Federal policy has embraced risa management as an appropriate paradigm for wildfire management. Economic theory suggests that over repeated wildfire events, potential economic costs and risas of ecological damage are optimally balanced when management decisions are free from biases, risa aversion, and risa seeking. Of primary concern in this article is how managers respond to wildfire risa, including the potential effect of wildfires (on ecological values, structures, and safety) and the likelihood of different fire outcomes. We use responses to a choice experiment questionnaire of U.S. federal wildfire managers to measure attitudes toward several components of wildfire risa and to test whether observed risa attitudes are consistent with the efficient allocation of wildfire suppression resources. Our results indicate that fire managers' decisions are consistent with nonexpected utility theories of decisions under risa. Managers may overallocate firefighting resources when the likelihood or potential magnitude of damage from fires is low, and sensitivity to changes in the probability of fire outcomes depends on whether probabilities are close to one or zero and the magnitude of the potential harm.

Wildfire management involves significant complexity and uncertainty, requiring simultaneous consideration of multiple, non-commensurate objectives. This paper investigates the tradeoffs fire managers are willing to make among these objectives using a choice experiment methodology that provides three key advancements relative to previous stated-preference studies directed at understanding fire manager preferences: (1) a more immediate relationship between the instrument employed in measuring preferences and current management practices and operational decision-support systems; (2) an explicit exploration of how sociopolitical expectations may influence decision-making and (3) consideration of fire managers’ relative prioritisation of cost-containment objectives. Results indicate that in the current management environment, choices among potential suppression strategies are driven largely by consideration of risk to homes and high-value watersheds and potential fire duration, and are relatively insensitive to increases in cost and personnel exposure. Indeed, when asked to choose the strategy they would expect to choose under current social and political constraints, managers favoured higher-cost suppression strategies, ceteris paribus. However, managers indicated they would personally prefer to pursue strategies that were more cost-conscious and proportionate with values at risk. These results confirm earlier studies that highlight the challenges managerial incentives and sociopolitical pressures create in achieving cost-containment objectives.

Wildfires present a complex applied risk management environment, but relatively little attention has been paid to behavioral and cognitive responses to risk among public agency wildfire managers. This study investigates responses to risk, including probability weighting and risk aversion, in a wildfire management context using a survey-based experiment administered to federal wildfire managers. Respondents were presented with a multiattribute lottery-choice experiment where each lottery is defined by three outcome attributes: expenditures for fire suppression, damage to private property, and exposure of firefighters to the risk of aviation-related fatalities. Respondents choose one of two strategies, each of which includes "good" (low cost/low damage) and "bad" (high cost/high damage) outcomes that occur with varying probabilities. The choice task also incorporates an information framing experiment to test whether information about fatality risk to firefighters alters managers' responses to risk. Results suggest that managers exhibit risk aversion and nonlinear probability weighting, which can result in choices that do not minimize expected expenditures, property damage, or firefighter exposure. Information framing tends to result in choices that reduce the risk of aviation fatalities, but exacerbates nonlinear probability weighting.

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