pgustafs

The definition of bijection is much more interesting than comparing cardinals. Many everyday use cases where (structure-preserving) bijections make it clear that two apriori different objects can be treated similarly.

More generally, mathematics is experimental not just in the sense that it can be used to make physical predictions, but also (probably more importantly) in that definitions are "experiments" whose outcome is judged by their usefulness.

The books are good, but way too many and wildly varying in difficulty. No one can read all that in 2 years starting without knowledge of linear algebra. just worry about the fundamentals first and then pick a couple good books in areas you’re interested in. The main thing is deep understanding, not superficial breadth.

Nah, just study linear algebra (Shilov or Hoffman & Kunze) and baby Rudin. Then read the most famous books in geometry, analysis, and algebra (do proofs + get a mentor). All these roadmap things are meaningless. It’s like “how to join the NBA.” Lift weight, condition, and practice fundamentals. Nothing else matters.

it's called "angel" investing because it's only one step removed from charity

There are three broad subareas of mathematics: geometry, algebra, and analysis. Geometry studies space, algebra studies time, and analysis studies infinity. They are not independent -- most professional mathematicians use some mixture of the three, and virtually every mathematician understands the basics of all three.

The most important object in modern geometry is the manifold. This is a space that looks locally like n-dimensional Euclidean space -- 1-dimensional manifolds are curves, 2-dimensional manifolds are surfaces, and higher dimensional manifolds are simply called n-manifolds. All of physics takes place on manifolds. Differential equations correspond to vector fields on manifolds. The manifold hypothesis says that much of the high-dimensional data we see actually lives on much lower-dimensional manifolds (partially explaining the unreasonable effectiveness of deep learning on very high-dimensional datasets).

The most important object in algebra is the group. The collection of symmetries of any object (e.g. a Rubick's cube, a piece of paper, or three-dimensional space) forms a group under composition. A group that is also a manifold is called a Lie group. These are everywhere -- n-dimensional rotations form groups, fundamental particles correspond to representations of Lie groups, invertible matrices form a group. Spherical harmonics and Fourier series are both naturally viewed in terms of representations of Lie groups.

The most important object in analysis is the limit. Limits first appear in the construction of the real line by adjoining limits of Cauchy sequences to the rational numbers. Using the real line, one can measure volumes, probabilities, and distances in geometric spaces such as manifolds, but also in spaces of functions, sequences, and more abstract objects. The proof of the fundamental theorem of calculus (that derivatives and integrals are roughly inverse operations) requires rigorous analysis of the definitions of derivative and integral as limits.

To learn math, you should begin by understanding what a proof is. All of mathematics is based on proving theorems. A mathematical proof is a sequence of statements that explains the logical steps required to use the assumptions of the theorem to verify the result. Just as a computer program cannot "almost output" the correct answer, there is no such thing as an "almost correct" proof. A proof either describes a correct chain of logic to reach the conclusion, or it does not. The reason math is based on proofs is because more advanced math and science builds upon more basic math. An error in a mathematical theorem or an imprecise definition will lead to bigger problems down the line, so every step must be carefully validated. For an individual student as well, only through proving theorems can one deeply understand a mathematical subject, and a solid understanding of basic subjects is required to understand more advanced topics.

Fortunately, you can learn to prove theorems at the same time as learning the foundations of math. The first books you should work through are "Principles of Mathematical Analysis" by Walter Rudin, and "Linear Algebra" by Georgi Shilov. This will be hard, not for an arbitrary reason, but because assimilating new math into your brain is intrinsically difficult, especially at the beginning. If possible, try to find a teacher.

Follow your innate curiosity, and respect the edifice of knowledge constructed by those who came before us (i.e., don't get sucked into quackery without a deep understanding of the SOTA).

I have nothing against contest math (I was a USAMO qualifier in high school), but contest math isn’t enough if a kid has to sit through years of tedium during regular classroom hours. Also, there is a large difference between contest problems which focus on cleverness and real-world problems which focus on conceptual understanding. Many kids prefer one or the other, and I think it’s a mistake to assume that contest math works for all kids who might be mathematically inclined.

Re: goals —- the goal is to let the kid learn as fast as they want assuming they have solid foundations. If they like proofs let them do proofs, if they like applications let them do that. Just don’t force them to sit in a classroom doing busywork for the most formative years of their lives.

I think problem solving math is definitely fun and can be a huge source of confidence, but I don't see why "racing" through the standard curriculum is a negative. Why should a smart kid do a million multiplication/division problems for 5 years when they would have a ton more fun and get a lot more long-term utility from learning some stat/algebra/geometry? If a kid demonstrates mastery of a concept, it's a lot more bizarre and potentially damaging to force them to relearn the same material over and over.

Agree, but I don't like the framing of "accelerating." Math in school is for the median student. If you want a quantitative career or just want to have quantitative skills, you should be aiming for way above median. Aiming for median outcomes makes zero sense in the current world. Find your niche and hit it hard.

Kids intuitively understand this -- they like doing what they're good at. Unfortunately, most schools are not good at serving this need. A very important part of being a parent is to encourage kids to start compounding positive habits/learning early, and to prevent the schools from dragging them back to the median.

Adding to your point, most successful markets are positive-sum -- hedgers gain value from mitigating their structural risks, and speculators get paid to assume price risk. For example, the wheat futures market has two natural participants -- farmers and bakers. Farmers can sell future produce to buy seeds right now. Bakers can hedge their wheat price exposure to reduce their chance of getting ruined by a bad harvest. Speculators get paid to hold onto wheat futures contracts if a farmer wants to sell a future when the bakers aren't around to buy (presumably baking), selling to a baker later for a higher price reflecting the price risk assumed. All of these participants derive value from the market.

It's not clear to me that prediction markets usually have natural hedging participants (maybe political operatives, but the tx costs are probably too high relative to the value at stake).

One point that seems to be missing from this discussion is climate.

Walkable cities: NYC, Chicago, SF, Boston, Seattle

Urban hellscapes: LA, Houston, Atlanta, Phoenix

Simple explanation -- people don't want to walk around in the heat.

Great post, one nitpick -- I wouldn't say that a matrix is a "sparsely defined" function, but rather a function defined on a finite grid. It might also be worth pointing out that same approach works for any graph, not just a grid.