cadCAD

Warning: Do not publish this package / software to any software repository except one permitted by BlockScience.

Description:

cadCAD is a differential games based simulation software package for research, validation, and Computer
Aided Design of economic systems. An economic system is treated as a state based model and defined through a
set of endogenous and exogenous state variables which are updated through mechanisms and environmental
processes, respectively. Behavioral models, which may be deterministic or stochastic, provide the evolution of
the system within the action space of the mechanisms. Mathematical formulations of these economic games
treat agent utility as derived from state rather than direct from action, creating a rich dynamic modeling framework.

Simulations may be run with a range of initial conditions and parameters for states, behaviors, mechanisms,
and environmental processes to understand and visualize network behavior under various conditions. Support for
A/B testing policies, monte carlo analysis and other common numerical methods is provided.

1. Install Dependencies:

Option A: Package Repository Access

IMPORTANT NOTE: Tokens are issued to and meant to be used by trial users and BlockScience employees ONLY. Please replace with and issued token in the script below.

pip3 install pandas pathos fn tabulate
pip3 install cadCAD --extra-index-url https://<TOKEN>@repo.fury.io/blockscience/

Option B: Build From Source

pip3 install -r requirements.txt
python3 setup.py sdist bdist_wheel
pip3 install dist/*.whl

2. Configure Simulation:

Intructions: /Simulation.md

Examples: /simulations/validation/*

3. Import cadCAD & Run Simulations:

Examples: /simulations/*.py or /simulations/*.ipynb

Single Simulation Run: /simulations/single_config_run.py

from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1
from cadCAD import configs

exec_mode = ExecutionMode()

print("Simulation Execution: Single Configuration")
print()
first_config = configs # only contains config1
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
run1_raw_result, tensor_field = run1.main()
result = pd.DataFrame(run1_raw_result)
print()
print("Tensor Field: config1")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()

Parameter Sweep Simulation Run (Concurrent): /simulations/param_sweep_run.py

import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import sweep_config
from cadCAD import configs

exec_mode = ExecutionMode()

print("Simulation Execution: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)

i = 0
config_names = ['sweep_config_A', 'sweep_config_B']
for raw_result, tensor_field in run2.main():
    result = pd.DataFrame(raw_result)
    print()
    print("Tensor Field: " + config_names[i])
    print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
    print("Output:")
    print(tabulate(result, headers='keys', tablefmt='psql'))
    print()
    i += 1

Multiple Simulation Runs (Concurrent): /simulations/multi_config run.py

import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1, config2
from cadCAD import configs

exec_mode = ExecutionMode()

print("Simulation Execution: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)

i = 0
config_names = ['config1', 'config2']
for raw_result, tensor_field in run2.main():
    result = pd.DataFrame(raw_result)
    print()
    print("Tensor Field: " + config_names[i])
    print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
    print("Output:")
    print(tabulate(result, headers='keys', tablefmt='psql'))
    print()
    i =+ 1

The above can be run in Jupyter.

jupyter notebook