Simulated Economy Tutorial
jasonfantl.com104 points by jfantl a day ago
104 points by jfantl a day ago
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.
>we do need and should have good fine-grain computational models of complex systems such [...] the global economy.
Many years ago when 'social graphs' were still a hot area to do research in I started building a simulation of the equivalent of a small medieval village.
What became quickly apparent is that you didn't just need interactions between any two individuals like classical social graphs talked about, but between any number of arbitrary groups of individuals. Otherwise something as simple as an extended family couldn't be modeled.
That meant that instead of being able to use a matrix as the fundamental data structure you'd need a tensor of rank N, where N is the number of people in the economy. Just to see how intractable this is if the village had 20 people in it with the traditional matrix approach you'd need 400 weights to model interactions. With the tensor approach you need ~1e+26.
In short: it's impossible to have fined grained simulations of complex societies. The best we can do is drastic over simplifications that give us _some_ predictive power.
A set of size N=20, has total number of subsets equal to 2^N = 1.05 million. You must have had other degrees of freedom to bump that to 1E26.
That's only for two way relationships, you need N way relationships, which is N^N.
Wouldn't you be able to quickly prune away invalid/unlikely interactions? Maybe, have some cutoff based on proximity of members or something?
Yes, the resulting tensor is incredibly sparse, but still too large to ever be practical for use as anything but the theoretical upper limit on the complexity of a model.
The issue is that while you can remove pretty much all possible interactions for a specific case you have no idea where an interaction could pop up unexpectedly with a huge impact ahead of time.
For the medieval theme the leader of the village may be a cousin of the king which is a very distant but very strong interaction.
how many people do you talk to in a day?
This is a perfect example of the limitations of old school social graphs. The number of people you talk to is just the first order effect. What about all the people that people in your company talked to? That has a non zero economic impact on you. Similarly for any other company that your company talks to, and so on and so on.