Really well done short video, 13 minutes, about the quest to experimentally prove Einstein’s prediction about gravitational waves.
Interesting cartoon that explains the dangers of fake news and how to combat it in your own mind. Unfortunately I am skeptical about the value of laying out such processes to deal with this problem. How can you stop someone from being “fooled” into believing something that they already believe? That confirms and conforms to their deeper world views? The deeper issue is motivated reasoning rather than an ignorance of how to deal with new information. All that being said, this is a fun cartoon, there is more than just this one panel featured below, click on the image for the full cartoon.
Artist Zach Weinersmith teams up with FiveThirtyEight to break down and explain the challenges in building accurate models for the pandemic from differing assumptions, data collection, and just a general lack of information.
Interesting quote from the cartoon: “Every variable is dependent on a number of possible choices and gaps in knowledge.”
Click on the images for the full cartoon.
Reminds me of a funny tweet I saw, “Using the right denominator is 50% of data science.”
We are about to start week 3 of distance learning. I’ve worked on adapting my lessons to be completed on google classroom and submitted. Students can join the “class” on zoom to work through these as if we were doing a normal class. This provides for opportunities to discuss course content which is a central part of the course.
Here is a folder of the work I have done. Please feel free to take what works for you. You can duplicate any of the docs. Please also feel free to share other experiences you have had that work.
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,
Here are some additional materials related to how we think about numbers and risk, etc. from my Maths page
Here is the article that accompanies the handout above.
Some different questions that get at the same point.
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.
This also connects to some questions that came up after Hurricane Katrina put a hospital in New Orleans in a situation in which it had to make similar decisions about life and death.
Who Gets the Ventilator?
Lastly, there is a recent Freakonomics Podcast about this very question that brings together a variety of perspectives on this question.
Please ignore the sensational headline but the article connects to many discussions that relate issues around ethics and public policy. This is a real life application of a form of “trolley problem” playing out in real life. This goes back to some of the choices faced by a hospital in New Orleans after Hurricane Katrina in 2005. When forced to make decisions about whose lives to save, how do we decide?
“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.