Critical Infrastructure Systems (CISs) are interdependent to maintain normal operations of the nation-wide economy and social well-being. Due to climate change and the growing population, the occurrence of natural hazards has increased. Recent worldwide events have highlighted that interdependencies within CISs increase the potential for cascading failures and amplify the impact of both large and small-scale initial failures into events of catastrophic proportions. Such interdependencies may cause additional vulnerability and cascading failures in a disaster event. Therefore, identifying and classifying interdependencies and assessing their impact is crucial to prevent or mitigate such unfavorable consequences and enhance resilience in the long term. To better understand CISs for planning, maintenance, and emergency decision-making, modeling and simulation of interdependencies across CISs has recently become an essential field of study. However, more technical guidance and consistent terminology are needed to ensure practical applications of interdependency models. In this study, we compiled and identified classifications of interdependencies and described a new comprehensive classification. Expanding our understanding of how critical infrastructure systems operate in concert is essential to anticipate potential disruptions, manage the impacts, and develop adaptation measures for future conditions.

Critical infrastructure interdependencies are fundamental when assessing the resilience of infrastructure, systems, and communities they serve. Due to the importance of their secure and reliable operations, understanding the behavior of CISs – particularly when stressed or under attack – is essential. Models and simulations have the potential to provide substantial insight into the complex nature of their behaviors and operational characteristics. Several modeling and simulation approaches under development today directly address interdependencies and offer considerable insight into critical infrastructures' operational and behavioral characteristics. We reviewed the existing literature and catalogued existing modeling and simulation approaches. For each type of interdependency model, fundamental assumptions and detailed implementation methods are discussed, explaining precise application areas for stakeholders, advantages, and limitations/opportunities. This study helps decision-makers and stakeholders plan, design, and operate more effectively and efficiently and act promptly and effectively to protect infrastructure systems from cascading failures. The findings of this study will help identify and prioritize potential resilience strategies in the broader perspective of long-term adaptation planning and sustainable development.

The 2022 Southwest Airlines Scheduling Crisis, resulting in approximately 15,000 flight cancellations, demonstrates the challenges of structuring infrastructure systems and their knowledge-making processes for increasingly disruptive conditions. While the point-to-point configuration was the focus of immediate assessments of the failure, it became rapidly evident that the crew-assignment software was unable to operate effectively due to the scale of disruption. The airline failed to recognize environmental shifts associated with internal and external complexity, leaving operations vulnerable to a known potential risk: computer and telecommunications failures due to an extreme weather event resulting in knowledge systems failures. The cascading failures of the crisis emphasize the necessity to invest in adaptive capacity prior to catastrophic events and provide a lesson to other infrastructure managers pursuing resilience in the face of increasingly uncertain environments.

The presentation will also cover some material about de/centralization from 'Centralization and decentralization for resilient infrastructure and complexity (DOI: 10/1088/2634-4505/ac0a4f)' which serves as a foundation of the perspective case study explored above. The abstract of that foundational work is listed below for reference:

The capacities of our infrastructure systems to respond to volatile, uncertain, and increasingly complex environments are increasingly recognized as vital for resilience. Pervasive across infrastructure literature and discourse are the concepts of centralized, decentralized, and distributed systems, and there appears to be growing interest in how these configurations support or hinder adaptive and transformative capacities towards resilience. There does not appear to be a concerted effort to align how these concepts are used, and what different configurations mean for infrastructure systems. This is problematic because how infrastructure are structured and governed directly affects their capabilities to respond to increasing complexity. We review framings of centralization, decentralization, and distributed (referred to collectively as de/centralization) across infrastructure sectors, revealing incommensurate usage leading to polysemous framings. De/centralized networks are often characterized by proximity to resources, capacity of distribution, volume of product, and number of connections. De/centralization of governance within infrastructure sectors is characterized by the number of actors who hold decision-making power. Notably, governance structures are often overlooked in infrastructure de/centralization literature. Next, we describe how de/centralization concepts are applied to emerging resilient infrastructure theory, identifying conditions under which they support resilience principles. While centralized systems are dominant in practice and decentralized systems are promoted in resilience literature, all three configurations—centralized, decentralized, and distributed—were found to align with resilience capacities in various contexts of stability and instability. Going forward, we recommend a multi-dimensional framing of de/centralization through a network-governance perspective where capabilities to shift between stability and instability are paramount and information is a critical mediator.

Fueled by climate change and policies that value property over livelihoods, there is an emergent and compounding problem regarding natural disaster losses in the US. It is indisputable that the costs and burdens of climate change impacts will not be borne equally by individuals in society. Losses due to disasters are only increasing and society will be faced with how to pay for the damages. Once a disaster surpasses the capacity of state and local governments to respond adequately, the federal government, through the Federal Emergency Management Agency (FEMA), provides disaster management and aid. While capacity of counties is known to be quite heterogeneous, the federal government treats local capacity as being equal. This research aims to try to identify patterns of aid allocation with respect to various economic and disaster parameters.

This research adds to the literature through the development of two new disaster resilience metrics, average Burden, and average Donor-Donee Ratio, each defined at the county level over the years 2010-2019 for the contiguous United States. Average Burden factors in individual county GDP and information tabulated from FEMA’s National Risk Index. This metric can be utilized to understand how the proportion of expected disaster losses compares to its overall economic viability, another way of viewing coping capacity. Average Donor-Donee ratio factors in a county's grants from FEMA’s Public Assistance (PA), Individual Assistance (IA), and Hazard Mitigation Grant Programs. This metric provides a benchmark comparison for countys’ aggregate disaster funding to the federal income taxes paid to the government, capturing financial redistribution of federal dollars. This research finds that there are in fact spatial patterns in both metrics across the US.

Furthermore, machine learning provides a descriptive analysis of relative coping capacities and federal aid redistribution through the ten-year snapshot this analysis provides. A partitioning clustering algorithm, k-medoids was applied to the tabulated average Donor-Donee Ratio and average Burden metrics with the addition of a population control. A high-level overview of clustering results shows that most counties are classified by moderately reduced coping capacity (Burden) and are associated with higher-than-average disaster aid redistribution (Donor-Donee Ratio). These results indicate that the federal disaster aid system is working as intended. Although, this research opens the conversation into the relationship between beneficiaries of federal disaster funding and the inherent coping capacity of counties. Sensitivity analyses including the addition of disaster frequency to the model yield additional insights into funding allocation with respect to this dimension. This research provides a starting point for future studies that are conducted at a finer granularity may begin to understand why these spatial patterns emerged in the first place.

Our urban systems and their underlying sub-systems are designed to deliver only a narrow set of human-centered services, with little or no accounting or understanding of how actions undercut the resilience of social-ecological-technological systems (SETS). Embracing a SETS resilience perspective creates opportunities for novel approaches to adaptation and transformation in complex environments. We: i) frame urban systems through a perspective shift from control to entanglement, ii) position SETS thinking as novel sensemaking to create repertoires of responses commensurate with environmental complexity (i.e., requisite complexity), and iii) describe modes of SETS sensemaking for urban system structures and functions as basic tenets to build requisite complexity. SETS sensemaking is an undertaking to reflexively bring sustained adaptation, anticipatory futures, loose-fit design, and co- governance into organizational decision-making and to help reimagine institutional structures and processes as entangled SETS.

Centralized health networks in the United States were designed to a provide high-quality, consistent standard of care. But as technology improves and inter-network communication becomes easier and easier, we must look at new ways to provide equitable, high-quality healthcare to as much of the population as possible. As we consider how to do this, we must also minimize the environmental impact of providing these services to prevent exacerbation of climate change and the associated health impacts. One potential way to minimize the impact of improved health services is to consider what technologies are available as point-of-care platforms. Specifically, point-of-care biomedical imaging technologies are rapidly evolving to not only image but use machine learning to extract medical metrics. Therefore, one way to reduce the greenhouse gas (GHG) impact of centralized health networks is to offload some of the routine imaging capabilities to decentralized networks through these technologies.

In this work, we selected one technology and one demographic region as a starting point for assessing the potential for decentralization of echocardiograms (ECHO), with the goal that this can serve as a framework for assessing a larger scope of healthcare decentralization. We identified Milwaukee, WI as the location for this study because of the available health and transportation data provided by the local health networks and state agencies. ECHO scans were selected as the target scan to assess because they are conducted with ultrasound technology, which is available at many point-of-use scales. The ButterflyIQ system, which is an ultrasound device that can be plugged in to an iPhone, was selected for this analysis because of its market-readiness and push towards incorporating diagnostics. The decentralized network was identified through current out-patient imaging facilities that do not currently conduct ECHOs in the specified area. Our results are expected to compare the GHG emissions associated with business-as-usual travel for ECHO scans in Milwaukee, WI to the GHG emissions associated with our established decentralized network. Future work will assess how the life-cycle impact of the technologies will impact these results.