Fermi Calculation Examples

Fermi estimates or problems are quick heuristic solutions to apprently insoluble quantitative problems rewarding clever use of real-world knowledge and critical thinking; bibliography of some examples.
bibliography⁠, statistics
2019-03-292021-06-11 in progress certainty: possible importance: 4 backlinks

A short discussion of “Fermi calculations”: quick-and-dirty approximate answers to quantitative questions which prize cleverness in exploiting implications of common knowledge or basic principles in given reasonable answers to apparently unanswerable questions. Links to discussions of Fermi estimates, and a list of some Fermi estimates I’ve done.

I really like (LessWrong)—it’s like for everything outside of physics1⁠.

Not only are they fun to think about, they can be amazingly accurate, and are extremely cheap to do—because they are so easy, you do them in all sorts of situations you wouldn’t do a ‘real’ estimate for, and are a fun part of a ⁠. The common distaste for them baffles me; even if you never work through Hubbard’s How to Measure Anything (some strategies) or Street-Fighting Mathematics or read 1982 essay “On Number Numbness” (collected in Metamagical Themas), it’s something you can teach yourself by asking, what information is public available, what can I compare this too, how can I put various boundaries around the true answers2 You especially want to do Fermi calculations in areas where the data is unavailable; I wind up pondering such areas frequently:

An entire “estimation” subreddit is devoted to working through questions like these (it can be quite fun), and of course, there are the memorable “what if?” xkcd columns.

suggests a number of problems which might help children really learn how to think with & apply the math they learn.

To look further afield, here’s a quick and nifty application by investor John Hempton to the : “Risk management and sounding crazy”⁠. What I found most interesting about this post was not the overall theme that the whistleblowers were discounted before and after they were proven right (we see this in many bubbles, for example, the housing bubble), but how one could use a sort of Outside View⁠/Fermi calculation to sanity-check the claims. If Sino Forestry was really causing 17m cubic meters of wood to be processed a year, where was all the processing? This simple question tells us a lot. With medicine, there is one simple question one can always ask too—where is the increased longevity/all-cause mortality reduction? (This is an important question to ask of studies, such as a recent caloric restriction study⁠.)

Simple questions and reasoning can tell us a lot.

  1. This is a little misleading; dimensional analysis is much more like a program in a language with a good type system like Haskell. Given certain data types as inputs and certain allowed transformations on those data types, what data types must be the resulting output? But the analogy is still useful.↩︎

  2. eg. if someone asks you how many piano tuners there are in Chicago, don’t look blank, start thinking! ‘Well, there must be fewer than 7 billion, because the human race isn’t made of piano tuners, and likewise fewer than 300 million (the population of the United States), and heck, Wikipedia says Chicago has only 2.6 million people and piano tuners are rare, so there must be many fewer than that…’ You always know something, and have a universe of beliefs which imply constraints on everything and equilibria.↩︎