Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic.

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.

Minding morality: ethical artificial societies for public policy modeling.

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.

A neurocomputational theory of nightmares: the role of formal properties of nightmare images.

Study Objectives: To test and extend Levin & Nielsen's (2007) Affective Network Dysfunction (AND) model with nightmare disorder (ND) image characteristics, and then to implement the extension as a computational simulation, the Disturbed Dreaming Model (DDM). Methods: We used AnyLogic V7.2 to computationally implement an extended AND model incorporating quantitative effects of image characteristics including valence, dominance, and arousal. We explored the DDM parameter space by varying parameters, running approximately one million runs, each for one month of model time, varying pathway bifurcation thresholds, image characteristics, and individual-difference variables to quantitively evaluate their combinatory effects on nightmare symptomology. Results: The DDM shows that the AND model extended with pathway bifurcations and image properties is computationally coherent. Varying levels of image properties, we found that when nightmare images exhibit lower dominance and arousal levels, the ND agent will choose to sleep but then has a traumatic nightmare, whereas, when images exhibit greater than average dominance and arousal levels, the nightmares trigger sleep-avoidant behavior, but lower overall nightmare distress at the price of exacerbating nightmare effects during waking hours. Conclusions: Computational simulation of nightmare symptomology within the AND framework suggests that nightmare image properties significantly influence nightmare symptomology. Computational models for sleep and dream studies are powerful tools for testing quantitative effects of variables affecting nightmare symptomology. The DDM confirms the value of extending the Levin & Nielsen AND model of disturbed dreaming/ND.

A potential explanation for self-radicalisation.

We believe that Whitehouse's model could be extended in a way that can help us make sense of self-radicalised individuals who are not active in cliques. We believe that conceptual ties may be important to this process and present a brief analysis of a database collected by the national consortium for the Study of Terrorism and Responses to Terrorism (START), to suggest future research to complement Whitehouse's proposal.

Impacts of religious semantic priming on an intertemporal discounting task: Response time effects and neural correlates.

The purpose of this study is to test the hypothesis that religious primes would influence intertemporal discounting behaviors in neurotypical older adults, but not in participants with Parkinson's disease (PD). Furthermore, we predicted that this priming effect would be related to functional connectivity within neural networks mediating religious cognition, decision-making, reward valuing, and prospection processes. Contrary to past research with young adults, we found a significant positive relationship between religiosity and discounting rates. Religious semantic primes did not reliably shift individual discounting rates. But religious controls did respond more quickly to intertemporal decisions under the religious priming condition than the neutral condition, compared to response time differences among the participants with PD. Differences in response time were significantly associated with functional connectivity between the nucleus accumbens and various regions, including the left anterior cingulate cortex and Brodmann areas 10 and 46 in the right dorsolateral prefrontal cortex. These results suggest that religious primes influence discounting behavior via dopaminergic meso-limbic and right dorsolateral prefrontal supporting cognitive valuation and prospection processes.