Exercises in Comprehensive Information Gathering
Looking back, several of the most durably-valuable exercises I’ve done over the years have a general theme of comprehensive information gathering.
The most recent example involves capital investments. Economists talk about “capital goods” as physical stuff—machines, buildings, etc. But in practice, savings and investments are passed through banks and ETFs, bundled and securitized, involve debts and shares of companies which own debts and shares of other companies, and so forth… where does all that capital end up? To get an intuitive sense, I pulled up fundamental data on about 7000 US publicly-traded companies in quantopian, sorted them by amount of non-financial assets, and found that the top 100 accounted for about 50% of the non-financial assets of the whole set. Then, I looked at annual reports for each of those 100 companies, to see what capital assets they had. I googled around for pictures and maps of where those assets were located, and read up on anything I hadn’t heard of before. What’s a “central office”, where are they, what do they look like, and why does AT&T have $90B worth of them? What are the major US oil basins, where are the wells, and what all goes into drilling them? What are the technical differences between traditional phone, cable, satellite, and cell networks, and how do those technical differences impact the capital requirements of each? Who runs power plants and the power grid in various parts of the country? What are the major US railroads, and where are they? Why did GE own so many airplanes? These are the kinds of questions which come up when you want to know what “capital goods” actually consist of, in the real world.
Another interesting exercise: I read through five years of Nature archives, reading all the titles and any abstracts which sounded novel/interesting. I didn’t google everything I hadn’t heard of; instead, I’d wait until the same acronym popped up a few times before looking it up. This took maybe a week of evenings after work. By the end, I could at least place the large majority of articles in context. Now, when I see a title full of jargon in a field I haven’t studied, like “Novel tau filament fold in corticobasal degeneration”, I usually at least understand enough to guess at what it’s relevant to (in this case: neurodegenerative disease involving protein aggregates, probably Alzheimers?). I can generally follow conversations in a bunch of different fields—not necessarily between specialists in the same sub-sub-field, but at least the level of a typical conference talk, and when I meet new people I can ask not-too-embarrassing questions about what they’re researching.
Going back further, if you’re in college, I strongly recommend reading your entire course catalogue, googling anything you’ve never heard of at all, and marking anything that sounds potentially interesting. This seems really obvious; it only takes a few hours, and something something a pile of value sitting on a silver platter right in front of you. (Note: I went to a small STEM school; if you’re at a big school with a bajillion courses or a school with poor STEM coverage or not at college at all, consider reading an MIT/Caltech course catalogue instead, to get a feel for what all is out there.) You never know what surprising and interesting topics might be hiding in there—microfluidics, underactuated robotics, recursive macroeconomics, systems biology, synthetic biology, origami algorithms, computational photography, evo-devo, procedural graphics, and on and on.
These sort of exercises provide value in a few ways:
They reveal unknown unknowns—things you didn’t even realize were missing from your picture of the world.
You can’t make a map of a city by sitting in your room with the shades drawn; exercises like these force you to look at large slices of the world.
Knowledge within fields tends to have decreasing marginal returns—your first physics or CS class will teach you much more than your eighth. These exercises give a broad, brief glance at many areas where you probably haven’t reached decreasing marginal returns yet.
You can get a very rough big-picture sense of how much effort other people are investing in various areas—e.g. where most capital investments go or where most research effort goes—which is useful for understanding the world in general.
While these exercises don’t avoid biased selection of information altogether, they’re probably different biases from what you run into naturally, and they’re systematic enough that we can guess at what biases are likely to be present.
They’re a lot of fun, if you have a curious streak.
Most importantly: I’ve found each of these exercises to have lasting, long-term value in exchange for a one-time investment of effort.
Other exercises which are on my to-do list, but which I haven’t done yet:
Go through all of the (known) functions of genes in a minimal organism.
I’m curious to hear other suggestions for exercises along these lines.