>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.

We already have tons of those models.

None of them are perfect.

And they never will be.

Could the be better? Yes.

The problem is, you won't really know they're better until post-ex. And even then, you'll never be sure how much better. They're always bound to fail catastrophically at some point. Etc.

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.

There’s a whole discipline which does nearly that, though they do not use this style of agent based model.

Generally agent based models have numerous parameters which can take many values (endowments, preferences) and the models don’t themselves give any guidance about how to set the parameters. Theory can give limited guidance (eg., that function is concave, this parameter is negative). Sometimes we have experimental data though its generalizability beyond the lab is uncertain.

What you want to do to create a scientific macroeconomics is to work backwards from the data you see in the economy (aggregate consumption, investment, etc.) and how you know the aggregates were generated (via the behavior of a lot of individual agents), and an equilibrium assumption to recover the parameters.

If you know the parameters of the model you assume, you can then simulate interesting counterfactuals. (And yes you assume the model - a “full” model including “all” of the individual endowments and parameters you can think of is completely intractable. You have to simplify.)

You’ll never get that out of the author’s computer game.

If you want references to the macro literature it’s enormous and I can provide them.

Issue of the Commons.

Weather models are good because if we know about it the weather doesn't care and doesn't change what it is going to do.

Anyone who has an accurate financial model is keeping it to themselves.

Anyone who has an accurate financial model and make it public... invalidates their model as everyone takes that information and plans to take advantage of it accordingly.

While your last point is certainly an ideal to aspire to, something tells me that the powers that be would not actually want to get rid of booms and busts, because ultimately that is where a lot of the “wealth” for those high up is created. You don’t really need complex models to solve the problem of some humans being really, really greedy, driving markets to overheat, ending in catastrophic failure.

> we do need and should have good fine-grain computational models of complex systems such as the cell .. and the global economy.

Thanks to the pioneering work done by physicists, we realized we could simulate dimension reduced versions of reality instead. We call them statistics and differential equations :)

Stack enough of them together, you get something called "deep learning". Large scale national lab supercomputer type numerical simulations are for your grandparents (these days you can probably take shortcuts and simulate that sort of born secret computations in a neural net that is much more compute efficient than the typical supercomputer).

If you had such a model you could arbitrage between Polymarket betting on wars and stock prices. There's not much of an incentive to release such a model publicly.