Simulated Economy Tutorial

Just as we have weather forecasting, climate models .. we do need and should have good fine-grain computational models of complex systems such as the cell .. and the global economy.

We should be able to have whole economy simulations give reasonable predictions in response to natural events and lever-pulling such as :

- higher progressive tax rates - central bank interest rate moves - local tariffs and sanctions - shipping blackades / blockages - regional war - extreme weather events - earthquake - regional epidemic - giving poor people cash grants - free higher education - science research grants - skilled immigration / emigration

But .. of course this would require something like a rich country providing grants to applied cross disciplinary research over many years.

It might even lead to insights that prevent semi-regular economic boom and bust cycles we experienced the past 100 years.

You've stumbled upon Chaos Theory (https://en.m.wikipedia.org/wiki/Chaos_theory), which aims to study chaotic systems (charactesised by very high relation to initial variables - see weather prediction, double pendulum, etc).

Some problems are too sensible to initial variables and solutions are not prescriptive like regular physics - meaning that variability at the 20th decimal in your initial variables will induce massive output differences. Lorentz discovery of this is interesting as he was working on weather modelling, it's a clear example of the issues with chaotic systems. He was running simulations of weather systems with multiple fixed initial variables (temperature, wind speed, etc) and seeing how the system progressed over a few hours. He realised that after a typo on a very far away decimal on a single parameter, the system was modelling the complete opposite of what we had seen in the previous test (think it was forecasting a typhoon when it used to say sunny day), even while using values that would be "equal" with relation to the precision of the measuring equipement. And that's nothing to talk about getting clean, precise enough data for such models, which is practically impossible (see the observer effect, between other causes). Garbage in, garbage out.

All this to say that problems in this sphere are characterized by quickly becoming untractable and impossible to model precisely how they evolve over time.

I can recommend James Gleick's Chaos: Making a new science for a overview for the layperson.