An initial implementation of "open loop in natura economic planning". Note that this is not your standard "data - train - validate - test" machine learning code, the topic of the paper is very different, and the purpose of the experiments is to showcase feasibility, rather then benchmark vs a previous method (which does not exist).
All the generated data for the plots is in ./plot_data
. All data required for the
village experiment is in ./data/
. The random generated matrices are missing, but
(a) they can be generated using ./generate_matrices.py
(b) they are 22GB.
./selvaria.py
executes the experiments and generates the experiment for
optimising the economy of a small alien village.
./online_time_complexity.py
optimises for one tick for various random
economies of different sizes. All the generated files are around 22GB, so they are
not included (but you can generate them).
./coeffs.py
has the coefficients for the production units of the village economy.
The corresponding consumption data is in ./data
. If you just run the file it will
create the coefficient plots for those three production units (i.e. factories).
./utils.py
include various helper functions.
./generate_matrices.py
includes the code to generate random sparse matrices.
./plot_selvaria.py
includes the code for investment plots, as presented in the paper.
./plot_time.py
plots the (empirical) time complexity of solving for matrices
of certain size.
./metrics.py
includes the code for humanity and other metrics.