With this more diffuse spread of the disease and early warning from experience out of China, we could be implementing their late-stage practices right up front. Require people to check-in/check-out of each public gathering space, have them record their names and times when the enter and leave, and record their responses to health questions along with a quick temperature reading.
I don’t think the rapid declaration of emergencies and the cascading declarations at every state and local government was a prudent decision. I would like to imagine a dedomenocracy would have come up with a wiser plan of action. A wiser plan of action would be to be much more selective about declaration of emergency and focused in such a way to minimize the impact.
The more likely scenario at this time is that a lot of vulnerable people will get the illness, many will have the complications that will overwhelm the medical systems, and many will die. If we continue to quarantine our potential heroes, the situation will be far worse with rapidly declining medical capacity or even a rapidly declining carrying capacity for the entire population.
Unless we permit the algorithms to project the trends into the future, thus inventing modeled data for future values, the algorithms would conclude on policies that may not be distinguishable from the current politically biased decision making. Government by data must permit dark data, data generated by models, as equals to bright data of trusted observations.
Much attention is spent on the job-loss implications of introduction of automation to improve productivity. Meanwhile, automation is also used for job-preservation of older workers in outdated yet still essential practices, and this too has some unfortunate implications for the future. Eventually the simulation of an earlier age will fail catastrophically in the fact that that age no longer exists. Alternatively, eventually we will run out of older workers who can work in that simulation.
Automation is needed for the operations productivity, but it adds new labor burdens on humans whose incentive of self-protection from automation drives him to pay attention to the credibility of the sensors in terms of correspondence to what it is supposedly measuring. The problem might be solved by live streaming of sensor observations of physical world back to some operations control center where a small staff can monitor all currently operating systems. This would require a data network to handle the data traffic in an timely manner, but I believe this is the right solution if it is feasible. Such as center would validate each sensor’s information against all information about the aircraft’s environment, and they would have the means to directly initiate the process to remove the bad sensor and mitigate its subsequent absence. Such a system also would allow the periodic review of more detailed sensor data collected from all operations to find anomalies that would identify the misbehaving automation, flagging it for engineering or certification review.
I suspect the agile paradigm has taken over the aircraft industry, or at least in Boeing and the FAA. The MCAS was a sprint for the 737 Max, and the 737 Max was a sprint to incorporate the new engine. There was a lot of engineering and testing in these sprints: these sprints took a long time. However, the fundamental thinking changed from a requirements-first approach to an agile approach that seeks to fail quickly.
In this increasingly globalized world, we confront the question of how well groups may govern themselves optimally not only to survive within their local context but also keep up with the standards of the global norms. The goal is for self government but that government should thrive at a comparable level to the global norm. What is that measurement that will determine a group’s success at self-governing?
Dedomenology has a saturation aspect, requiring very long periods of work stretching over many days regardless of the concepts of standard working hours such as a 40 hour workweek. When something needs to be tackled, it will employ the dedomenologist continuously until there is some level of completion. There will be an endless stream of assignments that someone will need to dive into the depths of the data ocean and staying there for a long time until the assignment is over.
One of the advantages of machine intelligence over human intelligence is that machines are not driven toward poetry. To me, poetry captures the scientific appreciation for the simplest explanations with the fewest number of terms. Humans are innately poets by nature, and even the objectivity of science can not escape the human delight in well-crafted poetry, or human disdain for inelegance in descriptions.
