Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling

Valuing Information in Complex Systems: An Integrated Analytical Approach to Achieve Optimal Performance in the Beer Distribution Game

Even seemingly simple systems can produce complex dynamics, which leads management professionals to develop tools for training, monitoring, and improving performance. Management simulators provide useful insights about human behavior and interactions, while computational and informational decision support tools offer opportunities to reduce inconsistencies, errors, and non-optimal human choices, particularly for complex systems that involve multiple decision makers, uncertainty, variability, and time. We use the context of a popular management simulator that teaches students about the bullwhip effect (i.e., the beer distribution game) to explore an integrated decision analytic, control theory, and system dynamics approach to the game that recognizes the value of available (imperfect) information and considers the value of perfect information to provide the optimal strategy. Using a discrete event simulation, we characterize optimal decisions and overall team scores for the situation of actual available information and perfect information. We describe our implementation of the strategy in the field to win the 2007 beer game world championship played at the 25th conference of the International System Dynamics Society. This paper seeks to demonstrate that better understanding of the system and use of available information leads to significantly lower expected costs than identified in prior studies. Understanding complex systems and using information optimally may increase system stability and significantly improve performance, in some cases even without better information than already available

Supporting uncertain policy decisions for global catastrophic risks

The three articles in this dissertation explore the contested, multi-dimensional concept of uncertainty and how experts and decision makers collectively grapple with it at governance organizations tasked with addressing global catastrophic risks (GCRs). This project examines the foundational concept of uncertainty and then explores “decision support” dynamics at the National Aeronautics and Space Administration (NASA) and the Intergovernmental Panel on Climate Change (IPCC) – the primary knowledge brokers in the governance regimes addressing planetary defense and climate change respectively. Article #1 begins by examining the contested, multidimensional concept of uncertainty itself. The paper presents a critical analysis of the conceptual literature on uncertainty that has become increasingly standardized behind the tripartite distinction between uncertainty location, uncertainty level, and the nature of uncertainty. I argue that the epistemological foundation on which this framework is built is both vague and inconsistent. Perhaps most surprising is its exclusion of the term “confidence” – which has become the dominant perspective for characterizing and communicating uncertainty in many disciplines and policy contexts today. This article reinterprets the tripartite framework from a Bayesian epistemological perspective, which views uncertainty as a mental phenomenon arising from “confidence deficits” as opposed to the ill-defined notion of “knowledge deficits” that dominates the literature. I propose a more consistent set of rules for determining when uncertainty may or may not be quantified, a clarification of the terms “ignorance” and “recognized ignorance,” and an expansion of the “level” dimension to include levels of uncertainty reducibility. Lastly, I challenge the usefulness of the conventional distinction made between aleatory and epistemic uncertainty and propose a more useful distinction based on developments in the field of complexity science that highlights the unique properties of complex reflexive (i.e. human) systems. Article #2 explores the decision support process of uncertainty reduction. “Mission-oriented” public research organizations like NASA invest in R&D to improve decision-making around complex policy problems, thus producing “public value.” However, the estimation of benefits produced by such R&D projects is notoriously difficult to predict and measure – a challenge that is magnified for GCRs. This article explores how public research organizations systematically reduce key uncertainties associated with GCRs. Building off of recent literature highlighting the organizational and political factors that influence R&D priority-setting at public research organizations, this article develops an analytical framework for explaining R&D priority-setting outcomes that integrates the key stages of decision analysis with organizational and political dynamics identified in the literature. This framework is then illustrated with a case study of the NASA planetary defense mission, which addresses the GCR of near-Earth object (asteroid and comet) impacts. The case study reveals how organizational and political factors interact with every stage in the R&D priority-setting process – from initial problem definition to project selection. Lastly, the article discusses the extent to which the case study can inform R&D priority-setting at other mission-oriented organizations, particularly those addressing GCRs. Article #3 investigates the decision support process of uncertainty communication. The uncertainty language framework used by the IPCC is designed to encourage the consistent characterization and communication of uncertainty between chapters, working groups, and reports. However, the framework has not been updated since 2010, despite criticism that it was applied inconsistently in the Fifth Assessment Report (AR5) and that the distinctions between the framework’s three language scales remain unclear. This article presents a mixed methods analysis of the application – and underlying interpretation – of the uncertainty language framework by IPCC authors in the three special reports published since AR5. First, I present an analysis of uncertainty language term usage in three recent special reports: Global Warming of 1.5°C (SR15), Climate Change and Land (SRCCL), and The Ocean and Cryosphere in a Changing Climate (SROCC). The language usage analysis highlights how many of the trends identified in previous reports – like the significant increase in the use of confidence terms – have carried forward into recent assessments. These observed trends, along with ongoing debates in the literature on how to interpret the framework’s three language scales inform an analysis of IPCC author experiences interpreting and implementing the framework. This discussion is informed by interviews with lead authors from the SRCCL and SROCC. Lastly, I propose several recommendations for clarifying the IPCC uncertainty language framework to address persistent sources of confusion highlighted by the authors