Interesting article connecting ideas about how we assess risk, the language we use, and how we interpret data depending on how it is presented to us. This connects well to the conflict between emotion and reason in decision making but also our inability to think probabilistically.
If you ask someone if they would be willing to sacrifice 100K people to avoid this intervention, people will be inclined to say no, “I would never put a price tag on human life, much less 100K human lives.” But let’s say you ask the question differently: “Would you be willing to accept a one in three thousand chance of dying this year to avoid this public health intervention?”… Once you ask the question this way then instead of focusing on the raw number of deaths, we can focus on the tradeoffs,
Great article for discussing the production of scientific knowledge, shared knowledge, and also the new knowledge and technology theme.
Scientific Cooperation Knows No Boundaries—Fortunately
Infectious diseases, it is commonly said, know no borders, and neither does the knowledge needed to fight them. Scientists around the world routinely share information and collaborate across borders. The current pandemic has scientists working together on platforms such as Slack, and using new tools, such as machine learning, to rapidly detect the novel coronavirus in tests that use large amounts data from multiple sources. This outbreak has demonstrated in real time how scientific understanding can indeed be a global public good.
This article gets into a lot of the relevant issues of medical ethics and uses appropriate ethical language in the discussion. I’m working with this in my class today (remotely). Here’s the worksheet I’m using today.
“The criteria for access to intensive therapy in cases of emergency must include age of less than 80 or a score on the Charlson comorbidity Index [which indicates how many other medical conditions the patient has] of less than 5.”
The ability of the patient to recover from resuscitation will also be considered.
One doctor said: “[Who lives and who dies] is decided by age and by the [patient’s] health conditions. This is how it is in a war.”
Calculating the economic costs of curtailing social interaction compared with the lives saved, he agreed, might yield a useful metric for policymakers. The U.S. government routinely performs such analyses when assessing new regulations, with the “statistical value of life” currently pegged by one government agency at about $9 million.
Still, Dr. Thunstrom asked, “Do we even want to look at that? Is it too callous?”
When you encounter a potential risk, your brain does a quick search for past experiences with it. If it can easily pull up multiple alarming memories, then your brain concludes the danger is high. But it often fails to assess whether those memories are truly representative.
A classic example is airplane crashes.
If two happen in quick succession, flying suddenly feels scarier — even if your conscious mind knows that those crashes are a statistical aberration with little bearing on the safety of your next flight.
All the data that we trust and believe on a daily basis, is only accurate in a specific context, at a specific time, and at a specific level. If you dig deep enough, ultimately all of the data in the world that drives major and minor decisions alike is built on wobbly foundations.