There is a case that the first prohibition failed because it was based on a moral justification and people can argue about what is moral or not. This second prohibition is on more solid ground based on a scientific justification. The justification now is scientific assurance that everyone will end up knowing someone who will die from this disease if the disease doesn’t kill them. We can’t deny science.
This navigation reminds me of the hyperspace short cuts in science fiction. In both cases, the ship is in a short cut where spatial properties are different from more routine conditions of open seas in deep waters. In both cases, the navigator must rely on information he had when he entered the short cut. The navigator has very little if any relevant measurements of what will really matter to the outcome of the journey.
In context of distinguishing bright data of observations from the dark data of theory, I should categorize the existence of a scale itself to be a form of dark data. For a purely data driven decision making, it may be beneficial to prefer finding and defining clusters or categories instead of scales.
There is a benefit to opening our processes to the possibility that the reality may be changing, where the changing is from an evolving intelligence or even from a plethora of competing intelligences that have transitions of power much like our political systems. Admitting dark data into our algorithms blinds us to this possibility, especially when we allow dark data to have priority over observations.
A government by data could consider the observations of iatrogenic complications and deaths. The public’s fear of a virus could grant this government permission to impose some new authoritarian policy that would do something, but that something would exploit the opportunity to improve the future prospects based on all observations of the current world. Such a government would be free to decide to tackle the problem of iatrogenesis instead of the problem of the virus. Fixing the overextension of medicine may ultimately benefit more people than overreacting to a virus that is not as threatening as the population perceived.
All government funded scientists, whether through salary, contract, or grant, have a conflict of interest when it comes to providing science to support government policies. The strong bias is toward supporting those policies and avoiding any challenge to those policies.
When considering whether to admit dark data to the data stores available to our algorithm, we can ask what would be different if we did not know this information, even if there is good reason to believe it to be true.
Given what we now know about this virus, our ancestral trust in God would have served us must better than our actual course of action that instead trusted humans acting beyond the boundary they should not have crossed.
It is conceivable that our faith in science over observations could return the human condition to where it was at after the fall of the bronze age, only the mysterious monuments would need to be explained by even bigger giants. The risk of this happening is significant even if it is unlikely.
My previous post outlines the ideas I have about a fantasy government of data and urgency as it relates to the COVID19 situation. Here, I want to contrast different approaches to governing.