1/ Constructing the dashboard to explain our paper (reopenmappingproject.com) involved a lot of careful thinking about what info to display/emphasize and how. The goal of the app was to make the message, methods and results of our paper accessible. Thread👇 for more weeds.
2/ First, data limitations: we build contact matrices from Replica's synthetic population. This is amazing data (e.g. it lets us account for how long ppl spent in the same place) but:
3/ a) it is based on a "typical" day and has poor coverage of rare/big events like concerts; b) it is based on cell pings inside the cities and has poor coverage of travel + of kids; c) it uses Q1 of 2019 as a baseline + modifies based on policies as defined by us.
Tl;dr: Heterogeneity matters when thinking about lockdown/re-opening policies. Diffs in concentrations of places where ppl encounter each other, diffs in industry, demographic (and co-morbidity) distributions, diffs in when the virus hit.
These forms of heterogeneity are (largely) measurable -- and we made a major effort to measure them. We combine a representation of daily activities + meetings across metro areas built on rich cell data by Replica w/ electronic medical records, O*Net, OES + more
Hey #econtwitter- I'm helping put together a tip sheet re computational tools for structural IO, including notes on when some languages/solvers are better than others. I don't use python for optimization but I know lots of ppl do. Any chance y'all could lend some tips? Example 👇
A few other things that it'd be great to have a 1-liner explaining (w/ links to more):
-How to evaluate trade-offs re Analytical Derivatives vs Numerical Differentiation vs Auto Differentiation
-When to impose optimizer constraints via transformation -- e.g., mapping [0,1] -> R
Here's my draft so far. Plz send more tips/correct any errors.
Policy debates re privacy on the internet often stress these trade-offs: 1) Firms getting user data -> better matches + service to larger market 👍 2) But lack of privacy is icky 👎 3) And facilitates "too much" price discrimination 👎
Lots of responses:
Policy -- GDPR, CCPA, FCC "best practices", etc
Industry -- "privacy oriented" products by Apple, Mozilla, etc.
Academics -- budding theoretical literature on privacy + value of data; small but growing empirical lit (eg this from NBERSI