Management for an uncertain world: robust decision theory in face of regime shifts (NSF Biology Postdoc Application)
This research will explore methods for more robust management of ecosystems when the underlying dynamics are uncertain. In so doing, this research will seek to bridge mathematical and biological methods that have hitherto been developed largely in isolation, such as optimal control (from decision-theoretic work) and early warning signals (from resilience work) as well as machine learning approaches (from statistics and computer science). To anchor this research in the biology of a real world problem, examples and applications will come from the ecosystem dynamics and economic concerns of marine fisheries. I will pursue three main objectives in this project: (1) Developing an approach to integrate early warning signals into a decision-theory framework (2) exploring how nonparametric Bayesian inference can account for structural uncertainty (unknown unknowns) in ecological dynamics, (3) exploring how active learning approaches in each of these areas could be developed to design adaptive management policies that actively decrease uncertainty.
Each of these objectives address active and open questions that span the ecological literature seeking to understand resilience, resource economics literature seeking to optimize the utilization of natural resources in a sustainable manner, and applied mathematics and computer science literature in which these emerging methods seek applications and testing in real systems.