Risk analysis of disinformation weaponized against critical networks.

The vulnerability of critical networks to disinformation creates significant risks of disruption with potentially severe societal consequences. Maintaining secure and resilient networks, including infrastructure and supply chain networks, is important for ensuring economic productivity along with securing the health and well-being of society. An over-the-horizon threat to critical networks deals with adversaries who attack such networks indirectly by altering the consumption behavior of unwitting users influenced by weaponized disinformation.

Game-theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment.

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service.

Multiregional, multi-industry impacts of fairness on pandemic policies.

The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of state- and industry-level mitigation policies. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socioeconomic impact of control strategies.

Modeling social, economic, and health perspectives for optimal pandemic policy decision-making.

While different control strategies in the early stages of the COVID-19 pandemic have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative economic impact of control strategies. This paper proposes a novel multi-objective mixed-integer linear programming (MOMILP) formulation, which results in the optimal timing of closure and reopening of states and industries in each state to mitigate the economic and epidemiological impact of a pandemic.

Adaptive algorithm for dependent infrastructure network restoration in an imperfect information sharing environment.

Critical infrastructure networks are vital for a functioning society and their failure can have widespread consequences. Decision-making for critical infrastructure resilience can suffer based on several characteristics exhibited by these networks, including (i) that there exist interdependencies with other networks, (ii) that several decision-makers represent potentially competing interests among the interdependent networks, and (iii) that information about other decision-makers' actions are uncertain and potentially unknown. To address these concerns, we propose an adaptive algorithm using machine learning to integrate predictions about other decision-makers' behavior into an interdependent network restoration planning problem considering an imperfect information sharing environment.

Protecting infrastructure performance from disinformation attacks.

Disinformation campaigns are prevalent, affecting vaccination coverage, creating uncertainty in election results, and causing supply chain disruptions, among others. Unfortunately, the problems of misinformation and disinformation are exacerbated due to the wide availability of online platforms and social networks. Naturally, these emerging disinformation networks could lead users to engage with critical infrastructure systems in harmful ways, leading to broader adverse impacts.

Competitive percolation strategies for network recovery.

Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem of finding the optimal order of repairing elements in power grids and similar infrastructure. Most existing methods either only consider system network structure, potentially ignoring important features, or incorporate component level details leading to complex optimization problems with limited scalability.

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery.

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics-related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization-based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.

Interdependent Network Recovery Games.

Recovery of interdependent infrastructure networks in the presence of catastrophic failure is crucial to the economy and welfare of society. Recently, centralized methods have been developed to address optimal resource allocation in postdisaster recovery scenarios of interdependent infrastructure systems that minimize total cost. In real-world systems, however, multiple independent, possibly noncooperative, utility network controllers are responsible for making recovery decisions, resulting in suboptimal decentralized processes.