Saikou Y. Diallo

The practice of deceased-donor solid-organ transplantation is unique among clinical therapies because it substantially benefits recipients, The allocation process is a complex System-of-Systems (SoS) with multiple stakeholders, components, rules, and policies. While we model and simulate such SoS, they are challenging to verify and validate thoroughly due to a large number of use cases and the sensitivity to initial conditions. Furthermore, subject matter experts within communities of practice do not trust such models because they are difficult to explain. This paper proposes a trust-centric approach to validation focusing on experimentally exploring the simulation’s behavior space and demonstrating its utility to a community of experts. We describe a three-step validation process to show how modelling and simulation professionals can foster trust in simulations of a complex SoS. We apply the framework to verify and validate a simulation model of the Kidney–Pancreas (KP) allocation process within the United States.

We present a perspective of the national transplant program based on organizational theory and complexity theory, framing the system’s allocation of donor organs as an interorganizational directed multiplex of agents with diverse belief formation in a cooperative‐competitive environment. Simulation and analysis of this macroscale complexity may help explain known behavioural variations across member organizations. However, the transplant community still relies on system‐scale simulations since effective macroscale methodologies are not well established. Therefore, we offer this perspective of the national transplant program as a means to stimulate new methods that capture macroscale impacts of policy development for deceased donor organ allocation.

A significant therapeutic challenge for people with disabilities is the development of verbal and echoic skills. Digital voice assistants (DVAs), such as Amazon’s Alexa, provide networked intelligence to billions of Internet-of-Things devices and have the potential to offer opportunities to people, such as those diagnosed with autism spectrum disorder (ASD), to advance these necessary skills. Voice interfaces can enable children with ASD to practice such skills at home; however, it remains unclear whether DVAs can be as proficient as therapists in recognizing utterances by a developing speaker. We developed an Alexa-based skill called ASPECT to measure how well the DVA identified verbalization by autistic children. The participants, nine children diagnosed with ASD, each participated in 30 sessions focused on increasing vocalizations and echoic responses. Children interacted with ASPECT prompted by instructions from an Echo device. ASPECT was trained to recognize utterances and evaluate them as a therapist would—simultaneously, a therapist scored the child’s responses. The study identified no significant difference between how ASPECT and the therapists scored participants; this conclusion held even when subsetting participants by a pre-treatment echoic skill assessment score. This indicates considerable potential for providing a continuum of therapeutic opportunities and reinforcement outside of clinical settings.

Introduction: COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses. Methods: An agent-based model of COVID-19 was developed with individual agents able to move between 3 states (susceptible, infectious, or recovered), with each agent placed in 1 of 7 social network types and assigned a propensity to comply with NPIs (quarantine, contact tracing, and physical distancing). A series of policy questions were tested to illustrate the impact of social networks and NPI compliance on viral spread among (1) populations, (2) specific at-risk subgroups, and (3) individual trajectories. Results: Simulation outcomes showed large impacts of physical distancing policies on number of infections, with substantial modification by type of social network and level of compliance. In addition, outcomes on metrics that sought to maximize those never infected (or recovered) and minimize infections and deaths showed significantly different epidemic trajectories by social network type and among higher or lower at-risk age cohorts. Conclusions: Although dynamic simulation models have important limitations, which are discussed, these decision support tools should be a key resource for navigating the ongoing impacts of the COVID-19 pandemic and can help local and national decision makers determine where, when, and how to invest resources.

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.

Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artificial societies, estimations of downstream policy effectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges. In this paper, we supply evidence that incorporating morally salient dimensions of a culture is critically important for producing relevant and accurate evaluations of social policy when using multi-agent artificial intelligence models and simulations.