Forecasting resource-allocation decisions under climate uncertainty: fire suppression with assessment of net benefits of research
Making input decisions under climate uncertainty often involves two-stage methods that use expensive and opaque transfer functions. This article describes an alternative, single-stage approach to such decisions using forecasting methods. The example shown is for preseason fire suppression resource contracting decisions faced by the United States Forest Service. Two-stage decision tools have been developed for these decisions, and we compare the expected gains to the agency, in terms of reduced personnel costs, of the single-stage model over the two-stage model, existing hiring decisions, and decisions that would have been made given perfect foresight about wildfire activity. Our analysis demonstrates the potential gains to versions of our single-stage model over existing hiring decisions, equivalent to a benefit-cost ratio of 22. The research also identified additional gains accruing from imposing biases on the single-stage model, associated with asymmetric penalties from contracting decisions.