The remarkable aspect of mainstream economists is their persistence treating values in (digital) ledgers as actual instances of banknotes (physical paper) as their mental model, then selectively dropping the model.
These are really good questions that actually would be best addressed by agent-based modeling, where individual agents (banks, employees, investors, bond traders, federal govt, state govt, firms etc) each have behaviors defined by rules and you run the system adjusting the rules to see how the scenario plays out. #julialang is a great tool for this using the Agents.jl library in part because it's fast
(compared to python when it is forced to apply arbitrary functions with loops inside, element-wise to an array - that is, can't benefit from vectorised numpy functions)
this #maths experiment took about an hour in python and about 1 second in julia lang
sure my python isn't professional, but today was my first time with julia lang so that will be far from optimal either
Seeing the schedule in #julialang conference and the last survey, looks like the community is embracing more the idea of using Rust as a support language.
Getting there. These images show tests of a triangulation algorithm I've developed that uses Delaunay triangulation. It features mostly equilateral triangles except for at the boundaries.