Background
Policymakers and landowners must make complex, high-stakes decisions under considerable uncertainty, with long-term societal and environmental impacts. For instance, achieving the UK’s Net Zero Carbon goal by 2050 requires land-use strategies —such as afforestation. These strategies are developed amid uncertainties in climate change, economic dynamics, and regulatory shifts. This project focuses on advancing decision science by developing tools that rigorously quantify uncertainties across inputs, models, and statistical frameworks, combining environmental processes with economic behaviours. Leveraging recent advances in uncertainty quantification and linking to preference elicitation, we will codevelop transparent, responsive tools with end users that integrate their preferences into the way alternative strategies are evaluated, grouped and navigated through, empowering decision-makers to explore diverse strategies and associated risks through a lens tailored to their preferences.
PhD opportunity
This project addresses key challenges in creating adaptive, uncertainty-informed decision-support for policymakers and land managers. There is demand for easy-to-use decision support integrating leading scientific knowledge of environmental processes, and economic behaviour with explicit quantification of different sources of uncertainty (e.g. input, modelling and statistical).
Leveraging transformative advances in uncertainty quantification (UQ), the project will enable rapid exploration of alternative strategic pathways. You will develop and apply emulation techniques to provide real-time approximations of complex models, and use calibration methods, such as history matching, to transfer complex models and refine parameterisation by integrating and fusing new data. Together, these techniques will enable proactive, evidence-based decision-making on strategic and operational levels, including the development of local and national policy and land management plans.
A central challenge is capturing the complexity of decision-makers’ preferences. You will apply preference elicitation techniques to reveal how decision-makers prioritize and trade off across outcomes, integrating both nonlinear preferences and risk preferences into the framework. Incorporating these insights through preference-informed UQ, the project advances decision support to allow consideration and navigation through vast multidimensional spaces of alternative strategies and associated outcomes, while also maintaining transparency. Collaboration with experts and stakeholders will ensure that the developed tools are scientifically rigorous and practically relevant to government, industry, and stakeholders.
The supervisory team includes Associate Professor Amy Binner, an environmental economist specializing in the development of decision support systems incorporating economic valuation and policy design, and Professor Daniel Williamson, a Bayesian statistician specialising in UQ for decision making.
Other information
Academic profile. The project would suit students with a strong background in mathematics, statistics or data science who are interested in real world applications and guiding policy and investment decisions.
- https://www.exeter.ac.uk/research/leep/
- https://netzeroplus.ac.uk/project/add-trees/
- https://business-school.exeter.ac.uk/economics/people/profile/index.php?username=arb233
- https://mathematics.exeter.ac.uk/people/profile/index.php?username=dw356
