Research ?>

Research

The Environmental Decision Support Science Lab conducts multi-disciplinary social science research at the intersection of the environment, technology, and society. Strategies and decisions made at this nexus often encounter significant uncertainty about scientific evidence and involve stakeholders with conflicting objectives and values. The goal of our lab is to understand and improve the processes and tools that aid these decisions, both in the public and private sectors.

Expertise: Our team has expertise in decision analysis, statistical and conceptual modeling, survey techniques, and data visualization.

Conceptual Modeling and Decision Structuring
Data and models from physical, social, and natural sciences are frequently needed for important decisions. Conceptual models are used to describe the important components and relationships of a system, and decision structuring identifies those objectives and values that are important for particular decisions or decision processes. However, they are rarely compatible to be used as is in the same analysis because they were produced for different purposes. Our research team has developed conceptual modeling methods to provide a scientific basis for the selection of indicators and to define system boundaries.

 

Indicators
Indicators are observations or calculations that are used to systematically report or forecast social and biophysical conditions over time. Ideally, indicators are co-produced with scientists, decision-makers, and stakeholders so that the information that they convey is meaningful and useful. Our research team collaborated with over 200 scientists and is seeking feedback from decision-makers to develop recommendations and prototype indicators and indicator systems that were input into the U.S. Global Change Research Program indicators.

 

Systems Analysis
Systems analysis uses computational methods to understand social and environmental system behavior in order to quantify uncertainty and risks. Our research team has conducted systems modeling using empirical, Bayesian, optimization, and agent-based methods to combine physical, natural, and societal factors to study climate policies, water quality, environmental restoration, and the food-energy nexus.

Multi-Objective Decision and Policy Analysis
Decision and policy analysis methods are used to identify feasible and preferred options given multiple objectives, predicted consequences of different policy choices, and competing value judgments. Our research team has extensive experience applying these methods to consider decisions for climate policy, restoration choices, and setting water quality criteria.

Evaluation of Climate Decision Support Tools
Effectively supporting decisions with science is an active process that requires co-production of information. This involves using scientific methods that are designed to bring stakeholders into the production of knowledge and to structure information so that it is aligned with stakeholder needs. Our research team is assessing indicator understandability for non-scientific audiences and the usability of decision support tools by multidisciplinary scientists and decision-makers.

By focusing on co-production of knowledge with stakeholders, we produce actionable science relevant to climate adaptation and mitigation, coastal resilience, food-energy-water-environment nexus, and water quality. We are most known for our innovative work on participatory indicator design and evaluation.

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