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Russian cyberattacks on Ukraine largely failed to produce meaningful outcomes not merely due to robust Ukrainian cyber defenses but were instead primarily a result of Ukraine's effective cyber resilience.

The term "cyber resilience by design" is growing in popularity. Here, by cyber resilience we refer to the ability of the system to resist, minimize and mitigate a degradation caused by a successful cyber-attack on a system or network of computing and communicating devices. Some use the term "by design" when arguing that systems must be designed and implemented in a provable mission assurance fashion, with the system's intrinsic properties ensuring that a cyber-adversary is unable to cause a meaningful degradation. Others recommend that a system should include a built-in autonomous intelligent agent responsible for thinking and acting towards continuous observation, detection, minimization and remediation of a cyber degradation. In all cases, the qualifier "by design" indicates that the source of resilience is somehow inherent in the structure and operation of the system. But what, then, is the other resilience, not by design? Clearly, there has to be another type of resilience, otherwise what's the purpose of the qualifier "by design"? Indeed, while mentioned less frequently, there exists an alternative form of resilience called "resilience by intervention." In this article we explore differences and mutual reliance of resilience by design and resilience by intervention.

To benefit from AI advances, users and operators of AI systems must have reason to trust it. Trust arises from multiple interactions, where predictable and desirable behavior is reinforced over time. Providing the system's users with some understanding of AI operations can support predictability, but forcing AI to explain itself risks constraining AI capabilities to only those reconcilable with human cognition. We argue that AI systems should be designed with features that build trust by bringing decision-analytic perspectives and formal tools into AI. Instead of trying to achieve explainable AI, we should develop interpretable and actionable AI. Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. In doing so, it will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making and ensure broad benefits from deploying and advancing its computational capabilities.

State governments in the U.S. have been facing difficult decisions involving tradeoffs between economic and health-related outcomes during the COVID-19 pandemic. Despite evidence of the effectiveness of government-mandated restrictions mitigating the spread of contagion, these orders are stigmatized due to undesirable economic consequences. This tradeoff resulted in state governments employing mandates in widely different ways. We compare the different policies states implemented during periods of restriction (lockdown) and reopening with indicators of COVID-19 spread and consumer card spending at each state during the first wave of the pandemic in the U.S. between March and August 2020. We find that while some states enacted reopening decisions when the incidence rate of COVID-19 was minimal or sustained in its relative decline, other states relaxed socioeconomic restrictions near their highest incidence and prevalence rates experienced so far. Nevertheless, all states experienced similar trends in consumer card spending recovery, which was strongly correlated with reopening policies following the lockdowns and relatively independent from COVID-19 incidence rates at the time. Our findings suggest that consumer card spending patterns can be attributed to government mandates rather than COVID-19 incidence in the states. We estimate the recovery in states that reopened in late April was more than the recovery in states that did not reopen in the same period - 15% for consumer card spending and 18% for spending by high income households. This result highlights the important role of state policies in minimizing health impacts while promoting economic recovery and helps planning effective interventions in subsequent waves and immunization efforts., Comment: Revised on October 4, 2021

With the emerging COVID-19 crisis, a critical task for public health officials and policy makers is to decide how to prioritize, locate, and allocate scarce resources. To answer these questions, decision makers need to be able to determine the location of the required resources over time based on emerging hot spot locations. Hot spots are defined as concentrated areas with sharp increases in COVID19 cases. Hot spots place stress on existing healthcare resources, resulting in demand for resources potentially exceeding current capacity. This research will describe a value based resource allocation approach that seeks to coordinate demand, as defined by uncertain epidemiological forecasts, with the value of adding additional resources such as hospital beds. Value is framed as a function of the expected usage of a marginal resource (bed, ventilator, etc). Subject to certain constraints, allocation decisions are operationalized using a nonlinear programming model, allocating new hospital beds over time and across a number of geographical locations. The results of the research show a need for a value based approach to assist decision makers at all levels in making the best possible decisions in the current highly uncertain and dynamic COVID environment., Comment: in Trump, B, Keenan, J., Linkov, I "COVID-19: Systemic Risk and Resilience" Springer, in press

The COVID19 pandemic has highlighted the lack of resilience in supply chains, as global networks fail from disruptions at single nodes and connections. Through an overview of the existing vaccine and pharmaceutical supply chain publications focusing on resilience, as well as recent papers reporting modeling of resilience in supply chains across multiple fields, we find that models for supply chain resilience are few and most of them are focused on individual dimensions of resilience rather than on comprehensive strategy necessary for scaling up vaccine production and distribution in emergency settings. We find that COVID19 resulted in a wave of interest to supply chain resilience, but publications from 2020 are narrow in focus and largely qualitative in nature; evidence-based models and measures are rare. Further, publications often focus exclusively on specific portions of the specific supply chain of interest, excluding associated supporting networks, such as transportation, social and command and control (C2) necessary for vaccine production and equitable distribution. This lack of network analysis is a major gap in the literature that needs to be bridged in order to create methods of real-time analysis and decision tools for the COVID19 vaccine supply chain. We conclude that a comprehensive, quantitative approach to network resilience that encompasses the supply chain in the context of other social and physical networks is needed in order to address the emerging challenges of a large-scale COVID-19 vaccination program. We further find that the COVID-19 pandemic underscores the necessity of positioning supply chain resilience within a multi-network context and formally incorporating temporal dimensions into analysis through the NAS definition of resilience, plan, absorb, recover, adapt, to ensure essential needs are met across all dimensions of society., Comment: in Trump, B., Keenan, J., Linkov, I. "COVID-19: ystemic Risk and Resilience" Springer (in press)

Desirable system performance in the face of threats and disruptions has been characterized by various management concepts. Through semi-structured interviews with editors of journals in the fields of emergency response and systems management, a literature review, and professional judgment, we identified nine related and often interchangeably-used system performance concepts: adaptability, agility, reliability, resilience, resistance, robustness, safety, security, and sustainability. We analysed expert responses and reviewed the linguistic definitions and mathematical framing of these concepts to understand their applications. We found a lack of consensus on their usage between interview subjects, but using a mathematical framing enriched the linguistic definitions and enabled formulating comparative visualizations and system-specific definitions for the concepts. We propose a conceptual framing to relate the concepts for management purposes. A better understanding of these concepts will allow system planners to pursue management strategies best suited to their unique system dynamics and specific objectives of 'goodness' that all these concepts bring., Comment: under review in Risk Analysis