Environmental factors that influence wildfire protective ...

Int. J. Emergency Management, Vol. 10, No. 2, 2014

Environmental factors that influence wildfire protective-action recommendations Frank A. Drews* Center for Natural & Technological Hazards, University of Utah, Salt Lake City, UT 84112, USA and Department of Psychology, University of Utah, 380 South 1530 East, Rm. 502, Salt Lake City, UT 84112, USA Fax: 801-581-5841 E-mail: [email protected] *Corresponding author

Adrian Musters L-3 Communications Systems West, 640 North 2200 West, Salt Lake City, UT 84116, USA E-mail: [email protected]

Laura K. Siebeneck Department of Public Administration, University of North Texas, Denton, TX 76203, USA E-mail: [email protected]

Thomas J. Cova Center for Natural & Technological Hazards, University of Utah, Salt Lake City, UT 84112, USA and Department of Geography, University of Utah, USA E-mail: [email protected] Abstract: Each year wildfire incident commanders (ICs) manage thousands of events throughout the USA that often threaten life and property. In this task they make important decisions to protect both firefighters and citizens, usually

Copyright © 2014 Inderscience Enterprises Ltd.

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F.A. Drews et al. under time pressure and uncertainty. Many environmental factors affect the choice and timing of the most effective protective-action in this context (e.g., evacuate, shelter-in-refuge, shelter-in-place). The goal of this research is to identify the critical factors that influence wildfire protective-action recommendations (PARs) and their relative importance. Forty-seven ICs from the western USA were surveyed to produce mean factor-importance scores, where multi-dimensional scaling (MDS) and pathfinder analysis were applied to visually assess factor similarity. The results indicate that more experienced ICs place greater importance on dynamic, fire-related factors, while also differing in their cognitive representation of these factors from less experienced ICs. These results have important practical implications in developing effective training interventions, supporting the process of sense-making, and designing decision support systems. Keywords: emergency management; wildland fire; ICs; incident commanders; PARs; protective-action recommendations; decision making; decision factors; evacuation; shelter-in-place. Reference to this paper should be made as follows: Drews, F.A., Musters, A., Siebeneck, L.K. and Cova, T.J. (2014) ‘Environmental factors that influence wildfire protective-action recommendations’, Int. J. Emergency Management, Vol. 10, No. 2, pp.153–168. Biographical notes: Frank A. Drews is a Professor of Psychology at the University of Utah and the Director of the Center for Human Factors in Patient Safety at the VA Medical Center in Salt Lake City Utah. His research and teaching focuses on breakdowns of human performance in complex environments like wild fire management, surface transportation, and healthcare. Adrian Musters is a Human Factors engineer at L-3 Communications designing, coding, and testing software for use in communications, intelligence collection, and network situational awareness for the Department of Defense. His emphasis is on filtering data and tasks so the user is presented with the information necessary for making critical decisions, and automating tasks that are better handled by the underlying software. Through cognitive analysis, rigorous usability testing, and intelligent underlying cognitive architectures, these interfaces facilitate optimal user performance in high-stress environments. Laura K. Siebeneck is an Assistant Professor in the Department of Public Administration and the Emergency Administration and Planning program at the University of North Texas. Her research interests include hazards, disaster evacuation and return-entry, emergency management, and resilience. Currently her research examines geographic and temporal dimensions of risk perception, communication, and household behaviour throughout the evacuation and return-entry processes during disasters. She teaches courses in hazard mitigation and preparedness, research methodology, and emergency management. Thomas J. Cova is Professor of Geography and Director of the Center for Natural & Technological Hazards at the University of Utah in Salt Lake City. His research and teaching interests are hazards, emergency management, transportation, and GIS with a particular focus on evacuation behaviour, analysis, and planning. He has published on a variety of topics in many leading hazards, transportation and GIS journals, and is most known for work on evacuation analysis in fire-prone communities. He teaches courses on hazards geography, emergency management, and GIS.

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Introduction

Major wildfire events have steadily increased in the USA in recent years (NIFC, 2013). Some of this increase has been attributed to a combination of land-use and climate change (Pierce et al., 2004; Schoennagel et al., 2004). Westerling et al. (2006) note that the increase in fire frequency in the western US may be related to longer wildfire seasons resulting from higher spring and summer temperatures, as well as earlier snow-melt dates. Projected warming in the western US will likely increase wildfire activity and expose more communities to wildfire hazards (Moritz et al., 2012). Another trend that is contributing to rising wildfire risks is a steady increase in the number of people living in fire-prone areas. These high-risk areas, referred to as the wildland-urban interface or WUI (Radeloff et al., 2005), occur where homes and other structures intermix with fire-prone wildlands. This can result in a range of vulnerabilities that present challenges for both emergency managers and WUI residents from fuel management and structure mitigation projects in the longer-term hazard phase to traffic management in the crisis phase (Collins, 2005; Cova et al., 2013). An analysis by Theobald and Romme (2007) revealed that the WUI in the USA expanded by 52% from 1970 to 2000 and now constitutes an estimated 12.5 million homes on 465,600 square km. The combination of more frequent wildfires and WUI expansion is heightening the need for highly-trained incident commanders (ICs) to manage these increasingly common and complex events. An important step in improving wildfire IC training is identifying important factors that affect the process of issuing protective-action recommendations (PARs) to threatened populations (Lindell and Perry, 1992, 2004). Because managing wildfires is a challenging and complex activity that can involve decision making under uncertainty, time-pressure, and limited resources, understanding the circumstances that lead to effective protective-action decisions is essential (Kim et al., 2006). While a significant progress has been made in studying decision making in the context of emergency response operations (Mendonça et al., 2001; Kowalski-Trakofler et al., 2003; Webb, 2004; Webb and Chevreau, 2006) and more specifically structural fire-fighting (McLennan et al., 2006), less attention has been paid to how wildfire ICs formulate and issue PARs to the public. In general, improving our understanding of the factors involved in decision making during wildfire events can advance protective-action theory (Brehmer, 1990; Gonzalez et al., 2003; Kersthold and Raaijmakers, 1997), as well as contribute to practical goals such as developing effective training interventions, supporting sensemaking (Weick, 1993), and designing decision support systems (Mendonça and Fiedrich, 2006; Kasper, 1996). The goal of this study is to assess the relative importance and similarity of environmental factors that influence PARs (e.g., evacuate, shelter-in-refuge, shelter-inplace). A secondary goal is to identify how experience affects these assessments. The overarching aim is to improve our understanding of the factors involved in PAR decisionmaking to both advance theory and develop more effective training tools. The subsequent sections of the paper review the literature, methods, and results of a telephone and web-based survey of Type 1, 2, and 3 ICs regarding wildfire PAR factors. The paper concludes with a discussion of the implications of the findings and areas for further research.

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Background

2.1 Decision making in emergency situations Research in decision making during emergencies provides insight into how the information and factors that are critical to issuing PARs are processed and organised by decision makers. Prior research notes a shift from a focus on choice to one of analysis of how humans interpret a situation and act (Hunt et al., 1989), but Useem et al. (2005) note that decision-making by those in stressful and complex wildfire situations remains under-examined. Several studies have addressed management errors of ICs through the examination of factors that may influence one’s decision-making ability. In their analysis of incident reports from 20 significant wildfire events in North America and Australia between 1993 and 2005, McLennan et al. (2006) identified various management and leadership weaknesses exhibited by wildfire ICs. The most common weaknesses include: •

becoming overwhelmed by information about the rapid development of the fire and lack of suppression resources

•

failing to act decisively to obtain essential need-to-know information

•

making decisions based on perceived threats without sufficient regard for resource availability.

McLennan et al. (2006, 2007) found that the greatest contributing factor leading to weaknesses in IC decision making is uncertainty. In their work they highlight six characteristics of effective ICs that influence the management of successful operations. These behaviour traits include: •

the ability to anticipate and plan for likely and worse-case scenarios

•

practicing clear and effective communication skills such as displaying maps, controlling speech tone and speed, practicing good listening skills, and utilising proper radio communication procedures

•

displaying assertiveness and speaking clearly to those around him or her

•

managing one’s workload effectively through use of reminder notes, prioritising tasks, and giving complete orders

•

recognising the need to reevaluate the situation should it deteriorate

•

using multiple sources of information.

Similar to leadership strengths, experience can play a significant role in the decisionmaking performance of an IC. In situations where the IC is unprepared to lead, or has little leadership experience, the time pressure and complexity of extreme events can inhibit the IC’s ability to exercise adaptive decision-making (Hannah et al., 2009). Fiedler (1992) showed that those less prepared and experienced in fighting urban fires often exhibit poor performance over those with more experience. Identification of these differences can lead to improved understanding of decisions made under duress, as well as help in developing training methods aimed at improving the quality of IC decision-making. McLennan et al. (2007) suggest that training include exercises for novice ICs that capture the deep-structure of emergency incidents and provide timely feedback to enhance learning outcomes.

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Many adverse consequences associated with disaster events result from errors attributed to action or inaction undertaken by organisations or persons in leadership positions (Dynes, 1974; Dynes et al., 1981). Useem et al. (2005) note that organisations, especially those involved with incident command operations, should strive to improve decision quality so as to avoid errors, improve sense-making, and increase one’s ability to work under stressful circumstances (Weick, 1993). While recent research has examined the usefulness of virtual simulation scenarios in training urban fire fighters (Schurr et al., 2006; Dugdale et al., 2004), significant challenges remain in improving the management of wildfire events. As McLennan et al. (2007) note, extreme care needs to be taken to ensure that new decision support and training systems account for human factors.

2.2 Protective-action recommendations PARs are among the most important decisions ICs make when facing a wildfire (Cova et al., 2009). A common wildfire PAR is evacuation (Cohn et al., 2006), which needs to be issued in a manner that avoids last-minute dashes (Handmer and Tibbits, 2005). A less-considered, potentially-effective alternative is to recommend that a threatened community seek protective shelter in a structure, safety area, or water body (Paveglio et al., 2008). This strategy has been used in conjunction with evacuation for select populations in some wildfires (Weiss and Chawkins, 2008), but a mass shelter-inplace has yet to be seen. Previous work on emergency response has identified key concepts for PAR decision making (Lindell and Perry, 1992; Dash and Gladwin, 2007). One important consideration is threat dynamics that lead the best PAR for a subset of the population to change (Cova et al., 2011). An IC must constantly monitor the environment and respond accordingly. It is more common for ICs to recommend evacuation early (potentially driven by commission bias) than to ‘wait and see’ (preserving the status quo). Cognitively, a preference for evacuation reduces the number of options, but shelter-inplace has been demonstrated as a viable option in events like the 2008 Santa Barbara Tea Fire where a gymnasium at Westmont College was used to harbor an estimated 800 students (Weiss and Chawkins, 2008). However, shelter-in-place recommendations are much less common than evacuation, and it is important to improve our understanding of the factors that influence such recommendations.

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Methods

The first step in this research was to identify an initial set of relevant environmental factors that may influence PARs. In interviews with three ICs we isolated 31 relevant factors that have the potential to affect the PAR decision process (Table 1). Each factor was a priori assigned to one of four categories: fire hazard, community context, warning and evacuation time, and policy context. Example fire-hazard factors include fire intensity, fire spread-rate, and the presence of spotting and branding (i.e., embers transported ahead of the flame front). Community context factors include the fuel in (and around) the community, structure flammability, and the presence of refuge shelter (e.g., designated community shelters and safety zones). Warning and evacuation time factors include the level of community preparedness, the population at risk, and the time to warn the threatened population. Policy factors include the political and regulatory

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context in which PARs are issued. For example, a mandatory evacuation recommendation is not possible in all jurisdictions (AFAC, 2005). Table 1

Factors that affect protective-action decisions in wildfires from an IC perspective

Factor

Description

Change

Slope, hills, ridges, valleys, canyons

Static

Wind direction

Cardinal direction of wind

Dynamic

Wind speed

Wind speed in mph

Dynamic

Fire hazard Terrain characteristics

Fuel moisture

Water content in fuel

Dynamic

Humidity

Relative humidity

Dynamic

Temperature

Air temperature

Dynamic

Forecast weather

Predicted temp, wind, humidity

Dynamic

Fire spread rate

Fire advancement in m/min

Dynamic

Fire intensity

Rate of fuel consumption

Dynamic

Fire direction

Cardinal direction fire is moving

Dynamic

Percent contained

0–100% of perimeter suppressed

Dynamic

Distance from fire to community

Miles or kilometres

Dynamic

Spotting and branding

Embers, spotting ahead of fire

Dynamic

Fire suppression resources

Fire fighters, fire engines, bull dozers

Dynamic

Housing density

Structures per unit area

Static

Fuel in/around community

Type, pattern, density

Static

Fuel along exiting roads

Distance from road, type, density

Static

Structure flammability

Shingle/tile, wood/plaster, …

Static

Defensible homes

Number, location, quality

Dynamic

Number of structures at risk

1, …, n

Dynamic

Number, capacity of exit roads

1, …, n, vehicles per hour

Dynamic

Direction of exit roads

Cardinal direction of exits

Dynamic

Community context

Shelter/Safe zones

Number, location, type, accessibility

Dynamic

Livestock in community

Type, amount

Dynamic

Warning and evacuation time Community preparedness

Evacuation plan, notification methods, … Dynamic

Population at risk

Total number of persons threatened

Time to warn population

Hours and minutes

Dynamic

Special needs populations

Elderly, young, disabled, …

Dynamic

Law enforcement personnel

Number, type, experience, …

Dynamic

Dynamic

Policy context Legal/regulatory context

Regulatory context

Static

Political context

External pressure/influence

Dynamic

‘Change’ is in reference to a 24-hour period.

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3.1 Procedure The study consisted of two parts: •

telephone interviews with the participants

•

completion of an online survey.

The phone interviews included both open and closed-ended questions to assess the participant’s experience, their perception of the factors that influence PARs, and their perspectives on implementing a stay-and-defend policy in the USA (McCaffrey and Rhodes, 2009). Upon completion of the phone interviews, participants were invited to answer a web-based survey. After completion of the importance ratings, participants rated the relatedness or similarity of pairs of factors with regard to PARs (e.g., “How similar are wind direction and fire direction?”). Using techniques in multi-dimensional scaling (MDS), the similarity ratings were used to provide a visual representation of the factors (which would likely be relatively high in the example just given). The resulting representation is a reflection of the respondent’s perception of the factors and their underlying dimensions, thus providing insight into how ICs think about PARs.

3.2 Participants Using contact information gathered from the US Forest Service website, telephone numbers for wildfire ICs were obtained. A total of 85 potential participants were identified and contacted via phone to request participation in our study. Specifically, 47 ICs located throughout the western US (California, Oregon, Washington, Montana, Idaho, Wyoming, Arizona, and New Mexico) participated in the phone survey. Of the ICs that participated in the phone survey, 63.8% completed the online portion of the survey, 15.0% partially completed the survey, and 21.3% declined participation. During the survey, 35 participants rated 31 factors on a 7-point important scale in issuing a PAR. The participants were primarily male (96%), middle-aged (mean age 46.2 years), with an average of 15.3 years of experience as an IC at any level. Of the ICs surveyed, 16.7% indicated that they were currently Type I, 10.4% were Type II, and 72.9% were Type III with ‘type’ indicating the level of fire they would manage (i.e., Type III being a small local wildfire, Type I being a large, complex wildfire). Regarding PARs, 83.8% responded that they had issued an evacuation order, whereas 16.7% had never issued an evacuation order. Twenty-nine percent of the participants had recommended that a population shelter-in-place in a wildfire, while 71% responded that they had never recommended that a population shelter-in-place. Attitudes towards shelter-in-place among participants varied, with 60.4% responding that they would consider recommending shelter-in-place, while 18.8% would never consider it (20.8% declined an answer). Furthermore, none of the ICs had recommended that a population ‘stay and defend’ property during a wildfire. However, when asked whether the USA should consider a stay-and-defend policy similar to the one that originated in Australia (AFAC, 2005; Paveglio et al., 2008; McCaffrey and Rhodes, 2009; Stephens et al., 2009), 45.8% favoured implementing a stay-and-defend policy, while 54.2% were not in favour of this approach.

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Results

4.1 Factor ratings Participants’ experience was determined by a median split of the number of previously managed wildfires. This number ranged from 2 to 600 fires (mean = 117; median = 70). Twenty-three ICs fell into the more-experienced group and 24 fell into the lessexperienced group. Across all participants, the five most important factors were: •

fire intensity

•

wind direction

•

forecast weather

•

wind speed

•

fire spread rate – factors related to fire threat.

Further analysis revealed that more-experienced ICs ranked fire spread-rate, wind direction, wind speed, spotting and branding, and fire intensity as the most important – fire behavioural factors that are also the most dynamic and uncertain in nature. Less-experienced ICs ranked fuel-in-and-around-the-community as the most important factor, followed by a five-way tie between shelter-safe-zones, distance-of-fire-tocommunity, fire-intensity, forecast weather, and fuel-moisture – a set comprised of a mix of dynamic and static factors. Table 2 shows the results of the importance ratings for the 15 highest ranked factors for more-experienced and less-experienced ICs. Table 2

Factor rankings according to level of IC experience More experienced

Less experienced

Rank Factor

Mean (SD)

1

Fire spread rate

6.71 (0.59)

Rank Factor 1

Fuel in/near community

6.65 (0.49)

2

Wind direction

6.65 (0.49)

2

Shelter/safe zones

6.59 (0.87)

3

Wind speed

6.59 (0.51)

Distance fire to comm.

6.59 (0.71)

4

Spotting and branding

6.56 (0.73)

Fire intensity

6.59 (0.62)

5

Fire intensity

6.53 (0.51)

Forecast weather

6.59 (0.71)

6

Forecast weather

6.35 (1.06)

Fuel moisture

6.59 (0.71)

7

Shelter/safe zones

6.24 (1.09)

Fire direction

6.53 (0.72)

8

Defensible structures

6.18 (1.13)

Terrain characteristics

6.53 (0.87)

Time to warn population

6.18 (1.01)

Wind speed

6.53 (0.72)

Population at risk

6.18 (1.01)

Wind direction

6.53 (0.80)

7

Mean (SD)

Resources available

6.18 (0.81)

11

Humidity

6.47 (0.94)

12

Fuel in/near community

6.18 (0.81)

12

Spotting and branding

6.41 (0.71)

13

Personnel available

6.12 (0.99)

Fuel along exiting roads

6.41 (0.94)

14

Distance fire to comm.

6.06 (0.90)

14

Population at risk

6.35 (0.93)

15

Terrain characteristics

6.06 (0.83)

15

Fire spread rate

6.35 (1.06)

The more experienced group has managed at least 70 fires, the median among the participants.

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T-tests were conducted to determine differences between factor rankings based on experience. The results indicated differences in the importance rankings for six factors: fuel-moisture (t(df=33) = 2.23, p < 0.05), fuel-in-and-around-the-community (t(df=33) = 2.45, p < 0.05), humidity (t(df=33) = 2.42, p < 0.05), fire direction (t(df=33) = 2.57, p < 0.05) distance of fire to community (t(df=33) = 2.23, p < 0.05) and fuel-along-exiting-roads (t(df=33)=33) = 3.01, p < 0.01). Less-experienced ICs ranked each of these factors as more important than the more-experienced group.

4.2 Multi-dimensional scaling Multi-dimensional scaling (MDS) (Kruskal, 1964) was performed using the proximity scaling method to determine dimensions in the ICs group structure of PAR factors (Borg and Groenen, 1997). MDS is a means for visualising similarity (or dissimilarity) in data, where more similar observations are depicted as more proximal. The MDS was applied to the factor similarity ratings from the survey (i.e., fire spread-rate was ranked as more similar to wind speed than the number of exit roads). To identify the optimal MDS solution, stress tests that assess the fit between mathematical solutions (minimum of dimensions required) and IC’s similarity ratings were conducted. The results of the stress tests indicated a considerable reduction in stress for two-dimensional spaces (Stress = 0.11), supporting a two-dimensional visualisation of the ICs rating data. A visual inspection of the two dimensions based on all participants’ ratings suggests that the vertical dimension of the MDS solution can be interpreted as indicating variables ranging from relatively static, community-based factors at the top to dynamic, wildfire-related factors at the bottom. The horizontal dimension can be interpreted as task orientation, with factors related to firefighting on the left side, and factors related to protecting the public on the right side (i.e., PARs). It is interesting to note that, although ‘fire intensity’ is a fire-hazard factor, it was more related to ‘population at risk’ in terms of recommending protective actions. Exceptions that do not fit well with these dimensions include ‘livestock’ in the lower right quadrant, which was given a very low importance weight by the participants and ‘forecast weather’ which is at the top of figure, but does not seem related to its surrounding relatively static, community-related factors. Further analysis focused on factor grouping in the decision space. For this analysis we calculated the original centroids (i.e.,, the mean value for all judgements in a category) for each of the four categories: fire hazard, community context, warning and evacuation time, and policy context. Application of the criterion of minimal distance of a factor to a centroid determined its new category membership resulting in a re-categorisation of a number of factors. The resulting space is shown in Figure 1 using ovals. The square symbols indicate the centroid location for each category. These categories correspond well with the dimensions of the MDS solution. For example, the category fire-hazard ranges in terms of the dynamic nature of the factors (vertical dimension), but all of the variables included in this category are associated with a task focus on fighting the fire (horizontal dimension). Contrarily, the variables that are part of the category ‘regulations’ are mapped in an area of slowly changing factors, but range in terms of task focus from firefighting to issuing PARs.

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F.A. Drews et al. Multi-dimensional scaling of factor similarity for all ICs (n = 36)

4.3 Pathfinder analysis To investigate the knowledge structure of participants in more detail, a Pathfinder analysis was performed (Cooke, 1992; Durso et al., 1994; Schvaneveldt et al., 1989) to identify close semantic relationships between factors. The Pathfinder scaling algorithm reveals the latent structure of a problem representation as a network using proximity data. Pathfinder has been used to distinguish between novices and experts (Schvaneveldt et al., 1985), to predict the order of recalled information (Cooke et al., 1986), to test the usability of menus (Roske-Hofstrand and Paap, 1986), and to discriminate the structure of categories (Hutchinson, 1989; Schvaneveldt et al., 1989). We analysed the similarity ratings of the 15 highest-rated factors for both experience levels. Figures 2 and 3 show the results for the high and low experienced ICs, respectively. In each figure, links connect proximal decision-making factors of high similarity to provide a visual representation of how ICs may cognitively organise these factors. The Pearson correlation between the graph-distance matrices (r = 0.11) between the two groups indicated distinctly different graphs. Measures of the focal point of the graph (i.e.,, centre and median) were computed. For both groups, as shown in Figures 2 and 3, fire spread rate is the median and in the central position. Thus, both groups focus on this factor as the most central aspect when considering PARs. However, this is where the similarity between the two groups ends. One of the most striking differences between the groups relates to the level of integration of the factors. While the factors for the moreexperienced ICs are interconnected, displaying a high level of integration and a coherent mental representation, the factors for the less-experienced ICs are grouped into one

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central and two peripheral clusters (of two factors each), revealing a less integrated and coherent mental representation. Figure 2

Pathfinder solution of top 15 factors for more experienced ICs (n = 18)

A link between the factors indicates the presence of a strong semantic relationship where its absence indicates the lack of such relationship. Figure 3

Pathfinder solution of top 15 factors for less experienced ICs (n = 18)

A link between the factors indicates the presence of a strong semantic relationship where its absence indicates the lack of such relationship.

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Discussion

The results of this study indicate that the factor importance assessments of moreexperienced and less-experienced ICs are related but also differ in important ways. One potential explanation for these differences is that the more-experienced ICs are likely to be in command of much larger and more complex Type I fires. However, an analysis of the distribution of IC types and experience did not support this claim. Less-experienced ICs identified factors that are more related to community characteristics than fire behaviour as the most important. Contrarily, more-experienced ICs identified fire factors as the most important, suggesting that a focus on fire-behaviour is key in deciding which PAR to issue and when. As a result, the more-experienced ICs rated dynamic, less certain factors as more important for decision making. Another difference between the groups is that the less-experienced ICs are less differentiated in their assessment as shown by the number of ties between factors (e.g., the five-way tie for the second most important factor in Table 1). A higher level of differentiation is commonly considered a hallmark of expertise (Hmelo-Silver and Green-Pfeffer, 2004) and this indirectly validates the approach taken in this work (i.e., splitting the groups of ICs according to the number of managed fires to differentiate experience). For methodological reasons, this finding is also important for training purposes, which should focus on differentiating the individual factors more effectively to improve decision quality. The MDS space also highlights the importance of understanding scenario dynamics in effective decision making (i.e., situational awareness). The vertical dimension of the MDS in Figure 1 reflects the dynamic nature of the factors involved in PAR decision making, while the horizontal dimension reflects a task set that ranges from fire-fighting on the left to protective-actions on the right. Taken together, these findings emphasise that the fire dynamics and task set differentiate IC expertise in formulating PARs. However, one question that is not answered by the above results is whether the experience differences are a result of variation in knowledge structures or arise from assessing the same factors using different importance weightings. Further work will be needed to replicate the MDS findings and answer this question, ideally with a larger sample size of ICs. The findings of this research have both theoretical and practical implications. On the theoretical side, the results of this study provide insight in the way ICs differ in their approach towards decision making in dynamic, hazardous environments. A focus on monitoring dynamic and uncertain variables is a training strategy that may improve decision making. On the practical side, the results indicate that differences exist in how differentiated, important, and related factors are in issuing PARs. This implies that IC training should emphasise not only the dynamic and uncertain nature of fire behaviour (or other factors), but also support the development of a more integrated representation of these variables. One approach to provide this information would be to emphasise more the interrelationship between variables using models and demonstrations. In the context of the development of decision support systems, one potential reason for miscalibration is the distortion of a person’s mental model of the problem (May, 1986). This implies that a less-experienced IC may reach incorrect conclusions and be overconfident when using a DSS as a result of not accurately representing the problem space. This work indicates that such mental representations exist in the less-experienced ICs.

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Finally, the results of this study indicate that multi-methodological approaches are needed to improve our understanding of expert cognition in formulating PARs. In general, results from similar research will improve our understanding of the cognitive processes involved in emergency management decision making in dynamic, uncertain naturalistic settings. This is a task that needs to be addressed in the near future, as wildfires and other climate-change induced extreme events are expected to increase.

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Conclusion

This study examined factors that influence PAR decision making in wildfires and how their importance ratings differ based on IC experience. This work reveals important differences between more experienced and less experienced ICs in terms of their cognitive representation of factors and relative importance. Potential consequences of differences in knowledge structures are differences in information search behaviour, assessment of a situation (situation awareness; Endsley, 1995), and ultimately decision making. Future research needs to investigate this in more detail. In addition, it is important to understand differences in knowledge representation, as training interventions should aim to minimise differences between less and more experienced ICs. With an increase in the need of protecting growing populations threatened by wildfires, the intent of this work is to address these challenges by improving our understanding of the factors that lead to improved PAR decision quality.

Acknowledgements The authors would like to thank the incident commanders that participated in this study. This work was supported by NSF grants CMMI-IMEE 065372 and CMMI-IMEE 1100890. Any opinion, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

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