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1. Means, motive, and opportunity: A method for understanding stakeholder agency within food–energy–water systems

   https://doi.org/10.1525/elementa.2021.00066  

   Bird, K. I. ; Nichols, V. A. ; Garay, E. S. ; Nowatzke, M. ; Essary, C. ; Post, K. K. ; Deviney, A. V. ; Welles, J. S. ; Alao, M. O. ; Dorbu, F. E. ; et al ( March 2022 , Elementa)

   Wicked problems are inherent in food–energy–water systems (FEWS) due to the complexity and interconnectedness of these systems, and addressing these challenges necessitates the involvement of the diverse stakeholders in FEWS. However, successful stakeholder engagement requires a strong understanding of the relationships between stakeholders and the specific wicked problem. To better account for these relationships, we adapted a means, motive, and opportunity (MMO) framework to develop a method of stakeholder analysis that evaluates the agency of stakeholders related to a wicked problem in FEWS. This method involves two key components: (1) identification of a challenge at the FEWS nexus and (2) evaluation of stakeholder agency related to the challenge using the dimensions of MMO. This approach provides a method for understanding the characteristics of stakeholders in FEWS and provides information that could be used to inform stakeholder engagement in efforts to address wicked problems at the FEWS nexus. In this article, we present the stakeholder analysis method and describe an example application of the MMO method by examining stakeholder agency related to the adoption of improved swine waste management technology in North Carolina, USA.
2. Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application

   https://doi.org/10.1038/s41598-022-11049-3  

   Licata, Richard J. ; Mehta, Piyush M. ( May 2022 , Scientific Reports)

   Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predictmore »thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

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3. Iowa Urban FEWS: Integrating Social and Biophysical Models for Exploration of Urban Food, Energy, and Water Systems

   https://doi.org/10.3389/fdata.2021.662186  

   Thompson, Jan ; Ganapathysubramanian, Baskar ; Chen, Wei ; Dorneich, Michael ; Gassman, Philip ; Krejci, Caroline ; Liebman, Matthew ; Nair, Ajay ; Passe, Ulrike ; Schwab, Nicholas ; et al ( May 2021 , Frontiers in Big Data)

   Most people in the world live in urban areas, and their high population densities, heavy reliance on external sources of food, energy, and water, and disproportionately large waste production result in severe and cumulative negative environmental effects. Integrated study of urban areas requires a system-of-systems analytical framework that includes modeling with social and biophysical data. We describe preliminary work toward an integrated urban food-energy-water systems (FEWS) analysis using co-simulation for assessment of current and future conditions, with an emphasis on local (urban and urban-adjacent) food production. We create a framework to enable simultaneous analyses of climate dynamics, changes in land cover, built forms, energy use, and environmental outcomes associated with a set of drivers of system change related to policy, crop management, technology, social interaction, and market forces affecting food production. The ultimate goal of our research program is to enhance understanding of the urban FEWS nexus so as to improve system function and management, increase resilience, and enhance sustainability. Our approach involves data-driven co-simulation to enable coupling of disparate food, energy and water simulation models across a range of spatial and temporal scales. When complete, these models will quantify energy use and water quality outcomes for current systems, andmore »determine if undesirable environmental effects are decreased and local food supply is increased with different configurations of socioeconomic and biophysical factors in urban and urban-adjacent areas. The effort emphasizes use of open-source simulation models and expert knowledge to guide modeling for individual and combined systems in the urban FEWS nexus.« less
4. Capturing Stakeholders’ Challenges of the Food–Water–Energy Nexus—A Participatory Approach for Pune and the Bhima Basin, India

   https://doi.org/10.3390/su14095323  

   Karutz, Raphael ; Omann, Ines ; Gorelick, Steven M. ; Klassert, Christian J. ; Zozmann, Heinrich ; Zhu, Yuanzao ; Kabisch, Sigrun ; Kindler, Annegret ; Figueroa, Anjuli Jain ; Wang, Ankun ; et al ( May 2022 , Sustainability)

   Systems models of the Food–Water–Energy (FWE) nexus face a conceptual difficulty: the systematic integration of local stakeholder perspectives into a coherent framework for analysis. We present a novel procedure to co-produce and systematize the real-life complexity of stakeholder knowledge and forge it into a clear-cut set of challenges. These are clustered into the Pressure–State–Response (PSIR) framework, which ultimately guides the development of a conceptual systems model closely attuned to the needs of local stakeholders. We apply this approach to the case of the emerging megacity Pune and the Bhima basin in India. Through stakeholder workshops, involving 75 resource users and experts, we identified 22 individual challenges. They include exogenous pressures, such as climate change and urbanization, and endogenous pressures, such as agricultural groundwater over-abstraction and land use change. These pressures alter the Bhima basin’s system state, characterized by inefficient water and energy supply systems and regional scarcity. The consequent impacts on society encompass the inadequate provision with food, water, and energy and livelihood challenges for farmers in the basin. An evaluation of policy responses within the conceptual systems model shows the complex cause–effect interactions between nexus subsystems. One single response action, such as the promotion of solar farming, can affectmore »multiple challenges. The resulting concise picture of the regional FWE system serves resource users, policymakers, and researchers to evaluate long-term policies within the context of the urban FWE system. While the presented results are specific to the case study, the approach can be transferred to any other FWE nexus system.« less
5. Federated Multi-Armed Bandits

   Shi, C ; Shen, C ( May 2021 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated learning (FL) framework in supervised learning. It is inspired by practical applications in cognitive radio and recommender systems, and enjoys features that are analogous to FL. This paper proposes a general framework of FMAB and then studies two specific federated bandit models. We first study the approximate model where the heterogeneous local models are random realizations of the global model from an unknown distribution. This model introduces a new uncertainty of client sampling, as the global model may not be reliably learned even if the finite local models are perfectly known. Furthermore, this uncertainty cannot be quantified a priori without knowledge of the suboptimality gap. We solve the approximate model by proposing Federated Double UCB (Fed2-UCB), which constructs a novel “double UCB” principle accounting for uncertainties from both arm and client sampling. We show that gradually admitting new clients is critical in achieving an O(log(T)) regret while explicitly considering the communication loss. The exact model, where the global bandit model is the exact average of heterogeneous local models, is then studied as a special case. We show that, somewhat surprisingly, the order-optimal regret can be achieved independentmore »of the number of clients with a careful choice of the update periodicity. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness and efficiency of the proposed algorithms.« less