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1. Failures to Food, Energy, and Water Systems: Mapping and Simulating Components to Improve Resilience

   https://doi.org/10.1109/SmartNets58706.2023.10215865  

   Modarresi, Alex; Symons, John (July 2023, IEEE)

   This paper identifies common varieties of threats and perturbations in contemporary food, energy, and water (FEW) systems in order to improve system resilience. We categorize perturbations and challenges faced by subsystems and then concentrate on the structural topology of the project’s components. We provide a graph model to represent this topology as an essential tool to improve system resilience. The model is then converted to a system dynamic model for further simulation. 

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2. Resolving the Rules of Robustness and Resilience in Biology Across Scales

   https://doi.org/10.1093/icb/icab183  

   Crespi, Erica; Burnap, Robert; Chen, Jing; Das, Moumita; Gassman, Natalie; Rosa, Epaminondas; Simmons, Rebecca; Wada, Haruka; Wang, Zhen Q; Xiao, Jie; et al (August 2021, Integrative and Comparative Biology)

   Abstract Why do some biological systems and communities persist while others fail? Robustness, a system's stability, and resilience, the ability to return to a stable state, are key concepts that span multiple disciplines within and outside the biological sciences. Discovering and applying common rules that govern the robustness and resilience of biological systems is a critical step toward creating solutions for species survival in the face of climate change, as well as the for the ever-increasing need for food, health, and energy for human populations. We propose that network theory provides a framework for universal scalable mathematical models to describe robustness and resilience and the relationship between them, and hypothesize that resilience at lower organization levels contribute to robust systems. Insightful models of biological systems can be generated by quantifying the mechanisms of redundancy, diversity, and connectivity of networks, from biochemical processes to ecosystems. These models provide pathways towards understanding how evolvability can both contribute to and result from robustness and resilience under dynamic conditions. We now have an abundance of data from model and non-model systems and the technological and computational advances for studying complex systems. Several conceptual and policy advances will allow the research community to elucidate the rules of robustness and resilience. Conceptually, a common language and data structure that can be applied across levels of biological organization needs to be developed. Policy advances such as cross-disciplinary funding mechanisms, access to affordable computational capacity, and the integration of network theory and computer science within the standard biological science curriculum will provide the needed research environments. This new understanding of biological systems will allow us to derive ever more useful forecasts of biological behaviors and revolutionize the engineering of biological systems that can survive changing environments or disease, navigate the deepest oceans, or sustain life throughout the solar system. 

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3. Biodiversity in changing environments: An external‐driver internal‐topology framework to guide intervention

   https://doi.org/10.1002/ecy.4322  

   Suding, Katharine N; Collins, Courtney G; Hallett, Lauren M; Larios, Loralee; Brigham, Laurel M; Dudney, Joan; Farrer, Emily C; Larson, Julie E; Shackelford, Nancy; Spasojevic, Marko J (August 2024, Ecology)

   Abstract Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant–plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external‐driver internal‐topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments. 

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4. Deciphering the generating rules and functionalities of complex networks

   https://doi.org/10.1038/s41598-021-02203-4  

   Xiao, Xiongye; Chen, Hanlong; Bogdan, Paul (December 2021, Scientific Reports)

   Abstract Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution. 

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5. Rethinking Resilience Analytics

   https://doi.org/10.1111/risa.13328  

   Eisenberg, Daniel; Seager, Thomas; Alderson, David L. (May 2019, Risk Analysis)

   Abstract The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise. 

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