representative of their populations. Since this was a pilot study, the survey was deliberately not broadcasted to a wider audience to conserve possibilities for a future, more advanced survey. Within the online survey, each participant of the amateur survey was presented with four activity-specific choice sets for the destination choice task (Fig. 1). The choice sets were drawn randomly from the pool of 36 without replacement until the entire set had been exhausted; thereafter, another round of random draws started. In the third and fourth choice sets, the recommendation of the decision aid was included as an additional destination- specific attribute (Fig. 2). The backcountry skiing version of the survey was used to expand this study to the professional avalanche community. Avalanche professionals are defined as individuals who earn significant portions of their annual income by providing safety to others traveling in avalanche terrain. When responding to the choice sets, professionals were asked to imagine their destination choices as private backcountry ski trips to eliminate possible effects of guiding constraints. Avalanche Professionals were not presented with any decision aid recommendations, because these risk communication tools are not aimed at professional backcountry travelers. In the spring of 2006, a link to the professional online survey was broadcasted to all members of the CAA and ACMG. These two organizations represent the great majority of individuals professionally involved in avalanche work in Canada. In addition, requests were sent to HeliCat Canada, the Backcountry Lodges of British Columbia Association and the Canadian Ski Guide Association to encourage their members and guiding staff to participate in this study. In total, 162 avalanche professionals completed the online survey. For the analysis, the sample was limited to survey participants, who regularly guide clients in avalanche terrain, to capture the decision expertise of the most experienced segment of participants. The final sample of 100 avalanche professionals consisted of approximately 25% of the relevant ACMG and 23% of the professional CAA memberships (Table 2).

Table 2 Numbers of survey participants User groups Abbreviation Number of Response participants rate

Amateur groups Backcountry skiers BC 136 49% Out-of-bounds skiers OB 55 33% Snowmobile riders SM 104 20% Reference group Avalanche professionals Prof 100 *25%a a Due to the broad dissemination of the survey link by a number of professional associations, the response rate can only be estimated as a rough percentage of the relevant memberships 123 Nat Hazards

3.3 Destination choice models

Separate destination choice models were estimated for each user group with and without the decision aid (Table 3). The Expected Avalanche Conditions attribute was coded as a linear and quadratic polynomial function based on the sum of the danger ratings over all three elevation bands. All other variables were effects coded and the part-worth utility of the base level was calculated as the negative sum of the other coefficients. In order to determine the significance of the base levels, their standard errors were calculated using the covariance matrix according to equations 7.4 of Ben-Akiva and Lerman (1985). The interaction effect between the Expected Avalanche Conditions and the Terrain Rating was included in the models to explicitly examine the influence of the decision aid as it primarily targets users’ awareness and understanding of this interaction. This resulted in three additional linear and quadratic terms for the Expected Avalanche Conditions attribute under different terrain ratings. Asymptotic t-tests (Ben-Akiva and Lerman 1985) were used to quantitatively explore the differences in part-worth utility values among the various decision models.

4 Results

4.1 Characterization of survey participants

An examination of the general characteristics revealed a high degree of homogeneity within the survey samples of each user group. The most relevant aspects for the discussion of the DCE results are the avalanche experience and training levels of the different user groups and their primary motivations for backcountry recreation. The Professional group has the most diverse recreation interests in avalanche terrain. On average, Professionals are engaged in two other winter backcountry sports. BC and OB generally participate in one other activity, and SM focus almost exclusively on snowmobile riding. While the Professional group clearly has the most backcountry experience, no significant differences in years of backcountry experience were detected among the three amateur user groups. Among them, BC has the highest percentage of participants who had formal avalanche training (83%). A total of 60% of OB and 71% of SM had taken at least an avalanche awareness seminar. Among those with formal training, BC had the highest level of training ahead of OB and SM. Skiing or snowmobile riding skill levels were rated highest in the Professional and OB groups, while BC and SM groups had higher percentages of skiers and riders with intermediate skill levels. Backcountry skills were rated significantly higher by Professionals and BC than the other two groups. Significant differences between the groups were also observed for many of the motivational factors. For BC and Professionals, the most important motivation factors for recreation were ‘beautiful surroundings’, ‘to be close to nature’ and ‘solitude’. In contrast, the ‘opportunity for good snow’ was the single most highly rated factor by OB. In addition, ‘challenging terrain’, ‘having fun’ and ‘enjoying the beautiful outdoors’ were also important for OB. SM rated ‘having fun’ as the most important factor, although ‘beautiful surroundings’ was also important.

123 123 Table 3 Choice model results by user groups without and with decision aid (DA) Attribute levels Part-worth utilities without DA Part-worth utilities with DA

Prof BC OB SM BC OB SM

Sample size 400 272 110 208 272 110 208 Context attributes ExAva Linear comp. 21.31***c 21.11*** 21.05** 22.10** 22.03***;d 22.51*** 22.73*** Quadratic comp. 21.16*** 21.10*** 21.08 0.02 20.53 21.05 0.17 ClCo Sunnyb 0.09 0.41 0.25 0.46 0.15 20.90* 0.45 Partly cloudy 20.10 0.06 0.11 0.12 0.20 0.41 20.18 Overcast 0.01 20.47* 20.36 20.58 20.35 0.49 20.27 Temp Very coldb 0.00 20.05 21.07** 0.43 0.15 0.80:: 20.22 Moderately cold 0.87** 0.46 1.19* 20.39 0.40 20.33; 0.47 Warm 20.87*** 20.42 20.12 20.05 20.25 20.47 20.25 Wind Calmb 20.35 20.22 20.79 20.69 0.24 0.28 0.19 Moderate 0.43 0.15 0.59 20.13 20.09 0.09 0.25 Strong 20.08 0.07 0.20 0.82 20.15 20.37 20.44; Destination-speciﬁc TrRt Simpleb 0.60*** 0.65*** 20.54 20.59* 0.69*** 1.00** 0.63**::: attributes Challenging 0.37** 0.38** 20.02 20.05 0.24 20.26 0.10 Complex 20.99*** 21.03*** 0.55 0.63* 20.93*** 20.74 20.73**;;; Elev n/a since derived from other attribute values Char Steady climbb 0.17 20.01 – – 0.28** – – Yo-yo 20.15* 0.01 – – 20.10 – – Short climbs 20.02 0.00 – – 20.18 – – Ridges and chutesb ––20.52 – – 0.06 – Bowls – – 20.61 – – 0.55**:: – Tree skiing – – 1.13*– – 20.62**;; – Hazards Nat Touring/Long dist.b –––20.29 – – 0.22 High marking – – – 20.62*** – – 0.04:: Exploring – – – 0.91** – – 20.26;; a Hazards Nat Table 3 continued Attribute levels Part-worth utilities without DA Part-worth utilities with DA

Prof BC OB SM BC OB SM

Enc Less than 3b 0.26** 0.43*** 0.02 – 0.10;; 0.04 – 3to6 20.04 20.12 0.01 – 0.07 20.07 – More than 6 20.22** 20.31** 20.03 – 20.17 0.03 – Less than 5b – – – 0.17 – – 20.08 5 to 25 – – – 0.04 – – 20.07 More than 25 – – – 20.21 – – 0.15:: Rec 1 Starb 20.02 20.26** 20.48** 20.15 20.42*** 20.28 20.13 2 Star 0.08 0.22** 0.15 0.28 0.22** 20.05 0.04 3 Star 20.06 0.03 0.33** 20.12 0.20 0.33* 0.10 Cost 600 m2 20.30** 20.33*– – 20.15 – – 1,200 m 0.04 0.08 – – 0.15 – – 1,800 m 0.26** 0.25 – – 20.00 – – $20b – – 0.09 – – 0.16 – $45 – – 0.20 – – 0.10 – $65 – – 20.29 – – 20.27 – Generally groomedb – – – 0.07 – – 0.01 Some groomed – – – 0.13 – – 0.08 None groomed – – – 20.19 – – 20.09 TrRt-AvSc Simple AvSc linearb 0.99*** 0.74*** 0.67* 0.75** 1.26*** 1.33** 1.32*** AvSc quadraticb 0.55* 0.04 20.45 20.22 0.03 20.66 20.06 Chall. AvSc linear 20.05 20.43 20.23 20.44 0.21 20.83 20.94**

123 AvSc quadratic 20.32 20.81* 0.89 0.49 20.25 20.84;; 20.63 Comp. AvSc linear 20.94*** 20.31 20.45 20.31 21.47***;; 20.51 20.38 AvSc quadratic 20.22 0.77** 20.53 20.27 0.22 1.50**:: 0.69*:: Intercept 1.41*** 0.85*** 0.73 1.83*** 0.52** 0.81* 0.97** 123 Table 3 continued Attribute levels Part-worth utilities without DA Part-worth utilities with DA

Prof BC OB SM BC OB SM

Log-likelihood ratio test (p-values) 2 9 10-40 8 9 10-26 2 9 10-4 1 9 10-14 5 9 10-25 1 9 10-9 6 9 10-13

Underlined attributesa were included in the DA assessment b Base level c Statistical signiﬁcance: ***p \ 0.01, **0.01 B p \ 0.05, *0.05 B p \ 0.10 d Statistical signiﬁcance of DA effect: 3 arrows: p \ 0.01; 2 arrows: 0.01 B p \ 0.05; 1 arrow: 0.05 B p \ 0.10; upward arrows indicate positive change, downward arrows present negative change a Hazards Nat Nat Hazards

4.2 Decision preferences without decision aid

Among the context attributes (Table 3), Expected Avalanche Conditions most strongly affected the decision of all user groups. For the Professionals and the BC, both the linear and quadratic specifications were significant, while for the OB and SM user groups, only the linear term was significant (Fig. 3). Only Professionals and OB viewed Temperature as another significant context variable. More significant differences were observed among the destination-specific attributes (Table 3). All user groups except OB viewed the Terrain Rating as a significant attribute, although opposing signs of the coefficients indicate fundamental differences in the inter- pretation of this attribute among user groups. Other destination-specific attributes that were relevant for the Professional user group include Trip Character, Encounters and Cost of Access (overall elevation gain). In the BC model, the additional significant destination- specific attributes were Encounters, Guidebook Recommendation and Cost of Access.In the case of the OB model, the only significant destination-specific attributes were Trip Character and Guidebook Recommendation. In addition to Terrain Rating, SM seem to primarily base their choice on the Trip Character attribute. Different patterns were also observed in the interaction effects between Expected Avalanche Conditions and Terrain Rating among the four different user groups. The part-worth utilities for the Professional group exhibit the expected pattern (Fig. 4a) with strong preferences for trips in complex terrain during low avalanche hazard, but choosing trips in simple terrain more frequently during times of high avalanche hazard. BC users show a weaker preference for trips in simple terrain and their patterns for trips in challenging and complex terrain, even though significant, did not match the pattern exhibited by the Professional group (Fig. 4b). For OB and SM, which are not graphed here, only the linear component for trips in simple terrain turned out to be significant. The intercepts (bottom of Table 3) describe the mean effect of all the unobserved sources of utility on the destination choice (Hensher et al. 2005). All intercepts were positive and highly significant (with the exception of OB; p-value = 0.12), which indicates a strong preference for choosing one of the three alternatives over staying at home. Despite the relatively small sample sizes, p-values of the log-likelihood ratio tests (Table 3) indicate that all models are statistically highly significant.

Fig. 3 Part-worth utilities for expected avalanche conditions main effects by user groups (Prof Avalanche professionals, BC Backcountry skiers, OB Out-of- bounds skiers, SM Snowmobile riders)

123 Nat Hazards

Fig. 4 Part-worth utilities for interaction between expected avalanche conditions and terrain rating (Simp. Simple terrain, Chall. challenging terrain, Comp. complex terrain) for (a) Professionals; (b) Amateur BC without decision aid and (c) Amateur BC with decision aid

4.3 Effect of decision aid recommendations

The introduction of the decision aid resulted in several changes in the choice models for the three amateur target groups (columns 5 to 7 of Table 3). Significant changes in part- worth utilities between models with and without the decision aid are highlighted with upward (significant increase) or downward (significant decrease) pointing arrows. The number of arrows indicates the level of significance (1: p-value\10%; 2: p-value\5%; 3: p-value \1%). Within the context variables, the decision aid recommendations primarily resulted in an increased awareness of the Expected Avalanche Conditions attribute. While the strength of the linear part-worth utility components increased for all three user groups (resulting in increased levels of significance of the attribute for OB and SM), the actual change was only significant for BC. Among the destination-specific attributes, the recommendations of the decision aid primarily affected the part-worth utilities for the different Terrain Rating levels. While there was no change in the preferences of the BC group, the initially more aggressive 123 Nat Hazards terrain preferences of OB and SM were replaced by more conservative choice patterns. However, an opposite effect was observed in the attribute Trip Character of the OB and SM user groups. Here, the emphasis shifted towards trips that are characterized by a higher exposure to avalanche hazard. Additional significant changes in part-worth utilities were also observed for Encounters (BC, SM), where the introduction of the decision aid eliminated it from the list of significant attributes. The decision aid also led to a number of significant part-worth utility changes in the interaction terms between the Expected Avalanche Conditions and Terrain Rating (Table 3). The effect is most obvious in the BC user group, where the new pattern for terrain preferences under different avalanche scenarios (Fig. 4c) now resembles the pattern exhibited by the Professional group much closer (Fig. 4a). While the effect can also be observed in the models of the OB and SM user groups (not graphed), the resulting terrain preferences do not match the preferences of the Professional group as nicely. In the OB model, the introduction of the decision aid also resulted in a considerable improvement of the p-value of the log-likelihood ratio test from 10-4 to 10-9 (Table 3). Changes were insignificant for the other user groups.

5 Discussion

The goal of this study was to examine the current decision preferences of backcountry recreationists and to explore the potential effect of a decision aid. In order to discuss the implications of the results, we will ﬁrst elaborate on the observed decision preferences of the Professional group before examining the amateur preferences with and without the decision aid.

5.1 The Professional reference model

The array of significant attributes and their part-worth utility patterns indicate that Pro- fessionals adjust personal enjoyment preferences to the existing avalanche hazard conditions when deciding on a backcountry destination. The present study represents a first attempt to explicitly examine this important trade-off behaviour in backcountry decision- making. The attributes primarily relevant for the assessment of avalanche hazard— Expected Avalanche Conditions, Temperature, Terrain Rating, and the examined interactions between Expected Avalanche Conditions and Terrain Rating—exhibited all the expected patterns. The increasing sensitivity towards higher levels of avalanche hazard (Fig. 3) is in agreement with the opinion of avalanche experts that avalanche hazard increases exponentially on the avalanche danger scale (see, e.g. Munter 1997). The significant enjoyment factors, such as the preference for a low number of Encounters and large elevation gains (Cost of Access), clearly reflect the rather ‘purist’ motivational interests of this group in backcountry recreation. With 14 significant attribute levels, the choice model for Professionals is the most comprehensive in this study, confirming the hypothesis that avalanche professionals have the necessary skills to comprehensively assess the decision situations provided in the present survey. While it is inherently difficult to obtain comprehensive revealed preference data on decision-making in avalanche terrain to further validate the results of our stated preference experiment, a number of comparative studies on recreational site choice (Loomis 1993; Haener et al. 2001; Grijalva et al. 2002) and hurricane evacuation behaviour (Whitehead 2005) provide empirical evidence that models based on stated preferences 123 Nat Hazards exhibit predictive validity in areas similar to decision situation examined in this study. Furthermore, List and Gallet (2001) emphasize that if stated preference studies are based on familiar behaviour, their predictive validity typically increases. Given our results and relevant findings in other studies, we are confident that the observed Professional choice behaviour can be used as a reference model for examining the decision skills of the amateur groups and the effects of an avalanche decision aid.

5.2 Amateur decision models without decision aid

The strong influence of the Expected Avalanche Conditions on the decision patterns of all user groups provides a strong signal about the importance of the avalanche danger scale for the communication of avalanche hazard to recreational users. Similar results were observed by Whitehead et al. (2000) who showed that the decision to evacuate among North Car- olina coast households relies heavily on the present rating on the Saffir-Simpson Scale, which describes the intensity of the approaching hurricane. The BC model without decision aid exhibits patterns that are very similar to the Pro- fessional references model. While Trip Character did not turn out to be a significant attribute for this user group, Guidebook Recommendation played a significant role in their decision process. It seems plausible that the high part-worth utility of BC for trips with only two stars and their preference for fewer encounters can be linked to their high scores for ‘solitude’ and ‘being close to nature’ in the motivation section of the survey. The only statistically significant differences that emerged between the Professional and BC models were in the interaction effects as illustrated in Fig. 4a and b. While the high level of similarity between the two models was initially surprising, it can be explained by the similar backgrounds and motivations of the two user groups and the relatively high level of avalanche awareness training that most members of the BC user group obtain. The discrepancy in the interaction effects highlights the advanced capability of Professionals to combine individual decision attributes in their decision-making process. Both OB and SM exhibit choice models that are considerably less detailed and are more focused on maximizing recreational enjoyment. While it is not possible to compare these models statistically to the two other user groups, a qualitative comparison still reveals interesting insights. For both groups, only the linear component of the part-worth utility for Expected Avalanche Conditions turned out to be significant, indicating considerably less refined sensitivities compared to the Professional and BC user groups (Fig. 3). While the Temperature part-worth utility of the Professional group is in agreement with the fact that avalanche danger increases with temperatures above the freezing point, the signs of the OB coefficients seem to indicate a comfort related preference. Professionals and BC prefer to avoid terrain that is rated as complex because of its potential for avalanches, whereas SM show preferences for complex terrain, apparently reflecting their interest in riding tech- nically more challenging terrain. Similarly, the preference of the OB group for resorts with a three star Guidebook Recommendation is consistent with their high motivational scores on ‘challenging terrain’ and ‘opportunity for good snow’. The strongly negative part-worth utility of the SM model for ‘High Marking’ must be interpreted as a conservative choice since approximately 60% of SM survey participants reported doing this type of riding regularly. The consistently positive and significant values of the intercepts can be interpreted as a generally strong commitment to backcountry recreation. The weaker commitment of the OB user group seems reasonable as skiing within the ski area boundary is always an option under unfavourable avalanche conditions. We attribute the high values of explanatory 123 Nat Hazards power of the BC model to the homogeneity within our sample and the familiarity of the survey participants with the strongly focused decision task. The observed differences in part-worth utility patterns among the various user groups provide evidence for the presence of the rationally adaptive approach (Simon 1955; Heiner 1983) in decision-making in avalanche terrain. While the primary differences between the BC model and the Professional reference model lay in the neglect of the interaction effects by the BC, the less trained SM and OB seem to employ decision strategies that are considerably less focused on avalanche hazard. At this point, the study provides no further evidence whether their decision preferences come from a general lack of understanding of avalanche hazard or reflect deliberate choices for high risk conditions. An analysis of error terms did not provide any conclusive evidence in support of the model of passive bounded rationality (de Palma et al. 1994) for further distinguishing the Pro- fessional and BC choice models.

5.3 Amateur decision models with decision aid

Foremost, the decision aid led to a higher awareness of Terrain Rating as a relevant attribute for assessing avalanche hazard. In the case of the SM and OB, it resulted in a reversal of their Terrain Rating preferences towards more conservative decisions. In the BC model, the introduction of the decision aid resulted in Expected Avalanche Conditions and Terrain Rating interaction terms (Fig. 4c) that are more in agreement with the Pro- fessional reference model. In addition, the results with the decision aid also showed that users were willing to compromise on their enjoyment preferences in the name of avalanche safety, as shown by the BC group, whose initial strong preferences for fewer Encounters did not emerge in the model with the decision aid. On the other hand, all three user groups showed stronger preferences for more aggressive Trip Character choices that are more in agreement with their actual skiing and riding preferences as specified in the motivation section of the survey. These results hint at the potential existence of additional trade-offs between different motivational factors. Unfortunately, the small sample sizes precluded a more detailed examination of these motivational trade-offs within each user groups. Overall, these results show that a well-designed decision aid has the potential to promote sensitivity to avalanche hazard and potentially decision-making consistency. The relatively simple decision aid used in the present study was able to provide decision makers with sufficient additional information about the interaction of Expected Avalanche Conditions and Terrain Rating to reduce the decision complexity to a more manageable level, and produce a significant shift in their decision preferences towards more avalanche hazard sensitive choices.

6 Conclusions

The present study provides several interesting results for the application of choice models and the development of decision aids for travel in avalanche terrain. The method of DCE was able to effectively characterize the aggregate decision preferences of the four different user groups when choosing a backcountry destination. The approach of using the Pro- fessional sample as a reference for examining the decision skills of the three amateur group produced useful insights about their decision capabilities and deﬁciencies. To our knowledge, this is the ﬁrst time that a DCE was used to quantitatively compare decision skills of different target audiences. 123 Nat Hazards

The applied results of this study contain a number of important recommendations for avalanche safety agencies with respect to the design of decision aids for traveling in avalanche terrain. First of all, the results clearly show that different user groups have significantly different decision preferences depending on their relevant training, experience and recreation preferences. However, some basic decision deficiencies, such as the inability to properly understand interactions between parameters, were observed across all amateur user groups. A future decision aid for decision-making in avalanche terrain would therefore need to focus on raising the awareness about these interactions. The results show that an aid that combines Expected Avalanche Conditions, Terrain Rating and Elevation, can—at least in the context of a stated choice survey—considerably influence the decision- making process of users and steer them towards more avalanche hazard sensitive behaviour. While the underlying logic of the recommendation was invisible to the participants of this survey, a decision aid that displays these relationships more explicitly might provide additional educational value. The observed preference shift towards more aggressive trips when the conditions are approved by the decision aid is encouraging, as a decision aid should not only warn users of hazardous avalanche conditions, but also encourage them to enjoy more challenging settings when conditions are appropriate. Results from avalanche safety surveys should always be examined critically. First, voluntary surveys about avalanche safety issues have the inherent potential to primarily attract participants who already have a special interest in avalanche safety and the context of a safety survey can further cause participants to provide answers that are biased towards more conservative behaviour (i.e. social compliance). Second, the decision situations presented in an online survey are simply unable to fully capture the physical complexity and emotional involvement experienced when planning a real backcountry trip. The high degree of realism in the survey results, however, indicates that careful sequencing of survey questions and the multi-attribute nature of the DCE are able to alleviate some of these concerns. While this survey has examined the difficult trade-off between maximizing recreational enjoyment and minimizing avalanche hazard more realistically and thoroughly than any previous study, more field research is necessary to examine the validity of the DCE approach with respect to actual decision-making when planning backcountry trips or traveling in avalanche terrain. While the present study was able to provide interesting insights in the aggregate decision-making behaviour of the different backcountry user groups, there is considerable potential for further studies in backcountry decision-making. Individual decision-making under risk is strongly influenced by psychological factors such as risk perception and risk propensity (Weber and Milliman 1997; Slovic 2000). While the low number of survey participants and choice sets completed by each participant in the present study prevented a further segregation of the sample on the basis of their choice behaviour,3 future studies in this area could use a latent class approach (Boxall and Adamowicz 2002; Semeniuk et al. 2009) in combination with a risk attitude scale (Weber et al. 2002) to gain further insights into decision-making under risk.

Acknowledgments The initial study on amateur decision preferences was supported by the ADFAR project of the Canadian Avalanche Association, which was funded by the Government of Canada through the Search and Rescue New Initiative Fund (SAR-NIF). The subsequent survey on professional decision- making was ﬁnanced jointly by HeliCat Canada, the Canadian Avalanche Association and the Backcountry

3 Since each survey participant only complete two decision scenarios with and two without the decision aid, it was not possible to use a latent class approach to further examine skill heterogeneity within the various user groups. 123 Nat Hazards

Lodges of British Columbia Association. Additional in-kind support was provided by Kicking Horse Mountain Resort, Zacs Tracs and Glacier National Park. The ﬁrst author of this paper was supported by a postdoctoral fellowship from the Social Science and Humanities Research Council of Canada for part of this research. The authors would also like to express their thanks to Don Anderson for his advice on the statistical design of the discrete choice experiment and Paulus Mau, Grant Statham, Wayne Tucker, Matt Gunn, Lisa Ochowycz and Shannon Dixon for their contributions to this research. We also thank the two anonymous reviewers for their constructive comments.

References

Adamowicz W, Boxall PC, Williams M, Louviere JJ (1998) Stated preference approaches for measuring passive use values: choice experiments and contingent valuation. Am J Agric Econ 80(1):64–75. doi: 10.2307/3180269 Adams L (2005) A systems approach to human factors and expert decision-making within the Canadian avalanche phenomena. MALT Thesis. Royal Roads University, Victoria, BC, 284 pp Ajzen I, Brown TC, Rosenthal LH (1996) Information bias in contingent valuation: effects of personal relevance, quality of information, and motivational orientation. J Environ Econ Manag 30:43–57. doi: 10.1006/jeem.1996.0004 Arentze T, Borgers A, Timmermans H, DelMistro R (2003) Transport stated choice responses: effects of task complexity, presentation format and literacy. Transp Res Part E-Logist Transp Rev 39(3):229– 244. doi:10.1016/S1366-5545(02)00047-9 Arnberger A, Haider W (2007) Would you displace? It depends! A multivariate visual approach to intended displacement from an urban forest trail. J Leis Res 39(2):245–265 Atkins D (2000) Human factors in avalanche accidents. In: International snow science workshop, Big Sky, MT, pp 46–51 Atkins D, McCammon I (2004) Differences between avalanche experts and novices. In: International snow science workshop, Jackson Hole, WY Sept, pp 19–24 Ben-Akiva ME, Lerman SR (1985) Discrete choice analysis: theory and application to travel demand. MIT Press, Cambridge MA, p 390 Bergstrom JC, Stoll JR (1990) An analysis of information overload with implications for survey design research. Leis Sci 12(3):265–280 Bolognesi R (2000) NivoTest: a pocket tool for avalanche risk assessing. In: International snow science workshop, Big Sky, MT, pp 554–557 Boxall PC, Adamowicz WL (2002) Understanding heterogeneous preferences in random utility models: a latent class approach. Environ Resour Econ 23(4):421–446. doi:10.1023/A:1021351721619 Bhudak Consultants Ltd (2003) Public avalanche safety program review—prepared for British Columbia Ministry of Public Safety and Solicitor General Campbell C, Bakermans L, Jamieson B, Stethem C (2007) Current and future snow avalanche threats and mitigation measures in Canada. Canadian Avalanche Centre, Revelstoke, BC Canadian Avalanche Association (CAA) (2002) Observation guidelines and recording standards for weather, snowpack, and avalanches. Canadian Avalanche Centre, Revelstoke BC, Canada Corso PS, Hammitt JK, Graham JD (2001) Valuing mortality-risk reduction: using visual aids to improve the validity of contingent valuation. J Risk Uncertain 23(2):165–184. doi:10.1023/A:1011184119153 Czerlinski J, Gigerenzer G, Goldstein DG (1999) How good are simple heuristics? In: Gigerenzer G, Todd PM, Group AR (eds) Simple heuristics that make us smart. Oxford University Press, New York, NY, pp 97–118 Dash N, Gladwin H (2007) Evacuation decision making and behavioral responses: individual and household. Nat Hazards Rev 8(3):69–77. doi:10.1061/(ASCE)1527-6988(2007)8:3(69) de Palma A, Myers GM, Papageorgiou YY (1994) Rational choice under an imperfect ability to choose. Am Econ Rev 84(3):419–440 DeShazo JR, Fermo G (2002) Designing choice sets for stated preference methods: the effects of complexity on choice consistency. J Environ Econ Manag 44(1):123–143. doi:10.1006/jeem.2001.1199 Engler M, Mersch J (2000) SnowCard: Lawinen—Risiko—Check. Verlag Martin Engler, Sulzberg, Germany, p32 Gigerenzer G, Todd PM, ABC Research Group (1999) Simple heuristics that make us smart. Oxford University Press, New York, NY Gladwin CH, Gladwin H, Peacock WG (2001) Modeling hurricane evacuation decisions with ethnographic methods. Int J Mass Emerg Disasters 19(1):117–143

123 Nat Hazards

Grijalva TC, Berrens RP, Bohara AK, Shaw WD (2002) Testing the validity of contingent behavior trip responses. Am J Agric Econ 84(2):401–414. doi:10.1111/1467-8276.00306 Haener MK, Boxall PC, Adamowicz WL (2001) Modeling recreation site choice: do hypothetical choices reflect actual behavior? Am J Agric Econ 83(3):629–642. doi:10.1111/0002-9092.00183 Haque E, Etkin D (2007) People and community as constituent parts of hazards: the significance of societal dimensions in hazards analysis. Nat Hazards 41(2):271–282. doi:10.1007/s11069-006-9035-8 Heiner RA (1983) The origin of predictable behavior. Am Econ Rev 73(4):560–595 Hensher DA (2006) How do respondents process stated choice experiments? Attribute consideration under varying information load. J Appl Econ 21(6):861–878. doi:10.1002/jae.877 Hensher DA, Rose JM, Greene WH (2005) Applied choice analysis—a primer. Cambridge University Press, New York, NY Hoehn JP, Randall A (2002) The effect of resource quality information on resource injury perceptions and contingent values. Resour Energy Econ 24(1–2):13–31. doi:10.1016/S0928-7655(01)00051-3 Jamieson JB, Stethem CJ (2002) Snow avalanche hazards and management in Canada: challenges and progress. Nat Hazards 26(1):35–53. doi:10.1023/A:1015212626232 Jamieson B, Campbell C, Jones A (2008) Spatial and temporal scale effects in Canadian avalanche bulletins. Cold Reg Sci Technol 51(2–3):204–213. doi:10.1016/j.coldregions.2007.03.012 Klein G (1998) Sources of power: how people make decisions. MIT Press, Cambridge, MA Klein G (2002) The fiction of optimization. In: Gigerenzer G, Selten R (eds) Bounded rationality—the adaptive toolbox. MIT Press, Cambridge, MA, pp 103–122 Larcher M (1999) Stop or go: Entscheidungsstrategie fu¨r Tourengeher. Berg Steigen 99(4):18–23 Lindell MK, Lu J-C, Prater CS (2005) Household decision making an evacuation in response to hurricane Lili. Nat Hazards Rev 6(4):171–179. doi:10.1061/(ASCE)1527-6988(2005)6:4(171) List JA, Gallet CA (2001) What experimental protocol influence disparities between actual and hypothetical stated values? Environ Resour Econ 20(3):241–254. doi:10.1023/A:1012791822804 Loomis JB (1993) An investigation into the reliability of intended visitation behavior. Environ Resour Econ 3(2):183–191. doi:10.1007/BF00338784 Louviere JJ, Hensher DA, Swait JD (2000) Stated choice methods: analysis and application. Cambridge University Press, New York, NY Mazzotta MJ, Opaluch JJ (1995) Decision making when choices are complex: a test of Heiner’s hypothesis. Land Econ 71(4):500–515. doi:10.2307/3146714 McCammon I (2000) The role of training in recreational avalanche accidents in the United States. In: International snow science workshop, Big Sky, MT, 2–6 Oct, pp 37–45 McCammon I (2001) Decision making for wilderness leaders: strategies, traps and teaching methods. In: Wilderness risk manager’s conference, Lake Geneva, WI, pp 16–29 McCammon I (2002) Evidence of heuristic traps in recreational avalanche accidents. In: International snow science workshop, Penticton, BC, 30 Sept –4 Oct McCammon I, Haegeli P (2007) An evaluation of rule-based decision tools for travel in avalanche terrain. Cold Reg Sci Technol 47(1–2):193–206. doi:10.1016/j.coldregions.2006.08.007 McClung DM (2002a) The elements of applied avalanche forecasting—Part I: the human issues. Nat Hazards 25:111–129. doi:10.1023/A:1015665432221 McClung DM (2002b) The elements of applied avalanche forecasting—Part II: the physical issues and rules of applied avalanche forecasting. Nat Hazards 25:131–146. doi:10.1023/A:1015604600361 McClung DM, Schaerer PA (2006) The Avalanche handbook. The Mountaineers, Seattle, WA, p 347 McFadden D (1974) Conditional logit analysis of qualitative choice behaviour. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, NY, pp 105–142 Mozumder P, Raheem N, Talberth J, Berrens RP (2008) Investigating intended evacuation from wildfires in the wildland–urban interface: application of a bivariate probit model. For Policy Econ 10:415–423 Munter W (1997) 3 9 3 Lawinen: Entscheiden in kritischen Situationen. Agentur Pohl & Schellhammer, Garmisch Partenkirchen, Germany, p 220 Payne JW, Bettman JR, Johnson EJ (1988) Adaptive strategy selection in decision-making. J Exp Psychol Learn Mem Cogn 14(3):534–552. doi:10.1037/0278-7393.14.3.534 Pfeiffer N, Foley JM (2006) Skill and knowledge mastery of students in level I avalanche courses. In: International snow science workshop, Telluride, CO, 1–6 October Rasid H, Haider W, Hunt LM (2000) Post-flood assessment of emergency evacuation policies in the Red River basin, Southern Manitoba. Can Geogr 44(4):369–387 Semeniuk CA, Haider W, Beardmore B, Rothley KD (2009) A multi-attribute trade-off approach for advancing the management of marine wildlife tourism: a quantitative assessment of heterogeneous visitor preferences. Aquat Conserv: Mar Freshw Ecosyst 19(2):194–208. doi:10.1002/aqc.990 Simon HA (1955) Behavioral model of rational choice. Q J Econ 69(1):99–118. doi:10.2307/1884852

123 Nat Hazards

Slovic P (2000) The perception of risk. Earthscan, London, p 473 Spash CL (2002) Informing and forming preferences in environmental valuation: coral reef biodiversity. J Econ Psychol 23:665–687. doi:10.1016/S0167-4870(02)00123-X Statham G, McMahon B, Tomm I (2006) The avalanche terrain exposure scale. International Snow Science Workshop, Telluride, CO, 1–6 October, pp 491–497 Train K (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge, MA Tsuge T, Kishimoto A, Takeuchi K (2005) A choice experiment approach to the valuation of mortality. J Risk Uncertain 31(1):73–95. doi:10.1007/s11166-005-2931-6 Weber EU, Milliman RE (1997) Perceived risk attitudes: relating risk perception to risky choice. Manag Sci 43(2):123–144. doi:10.1287/mnsc.43.2.123 Weber EU, Blais AR, Betz NE (2002) A domain-speciﬁc risk-attitude scale: measuring risk perceptions and risk behaviors. J Behav Decis Making 15(4):263–290. doi:10.1002/bdm.414 Whitehead JC (2005) Environmental risk and averting behavior: predictive validity of jointly estimated revealed and stated behavior data. Environ Resour Econ 32(4):301–316. doi:10.1007/s10640-005-4679-5 Whitehead JC, Edwards B, Van Willigen M, Maiolo JR, Kenneth W, Smith KT (2000) Heading for higher ground: factors affecting real and hypothetical hurricane evacuation behavior. Environ Hazards 2(3):133–142. doi:10.1016/S1464-2867(01)00013-4

123