Technology advances forced the Census Bureau to use sweeping measures to ensure privacy for respondents. The ensuing debate goes to the heart of what a census is.

This is a great article on a big-name application of differential privacy: it even features an interview with Dworkin! This, to me, seems like a pretty necessary change to preserve the privacy of folks.

via: https://mit.edu/6.1800/www/tutorials/01-intro.shtml

Much of good survey design is getting a decent model of the mind and incentives of your audience. Egos are predictable. You have to tease it out like taffy, and if the ego is sticking to your results too hard you have to find other questions to trap them.

We want assurances that sensitive information will not be disclosed when aggregate data derived from a database is published. Differential privacy offers a strong statistical guarantee that the effect of the presence of any individual in a database will be negligible, even when an adversary has auxiliary knowledge. Much of the prior work in this area consists of proving algorithms to be differentially private one at a time; we propose to streamline this process with a functional language whose type system automatically guarantees differential privacy, allowing the programmer to write complex privacy-safe query programs in a flexible and compositional way.

Very interesting work here!

via: https://ncatlab.org/nlab/show/short+map#use_for_denotational_semantics

One of the questions of the 2026 acx prediction contest is whether Nvidia’s stock price will close below $100 on any day in 2026. At the time of writing, it trades at $184 and a bit, so going down to $100 would be a near halving of the stock value of the highest valued company in the world.

It’s an interesting question, and it’s worth spending some time on it.

via: https://nebula.tv/videos/notdavid-are-men-actually-lying-about-heights-on-dating-apps/

When someone says something like “The us paid $1.1 trillion for its 2003 invasion of Iraq,” you can think to yourself, “Okay, that’s like a paying for the repairs of a global 100-year natural disaster in the PC age.”

50 Cent Adjusted for Inflation

This GitHub repository contains the course materials and code for the 2024 edition of the “Statistical Rethinking” course taught by Richard McElreath, focusing on Bayesian data analysis using R and Stan.