Uses of math, & limits of knowledge... Attempting to solve the C.P. Snow "two cultures" problem... The systemic parts of a Malthusian economy are (a) stagnant living standards, and (b) population...
Dietrich Vollrath blogged a fantastic review of "Slouching" back in October (https://growthecon.com/feed/2022/10/11/DeLong-Review.html) in which he pointed out that to see 1870 as the "hinge of history" you really need to take into account the demographic transition. Your Python exercise above gave me the opportunity to visualize that perfectly.
Set the graph to have log scale on the y-scale (`ax.set_yscale('log')`) and at some arbitrary year (e.g. `t==500`) change h to 0.01:
Population starts to grow exponentially, but income bumps up by a small constant and then again becomes stagnant.
So to see the "hinge of history" we have to modify the equation for `n` in some way so that as income gets sufficiently high, `n` starts to fall.
Different topic, but has anyone else noticed that Tesla's quarterly US sales count isn't increasing? A company with a growth P/E that isn't growing, and is cutting prices.
Economics is a wonderful discipline because most ideas can simultaneously be expressed with words, graphs, and equations. If they can't, then maybe the idea hasn't been thought out sufficiently.
But most people can only understand one or two expressions. And many teachers I fear emphasize whichever is their strength, or demand all three. If we consider that the objective in undergraduate is to transfer understanding, then we offer all three choices somehow, and are happy if the student leaves with understanding (not regurgitation) in any one of them to apply later in life. I say this as a non-teacher, so this may be nonsense.
I think the best exercise is basic predictive modeling because it can demonstrate probabilities, distributions, uncertainty, and the multivariate way the world usually works. These concepts slay many lazy ideas and sophists. This, more than simulations. Even if the modeling is in a high level software that doesn't require coding the equations.
I've been trying to think a bit about the macroeconomic impact of AI, probably without much wisdom, and with even less wisdom thinking aloud about it online https://blog.rinesi.com/2023/01/ai-and-the-macroeconomics-of-brains/ , but I'm sure there's already a ton of work done and being done on it. Any proto-canonical references?
Dietrich Vollrath blogged a fantastic review of "Slouching" back in October (https://growthecon.com/feed/2022/10/11/DeLong-Review.html) in which he pointed out that to see 1870 as the "hinge of history" you really need to take into account the demographic transition. Your Python exercise above gave me the opportunity to visualize that perfectly.
Set the graph to have log scale on the y-scale (`ax.set_yscale('log')`) and at some arbitrary year (e.g. `t==500`) change h to 0.01:
Population starts to grow exponentially, but income bumps up by a small constant and then again becomes stagnant.
So to see the "hinge of history" we have to modify the equation for `n` in some way so that as income gets sufficiently high, `n` starts to fall.
Different topic, but has anyone else noticed that Tesla's quarterly US sales count isn't increasing? A company with a growth P/E that isn't growing, and is cutting prices.
Economics is a wonderful discipline because most ideas can simultaneously be expressed with words, graphs, and equations. If they can't, then maybe the idea hasn't been thought out sufficiently.
But most people can only understand one or two expressions. And many teachers I fear emphasize whichever is their strength, or demand all three. If we consider that the objective in undergraduate is to transfer understanding, then we offer all three choices somehow, and are happy if the student leaves with understanding (not regurgitation) in any one of them to apply later in life. I say this as a non-teacher, so this may be nonsense.
I think the best exercise is basic predictive modeling because it can demonstrate probabilities, distributions, uncertainty, and the multivariate way the world usually works. These concepts slay many lazy ideas and sophists. This, more than simulations. Even if the modeling is in a high level software that doesn't require coding the equations.
I've been trying to think a bit about the macroeconomic impact of AI, probably without much wisdom, and with even less wisdom thinking aloud about it online https://blog.rinesi.com/2023/01/ai-and-the-macroeconomics-of-brains/ , but I'm sure there's already a ton of work done and being done on it. Any proto-canonical references?