Project Risk and Decision Making (RDM) takes up the challenge of better applying earth system sensors, data and analytics to improve risk management and decision-making in land use (e.g., agriculture and infrastructure), hazards mitigation, insurance, and other areas sensitive to the pace and pattern of environmental change.
Project Risk brings decision analytics to earth systems management, with the goal of contributing to all EarthLab projects that have social impact and decision-making implications.
Risk and decision analysis is a mostly normative set of methods for making choices under uncertainty. We apply decision analysis as a research tool by giving more weight to studying the decision context and structure at the front end, and post-hoc, forensic decision analysis at the back end, with particular attention to the value of additional information, as illustrated here, to test theories and research hypotheses. Our approach aims to create realistic decision models that can be simulated under historical and projected conditions, subjected to extensive sensitivity analysis, and that can be used and revised by actual decision makers who face risks like drought, flood, and wildfire.
Besides gaining new insights into the risks themselves, our challenge is to simulate the decision process so that researchers and managers can examine the implications of multiple factors and decisions, and thus get a feel for how sensitive their choices are to uncertain conditions. Decision analysis as a learning tool can:
We also have a special interest in extreme events and decisions driven by extremes, as part of Earth Lab’s extremes project.
Project Risk’s first focal effort is tied to the integrated EarthLab/IRISS “Project Drought.” Project Drought seeks to improve drought early warning and response through improved measurement of key variables such as soil moisture, and analytics that help decision-makers, like water managers, ranchers and farmers, manage drought impacts. Project Drought-Risk will initially contribute with a focus on agricultural decision-makers, specifically for range livestock production and the interaction of drought, rangeland ecology, production, markets, and policy. Building on work already underway, we will incorporate drought risk management via decision models prototyped to help ranchers make herd management, land use, and financial choices during drought, and by simulating the effect of USDA’s experimental range vegetation index insurance. The insurance analysis reflects Earth Lab’s goal of applying environmental data-sets to practical problems, in this case that means analyzing gridded satellite-derived vegetation index and station-derived rainfall index data, and combining them with other data (like hay prices), to evaluate the ability of affordable insurance to help ranchers through droughts. Learn more.