Project Risk and Decision Making (RDM) uses data and analytics to improve risk management and decision-making in land use (e.g., agriculture and infrastructure), hazards mitigation, insurance, extreme events, and other areas sensitive to the pace and pattern of environmental change.
Project Risk brings decision science to earth systems analysis and management, with the goal of bringing a social science dimension to Earth Lab projects.
What We Do
Decision science helps us understand the fundamental nature of choices made under uncertainty and informs us on how to simulate, predict and respond to choices under risk and uncertainty, especially when they are related to extreme events.
Risk analysis is aimed at quantifying both the likelihood of damaging events and their consequences. Many applications lack information on probability of events, but an even larger problem is lack of data on consequences that factor into a risk management decision.
Decision analysis is a systematic approach to calculating efficient responses to environmental and social risks. We apply decision analysis as a research tool by studying how the decision environment affects choices. We focus particularly on the value of additional information in adaptation to climate and other environmental extremes. Our approach aims to create realistic, theory-driven decision models that can be simulated under historical and projected conditions, subjected to extensive sensitivity analysis, and that can be used by stakeholders who face drought, flood, and wildfire risks.
Besides gaining new insight 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:
- Help analysts and decision-makers recognize the structure, trade-offs, and likely outcomes of a range of choices made under uncertainty;
- Provide tools for testing how sensitive decisions are to climate variation, to improved information, or to down-side risks;
- Suggest optimum, robust, or least-regret decisions.