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We are grateful to Mr Peter Seymour, of Hertfordshire, for drawing our attention to the article by Daniel Burke* on CNN, concerning the increasingly acrimonious debate in the United States about whether that country should be opened again for normal commercial business. Burke bases his argument on a detailed analysis of the Utilitarian Philosopher Jeremy Bentham, author of the famous dictum “The Greatest Good of the Greatest Number“. “Openers” will scream that more damage will be done to more people by the lockdown than the lives saved by its converse. The other side will reply in the opposite, with every weapon in its armoury. We expect that the coming months will see a bitter and divisive debate, with each side brandishing statistics, arguments and proofs, with no clear winner.
We lack a really objective mathematical model of what actually works.
Now Ciaran Lee Gilligan, writing in New Scientist, offers an answer.* He thinks the problem is that we do not clearly distinguish between causation and correlation. Here is an example.
“Data from seaside towns tells us that the more ice creams are sold on a day, the more bathers are attacked by sharks. {Correlation}Does this mean that ice cream vendors should be shut down in the interests of public safety?”
The answer is no. Here it is easy to see the cause behind this correlation. More people go to the beach in hot weather. More ice creams. More swimmers. More shark deaths. But-if you only had the data on the sharks and ice creams, how would you know this? The trouble is for thousands of problems in science, medicine and economics, we have lots of data. And no way of knowing the true cause, because correlation is never proof of cause.
In the 1990s, Judea Pearl of UCLA came up with a new tool which he called the theory of causal inference. At this point you really, really should read the article below: but to give you a flavour-
It should allow us to mine data sets to better establish the real causes and effects in all sorts of areas like medicine, science and economics (see the relevance to Burke now?)
It should allow us to remove all sorts of biases and uncontrolled variables when we try to replicate the work of other researchers
Economists are excited, as it should give us much better ways of really assessing all kinds of policy changes, in health, taxation, and so on. (Both Daniel Burke and Jeremy Bentham would love this)
We at LSS humbly admit to the most breathless, desperate filleting of an intelligent article about intelligent people. We know where the foregoing sentence leaves us. But please again, read and judge for yourself. If enough people do it, maybe we can avoid these desperate miscomprehensions in places like Denver.
https://edition.cnn.com/2020/04/23/us/reopening-country-coronavirus-utilitarianism/index.html
#CNN #JeremyBentham #Utilitarianism #Reopen #coronavirus #JudeaPearl #causalinference
Learning, Science and Society 