Managing pandemics as super wicked problems: lessons from, and for, COVID-19 and the climate crisis

COVID-19 has caused 100s of millions of infections and millions of deaths worldwide, overwhelming health and economic capacities in many countries and at multiple scales. The immediacy and magnitude of this crisis has resulted in government officials, practitioners and applied scholars turning to reflexive learning exercises to generate insights for managing the reverberating effects of this disease as well as the next inevitable pandemic. We contribute to both tasks by assessing COVID-19 as a “super wicked” problem denoted by four features we originally formulated to describe the climate crisis: time is running out, no central authority, those causing the problem also want to solve it, and policies irrationally discount the future (Levin et al. in Playing it forward: path dependency, progressive incrementalism, and the “super wicked” problem of global climate change, 2007; Levin et al. in Playing it forward: Path dependency, progressive incrementalism, and the "super wicked" problem of global climate change, 2009; Levin et al. in Policy Sci 45(2):123–152, 2012). Doing so leads us to identify three overarching imperatives critical for pandemic management. First, similar to requirements to address the climate crisis, policy makers must establish and maintain durable policy objectives. Second, in contrast to climate, management responses must always allow for swift changes in policy settings and calibrations given rapid and evolving knowledge about a particular disease’s epidemiology. Third, analogous to, but with swifter effects than climate, wide-ranging global efforts, if well designed, will dramatically reduce domestic costs and resource requirements by curbing the spread of the disease and/or fostering relevant knowledge for managing containment and eradication. Accomplishing these tasks requires building the analytic capacity for engaging in reflexive anticipatory policy design exercises aimed at maintaining, or building, life-saving thermostatic institutions at the global and domestic levels.

A Theoretical Model for the Self-Noise of a Velocity-Broadband Seismometer

ABSTRACT A theoretical model of the seismometer self-noise can be used to predict the self-noise. Its inputs are mechanical and electrical parameters of the seismometer. In this article, we studied the theoretical model of the self-noise of the velocity-broadband seismometer, using the CS60 seismometer as an example. Velocity-broadband seismometer is a type of feedback seismometer. The previous theoretical model of the self-noise of the feedback seismometer only involved noise sources in the forward path of the feedback system. Our model involved not only noise sources in the forward path, but also noise sources in the feedback path and external to the feedback loop. We introduced noise sources in the feedback system of the seismometer and the method of calculating their levels with mechanical and electrical parameters, developed expressions for referring all noise sources to the input terminal of the feedback system, and finally established the model. We compared the CS60 seismometer’s predicted self-noise calculated using this model with the measured self-noise, and we found good agreement between them. The contribution of each noise source to the total noise was studied, and it was found that the dominant noise source in CS60 is different over different frequency bands. Over the frequency band below 0.0095 Hz, the noise of the integrator (in the feedback path) is the dominant noise source. Over the frequency band from 0.0095 to 0.21 Hz, suspension noise is the dominant noise source. Over the frequency band from 0.21 to 39 Hz, the noise of the differential driver (external to the feedback loop) is the dominant noise source. Over the frequency band above 39 Hz, the noise of the preamplifier (in the forward path) is the dominant noise source. In addition, some viewpoints about the low-noise design of seismometers were proposed.

Automatically Building Linking Relations between Lane-Level Map and Commercial Navigation Map Using Topological Networks Matching

The lane-level map, which contains the lane-level information severely lacking in widely used commercial navigation maps, has become an essential data source for autonomous driving systems. The linking relations between lane-level map and commercial navigation map can facilitate an autonomous driving system mapping information between different applications using different maps. In this paper, an approach is proposed to build the linking relations automatically. The different topology networks are first reconstructed into similar structures. Then, to build the linking relations automatically, the adaptive multi-filter algorithm and forward path exploring algorithm are proposed to detect corresponding junctions and paths, respectively. The approach is validated by two real data sets of more than 150 km of roads, mainly highway. The linking relations for nearly 94% of the total road length have been built successfully.