[jldugger]

> https://cloudfront-us-east-2.images.arcpublishing.com/reuter...

Help me understand: is this a stacked chart? Why does a chart that ends in 2021 show inflation in 2022 and 2023?

> This inflation is due to insiders (central bankers) printing money.

If this is about the money supply, why is food more affected than other products? IMO everything is a factor, and there is no one culprit, and therefore no one easy fix.

[elevenoh4]

Imagine being a rational human looking at g7 central banks adding 40% to the money supply in <2 years, and your focus is "everything is a factor, there no one culprit" for price inflation.

https://image.cnbcfm.com/api/v1/image/106649548-159664277043...

I look forward to open source ai economic simulation tools - when you change variables like m2, you'll see what happens to inflation - and there'll be no more ambiguity

For now: ask gpt-4 yourself & get a lesson

[sofixa]

> Imagine being a rational human looking at g7 central banks adding 40% to the money supply in <2 years, and your focus is "everything is a factor, there no one culprit" for price inflation.

Imagine being a rational human and deciding that a war involving two of the biggest agricultural exporters in Europe, one of whom is also one of the main exporters of oil and gas, as well as a ton of different important materials (nickel, steel, fertilizers, etc.), after 2 years of pandemic that crippled global supply lines and resulted in explosion of prices in many raw materials, have combined absolutely no impact on prices anywhere.

[jldugger]

just sayin': why did inflation appear when it did? why not sooner? why not later? Why is the impact of inflation non-uniform?

We know the model pretty well: "money printer goes brrr" leads to higher prices for good and services. But the data lately doesn't make a great supporting case.

> I look forward to open source ai economic simulation tools - when you change variables like m2, you'll see what happens to inflation - and there'll be no more ambiguity

Economists have been leading the way in multifactor causal inference, but GPT in particular is not well suited to it. And macroeconomics in particular is hard to measure via natural experiment, so you're left trying to estimate counterfactuals based on a paucity of data

but look, my day job is people coming to me with timeseries data showing an engineering fuckup and debating about what caused it (usually ops vs prod). if theres a nuanced take, im all ears, but so far your fire has brought more heat than light.