This is ham-handed government in action where the “greater good” benefits no one and is a disaster to many. There were enough beds somewhere. At Stony Brook there were hundreds of unused beds in temporary buildings built (I think) by the Army Corps of Engineers. 1/4

https://twitter.com/adamcrigler/status/1367289866447441922

These buildings were sealed and employed the proper filters. This was a Federal program not unique to NY. Instead of returning recovered healthy elder patients to crowded nursing homes many could have been kept in a safe environment, requiring minimal medical supervision. 2/4

The PRC and WHO warned the world not to overreact. Many criticized governments for overreacting. Then the extent of the pandemic became clear. Governments were criticized by some for not reacting earlier. Models began showing potentially huge numbers of fatalities. 1/8 Now there seems to be general consensus that although governments acted late, the fatalities were tragic, they did not hit the extremes forecast so perhaps the government was too aggressive. The fact that appropriate action may be the reason the extremes were avoided 2/8

The #Covid_19 crisis is an excellent example of how stress can make systems #Antifragile because it offers us a opportunity to see the weaknesses extant in global travel and supply chains and to develop policies and procedures for dealing with pandemic control and treatment. 1/6 I'm not one for Panglossian interpretations. I do assert, however, that we have a valuable opportunity and, therefore, a real duty to learn and to make definite changes in order that we are better prepared to handle the next--inevitable--pandemic. 2 /6

Preferences revealed (e.g., dogs vs cats, purchasing patterns, film & book ratings you make, iPhone vs Android) are weak predictors of your politics, sexuality, pregnancy status, the probability you'll repay debts, how secure you feel economically, and many other factors. 1/2 The machine learning technique of boosting (en.m.wikipedia.org/wiki/Boosting_…) can sometimes combine an ensemble of individually weak predictors into a strong predictor. Social media, Amazon and Netflix purchases and ratings, Yelp and Uber reviews, and similar data are easily scraped. 2/2