SimCad

Warning: Do not publish this package / software to any software repository except one permitted by BlockScience.

Description:

SimCAD 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.

SimCAD is written in Python 3.

1. Install Dependencies:

pip install -r requirements.txt
python3 setup.py sdist bdist_wheel
pip install dist/SimCAD-0.1-py3-none-any.whl

2. Configure Simulation:

Intructions: /Simulation.md

Examples: /simulations/validation/*

3. Import SimCAD & Run Simulation:

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

/simulations/example_run.py:

import pandas as pd
from tabulate import tabulate

# The following imports NEED to be in the exact order
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
from validation import config1, config2
from SimCAD import configs

exec_mode = ExecutionMode()

print("Simulation Execution 1")
print()
first_config = [configs[0]] # from 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:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()

print("Simulation Execution 2: Pairwise Execution")
print()
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
for raw_result, tensor_field in run2.main():
    result = pd.DataFrame(raw_result)
    print()
    print("Tensor Field:")
    print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
    print("Output:")
    print(tabulate(result, headers='keys', tablefmt='psql'))
    print()

The above can be run in Jupyter.

jupyter notebook