Economic decision-making in wildfire defense and fire management programs is not easy when performed under efficiency criteria. The determination of variables to be considered and the lack of data analyzed in relation to the results achieved by the action plans adopted to reduce the impact of fires condition the adoption of strategic solutions, both in the management of the landscape against fires and in suppression operations. If, by itself, the decision on how much, where and how to invest protection budgets is complex, the choice in environments of risk and uncertainty undoubtedly increases the difficulty in finding the right solutions.

Determining the expected utility function and measuring risk aversion provide interesting and advanced diagnostic tools that allow comparing the responses that can be provided by the application of different action plans in the forest landscape. Based on the results obtained, the best solution under uncertainty scenarios can be selected. The integration of variables that identify the initial extinction difficulty of the landscape under study, as well as the potential danger of wildfires and their effects on the net change in the value of resources due to the fire’s impact and the extinction costs, help characterize the behavior of the expected utility functions. This paper analyzes the results of different utility functions and compares them with the purpose of identifying the expected utility function with the best explanatory capacity when choosing among different fire protection options under situations of uncertainty generated by climate change, the probability of occurrence, and the influence of social behaviors, as well as the different extinction capacities, among other factors. The management of forest fuels and the different opportunities for extinction depending on the combinations of means of suppression can be treated from the approach of choosing strategic solutions in scenarios of uncertainty. The SINAMI (Rodríguez y Silva, González-Cabán, 2010) and Visual-SEVEIF (Rodríguez y Silva, et al. 2013, 2014) models provide the baseline data for decision-making and choice of solutions under conditions of uncertainty in the forest landscape.