About Improving Decisions in a Complex and Changing World
This project—a joint endeavor of the RAND Pardee Center and the Environment, Energy, and Economic Development program within RAND Infrastructure, Safety, and Environment—conducts fundamental research on two key questions important to the design and use of decision tools for supporting climate-change decisionmaking:
What are the best ways to represent uncertainty for decisionmakers? and
What tools and methods work best in practice in providing these representations to decisionmakers?
In particular, the project strengthens the scientific foundations of robust decisionmaking (RDM), a promising new approach to computer-assisted support for decisions under conditions of deep uncertainty, that is, when decisionmakers are unsure of the system model or the prior probability distributions across the inputs to the system model(s).
RDM helps decisionmakers to identify robust strategies whose satisfactory performance is largely independent of the resolution of most "unknowns" and in characterizing the residual deep uncertainties via their impact on the choice among strategies. RDM has the potential to significantly improve decisionmaking for climate-related and other policy areas.
Interest in RDM is motivated by increased understanding of the processes of decisionmaking and the new capabilities of modern information technology which enable unprecedented interactions between groups of decisionmakers and tools for quantitative decision support.
This research draws on interactions with decisionmakers in two policy areas:
Long-term planning for the management of water supplies by agencies in California, and
The design of scientific observation systems that could provide actionable warning of abrupt climate change
In each policy area, the project employs integrated assessment models to characterize policy-relevant uncertainty in different ways (e.g., best estimates, probability densities, and vulnerabilities of robust strategies), perform Judgment and Decision-Making (JDM) experiments to determine how decisionmakers respond to different characterizations of uncertainty, deploy with decisionmakers tools that employ promising characterizations of uncertainty, and assess how these uncertainty characterizations perform in actual use.
The project also conducts basic research that identifies and evaluates the best statistical and other algorithms for supporting robust decisionmaking under a variety of conditions.
This project will significantly enhance the scientific foundations of new approaches to climate-related decisions under conditions of deep uncertainty and it will enrich understanding of decisionmaking processes under these ubiquitous conditions. The research will provide important new insights into the types of algorithms that can best be used to support these new decision approaches.
The project will improve understanding of two important climate-related policy areas, water policy and abrupt climate change. Not only will the project’s findings directly aid decisionmakers in these two domains, the project will improve methods to support decisionmaking under conditions of deep uncertainty useful in addressing a very wide range of public and private sector decision challenges