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"What these examples have in common is that they illustrate two of the key tasks of the social sciences: to measure important social variables over time and space, and to identify the social mechanisms that lead to variation in these variables. There are large problems of methodology and conceptual clarification that need to be addressed in both parts of this agenda. On the side of measurement, we have the problems of arriving at consistent and revealing definitions of economic wellbeing, using incomplete historical sources to reconstruct estimates of prices and wages, and using a range of statistical methods to validate and interpret the results. And on the explanatory side, we are faced with the difficult task of reconstructing social processes and forces in the past that may have powered the changes we are able to document, and with the task of validating the hypotheses we have put forward on the basis of historical evidence."

Mandates for mask use in public during the recent COVID-19 pandemic, worsened by global shortage of commercial supplies, have led to widespread use of homemade masks and mask alternatives. It is assumed that wearing such masks reduces the likelihood for an infected person to spread the disease, but many of these mask designs have not been tested in practice. We have demonstrated a simple optical measurement method to evaluate the efficacy of masks to reduce the transmission of respiratory droplets during regular speech. In proof-of-principle studies, we compared a variety of commonly available mask types and observed that some mask types approach the performance of standard surgical masks, while some mask alternatives, such as neck fleece or bandanas, offer very little protection. Our measurement setup is inexpensive and can be built and operated by non-experts, allowing for rapid evaluation of mask performance during speech, sneezing, or coughing.

Physicists who think carefully about time point to troubles posed by quantum mechanics, the laws describing the probabilistic behavior of particles. At the quantum scale, irreversible changes occur that distinguish the past from the future: A particle maintains simultaneous quantum states until you measure it, at which point the particle adopts one of the states. Mysteriously, individual measurement outcomes are random and unpredictable, even as particle behavior collectively follows statistical patterns. This apparent inconsistency between the nature of time in quantum mechanics and the way it functions in relativity has created uncertainty and confusion.

Since the emergence of coronavirus disease 2019 (COVID-19), unprecedented movement restrictions and social distancing measures have been implemented worldwide. The socioeconomic repercussions have fueled calls to lift these measures. In the absence of population-wide restrictions, isolation of infected individuals is key to curtailing transmission. However, the effectiveness of symptom-based isolation in preventing a resurgence depends on the extent of presymptomatic and asymptomatic transmission. We evaluate the contribution of presymptomatic and asymptomatic transmission based on recent individual-level data regarding infectiousness prior to symptom onset and the asymptomatic proportion among all infections. We found that the majority of incidences may be attributable to silent transmission from a combination of the presymptomatic stage and asymptomatic infections. Consequently, even if all symptomatic cases are isolated, a vast outbreak may nonetheless unfold. We further quantified the effect of isolating silent infections in addition to symptomatic cases, finding that over one-third of silent infections must be isolated to suppress a future outbreak below 1% of the population. Our results indicate that symptom-based isolation must be supplemented by rapid contact tracing and testing that identifies asymptomatic and presymptomatic cases, in order to safely lift current restrictions and minimize the risk of resurgence.

COVID-19 is a wicked problem for policy makers internationally as the complexity of the pandemic transcends health, environment, social and economic boundaries. Many countries are focusing on two key responses, namely virus containment and financial measures, but fail to recognise other aspects. The systems approach, however, enables policy makers to design the most effective strategies and reduce the unintended consequences. To achieve fundamental change, it is imperative to firstly identify the "right" interventions (leverage points) and implement additional measures to reduce negative consequences. To do so, a preliminary causal loop diagram of the COVID-19 pandemic was designed to explore its influence on socio-economic systems. In order to transcend the "wait and see" approach, and create an adaptive and resilient system, governments need to consider "deep" leverage points that can be realistically maintained over the long-term and cause a fundamental change, rather than focusing on "shallow" leverage points that are relatively easy to implement but do not result in significant systemic change

"As AI systems become more general and more useful in the real world, ensuring they behave safely will become even more important. To date, the majority of technical AI safety research has focused on developing a theoretical understanding about the nature and causes of unsafe behaviour. Our new paper builds on a recent shift towards empirical testing (see Concrete Problems in AI Safety) and introduces a selection of simple reinforcement learning environments designed specifically to measure 'safe behaviours'."

"his paper takes stock of megaproject management, an emerging and hugely costly field of study. First, it answers the question of how large megaprojects are by measuring them in the units mega, giga, and tera, concluding we are presently entering a new "tera era" of trillion-dollar projects. Second, total global megaproject spending is assessed, at USD 6-9 trillion annually, or 8 percent of total global GDP, which denotes the biggest investment boom in human history. Third, four "sublimes" - political, technological, economic, and aesthetic - are identified to explain the increased size and frequency of megaprojects. Fourth, the "iron law of megaprojects" is laid out and documented: Over budget, over time, over and over again. Moreover, the "break-fix model" of megaproject management is introduced as an explanation of the iron law. Fifth, Albert O. Hirschman's theory of the Hiding Hand is revisited and critiqued as unfounded and corrupting for megaproject thinking in both the academy and policy. Sixth, it is shown how megaprojects are systematically subject to "survival of the unfittest," explaining why the worst projects get built instead of the best. Finally, it is argued that the conventional way of managing megaprojects has reached a "tension point," where tradition is challenged and reform is emerging. "

How do statistics so often lead scientists to deny differences that those not educated in statistics can plainly see? For several generations, researchers have been warned that a statistically non-significant result does not 'prove' the null hypothesis (the hypothesis that there is no difference between groups or no effect of a treatment on some measured outcome)1. Nor do statistically significant results 'prove' some other hypothesis. Such misconceptions have famously warped the literature with overstated claims and, less famously, led to claims of conflicts between studies where none exists.