Objective Although anecdotal evidence indicates the effectiveness of coronavirus disease 2019 (COVID-19) social-distancing policies, their effectiveness in relation to what is driven by public awareness and voluntary actions needs to be determined. We evaluated the effectiveness of the 6 most common social-distancing policies in the United States (statewide stay-at-home orders, limited stay-at-home orders, nonessential business closures, bans on large gatherings, school closure mandates, and limits on restaurants and bars) during the early stage of the pandemic. Methods We applied difference-in-differences and event-study methodologies to evaluate the effect of the 6 social-distancing policies on Google-released aggregated, anonymized daily location data on movement trends over time by state for all 50 states and the District of Columbia in 6 location categories: retail and recreation, grocery stores and pharmacies, parks, transit stations, workplaces, and residences. We compared the outcome of interest in states that adopted COVID-19–related policies with states that did not adopt such policies, before and after these policies took effect during February 15–April 25, 2020. Results Statewide stay-at-home orders had the strongest effect on reducing out-of-home mobility and increased the time people spent at home by an estimated 2.5 percentage points (15.2%) from before to after policies took effect. Limits on restaurants and bars ranked second and resulted in an increase in presence at home by an estimated 1.4 percentage points (8.5%). The other 4 policies did not significantly reduce mobility. Conclusion Statewide stay-at-home orders and limits on bars and restaurants were most closely linked to reduced mobility in the early stages of the COVID-19 pandemic, whereas the potential benefits of other such policies may have already been reaped from voluntary social distancing. Further research is needed to understand how the effect of social-distancing policies changes as voluntary social distancing wanes during later stages of a pandemic.

In the absence of a vaccine and effective antiviral medications, most of the non-pharmaceutical interventions focus on reducing social contact rates through different social distancing policies. However, the effectiveness of different policies and their relative impact vis-a-vis that of mechanisms driven by public awareness and voluntary actions have not been studied. This is crucial since in most places we observe significant reductions in social interaction before any policy was implemented. Variations in types and effective dates of different social distancing policies across different states in the US create a natural experiment to study the causal impact of each policy during the early stage of the outbreak. Using these policy variations and the aggregate human mobility and location trends published by Google for the month of March 2020, we employ a quasi-experimental approach to measure the impact of six common policies on people's presence at home and their mobility in different types of public places.Our results rank six common social distancing policies based on the magnitude and significance of their impact, beyond what has already been achieved through voluntary actions. They show that while strong policies such as statewide stay home mandate and non-essential business closure have strong causal impact on reducing social interactions, most of the expected impact of more lenient policies (such as large gathering ban and school closure mandates) are already reaped from non-policy mechanisms such as voluntary actions and public awareness.

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Cooperative behavior, which pervades nature, can be significantly enhanced when agents interact in a structured rather than random way; however, the key structural factors that affect cooperation are not well understood. Moreover, the role structure plays with cooperation has largely been studied through observing overall cooperation rather than the underlying components that together shape cooperative behavior. In this paper we address these two problems by first applying evolutionary games to a wide range of networks, where agents play the Prisoner's Dilemma with a three-component stochastic strategy, and then analyzing agent-based simulation results using principal component analysis. With these methods we study the evolution of trust, reciprocity and forgiveness as a function of several structural parameters. This work demonstrates that community structure, represented by network modularity, among all the tested structural parameters, has the most significant impact on the emergence of cooperative behavior, with forgiveness showing the largest sensitivity to community structure. We also show that increased community structure reduces the dispersion of trust and forgiveness, thereby reducing the network-level uncertainties for these two components; graph transitivity and degree also significantly influence the evolutionary dynamics of the population and the diversity of strategies at equilibrium.

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This paper introduces a conceptual, yet quantifiable, architecture framework by extending the notion of system modularity in its broadest sense. Acknowledging that modularity is not a binary feature and comes in various types and levels, the proposed framework introduces higher levels of modularity that naturally incorporate decentralized architecture on the one hand and autonomy in agents and subsystems on the other. This makes the framework suitable for modularity decisions in Systems of Systems and for analyzing the impact of modularity on broader surrounding ecosystems. The stages of modularity in the proposed framework are naturally aligned with the level of variations and uncertainty in the system and its environment, a relationship that is central to the benefits of modularity. The conceptual framework is complemented with a decision layer that makes it suitable to be used as a computational architecture decision tool to determine the appropriate stage and level of modularity of a system, for a given profile of variations and uncertainties in its environment. We further argue that the fundamental systemic driving forces and trade-offs of moving from monolithic to distributed architecture are essentially similar to those for moving from integral to modular architectures. The spectrum, in conjunction with the decision layer, could guide system architects when selecting appropriate parameters and building a system-specific computational tool from a combination of existing tools and techniques.To demonstrate the applicability of the framework, a case for fractionated satellite systems based on a simplified demo of the DARPA F 6 program is presented where the value of transition from a monolithic architecture to a fractionated architecture, as two consecutive levels of modularity in the proposed spectrum, is calculated and ranges of parameters where fractionation increases systems value are determined.