ABSTRACT
Today's challenges must be addressed as socio-technical systems, including insights from the social sciences and humanities to adequately represent the human components. As results of simulations are increasingly driving and justifying political and social decisions, it is important to validate and verify (VV) simulation and data. However, the understanding of what establishes truth and how these views impact validation differ between the social and technical partners. Therefore, we must expand our view of VV. The panel provides various use cases and derives ethical questions related to supporting universities during the COVID-19 pandemic, creating multi-disciplinary teams with diverse viewpoints, challenges of using validated insights without critical evaluation, and lack of broadly accepted scientific measures to connect social models and empirical data. We conclude that the role of VV must be reemphasized, that its social-theoretical implications must be better understood, and that it should be driven by an overarching metaethical framework. © 2021 IEEE.
ABSTRACT
Operating universities under pandemic conditions is a complex undertaking. The Artificial University (TAU) responds to this need. TAU is a configurable, open-source computer simulation of a university using a contact network based on publicly available information about university classes, residences, and activities. This study evaluates health outcomes for an array of interventions and testing protocols in an artificial university of 6,500 students, faculty, and staff. Findings suggest that physical distancing and centralized contact tracing are most effective at reducing infections, but there is a tipping point for compliance below which physical distancing is less effective. If student compliance is anything short of high, it helps to have separate buildings for quarantining infected students, thereby gracefully increasing compliance. Hybrid in-person and online classes and closing fitness centers do not significantly change cumulative infections but do significantly decrease the number of the infected at any given time, indicating strategies for "flattening the curve" to protect limited resources. Supplementing physical distancing with centralized contact tracing decreases infected individuals by an additional 14%;boosting frequency of testing for student-facing staff yields a further 7% decrease. A trade-off exists between increasing the sheer number of infection tests and targeting testing for key nodes in the contact network (i.e., student-facing staff). There are significant advantages to getting and acting on test results quickly. The costs and benefits to universities of these findings are discussed. Artificial universities can be an important decision support tool for universities, generating useful policy insights into the challenges of operating universities under pandemic conditions.