Using structured decision making (SDM) can change how resource managers make decisions by separating the clinical problem analysis from the value based decision process. In a natural resource management setting, SDM necessitates making decisions based on clearly articulated objectives, recognizing scientific prediction in decisions, addressing uncertainty explicitly, and responding with transparency towards societal values in decision making. When used as an overarching framework, natural resource managers can be better equipped to identify, critique, and discuss sources and implications of uncertainty and thus improve decision-making.
SDM is a template for considering data, knowledge, values, and uncertainty transparently in the decision process. Indeed, uncertainty—appropriately explained and displayed—is a form of useful information. Analysts and planners can present uncertainty in a useful light by evaluating its implications in tradeoffs among alternative actions, and by estimating the incremental value of additional knowledge.
SDM will not solve every problem, but it can improve transparency and clarity in decision-making processes and help decision makers in the following ways: deciphering, decomposing, and understanding complex problems that create the need for decisions; maintaining the sequences and internal consistency of the various stages of decision-making; articulating and quantifying values that guide the design and selection of alternatives; guiding the input from scientific, experiential, and traditional forms of knowledge; and organizing and documenting the logic of choice and tradeoff.
The craft of natural resource decision-making will be more demanding in the future, and it will require a more flexible toolkit to support decisions that address complex problems. Large-scale drivers of change—climate, demographics, global economic patterns, and changing social values—will increasingly provide surprises and uncertainties that will further shape decision spaces and prompt rethinking decisions already made. Never has a better time existed to focus on enhancing decision-making. The coming years will demand closer attention to achieving and demonstrating tighter alignment with stated goals, despite increasing financial constraints and increasing social conflicts over natural resources. Decision processes will increasingly weigh environmental costs and benefits against those of economic development, social equity, and contribution to financial solvency.
Examples of the Team’s application of SDM to wildfire management challenges include Risk Management Assistance Team (RMAT) engagements at individual incident and regional scales to improve decision making during the management of large wildfires and the strategic wildfire risk planning (SWRP) process whereby quantitative wildfire risk is integrated with pre-identified suppression opportunities to help align long-term landscape management direction with short-term wildfire incident response. SDM methods tools are also being used to identify, prioritize, and implement opportunities for risk reduction due to wildfire, insect outbreak, and flood damage through proactive landscape management.