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105 points jfantl | 2 comments | | HN request time: 0.593s | source
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jgord ◴[] No.43553018[source]
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.

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noosphr ◴[] No.43553741[source]
>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.

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1. pornel ◴[] No.43570671[source]
Use of a dense matrix is an artificial constraint you've imposed yourself, but that only disproves feasibility of your proposed solution, not the entire problem in general.

A similar problem, n-body simulation*, has n² gravitational interactions. You will similarly hit a wall if you try to do it with a dense n² matrix. However, there's a hierarchical solution that takes advantage of the sparsity and exponential decay, and can solve it in (n log n) with an imperceptibly low loss of precision.

Social interactions are sparse, and group interactions can be optimized with clustering. Fine-grained simulation of the entire society is such a massive chaotic problem with so many variables, that some loss of precision from clustering is completely insignificant compared to the inevitable simplifications you'll have to make in the design of the model itself.

* I mean the naive one with a fixed timestemp, not trying to solve chaos.

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2. noosphr ◴[] No.43578605[source]
>Use of a dense matrix is an artificial constraint you've imposed yourself, but that only disproves feasibility of your proposed solution, not the entire problem in general.

The use of a dense matrix is the traditional way of solving the problem. The issue is that it solves the wrong problem. You need a dense tensor which requires more storage than the world currently has for an economy of 20 people.

Social interactions are sparse until they aren't. If you think otherwise try to estimate what every Europeans interaction with Gavrilo Princip was on 27 June 1914 vs 28 June 1914.

As for gravitation: I'm very happy for the planets and asteroids out there. Unfortunately the economy isn't a solar system.