Applications Due Soon!!

There are just six days left until priority applications for Uncommon Sense, Bit by Bit’s seminar on critical thinking, are due! The program is a new, free experiment in rationality launched in partnership with Stanford Effective Altruism. If you like puzzles that help you think about the world, debates that challenge your assumptions, and equations that model everything from viruses to elections, this program is for you! Stop by our info session at 4pm PDT on Wednesday, Sept 23rd at https://fb.me/e/31LvLitbL to learn more!

Here’s a small sample of some of the conundrums we will work through:

It’s Feb 28th, 2020. You’ve noticed a certain pandemic spreading across the US. None of your friends are all that concerned, but you and are wondering if you should be taking any precautions or making plans in case you need to self-isolate. How can you use what you know to quickly model the pandemic’s spread in your area and predict how many days until you need to self-isolate?

You are an art collector working for a prominent museum. A valuable piece of art is up for auction. Supposedly, it is a famous lost Vermeer worth $20,000,000 to any buyer. Several prominent collectors and galleries will be bidding in the auction. But, the art community has its doubts about the painting’s authenticity. If it’s a fake, it is worthless. Every collector at the auction has been offered the chance to have an appraiser evaluate the piece for an hour. No one will know for certain if the painting is a fake until they buy it and run tests on it for weeks. But each appraiser has had enough time to take a cursory examination and reach their own conclusions about the painting’s authenticity. After careful analysis, your art appraiser says she thinks there is a 60% chance the painting is the real deal. She warns you that this value is a rough estimate, and other appraisers have likely reached different conclusions. How high should you be willing to bid?

You are in a jury trying to decide whether or not to condemn someone for kidnapping and ransoming an innocent man. Police are certain the perpetrator was one of 30 people staying in a certain motel who match the basic demographics of the kidnapper and have weak alibis. The businessperson then picked the accused out of a lineup. You know a perpetrator gets correctly identified in a lineup about 60% of the time, and any given innocent person gets identified about 10% of the time. Based on this evidence, what are the odds that the accused is guilty?

Happy thinking!

Bit by Bit