cadCAD/README.md

cadCAD

Warning: Do not publish this package / software to any software repository except one permitted by BlockScience.

Description:

cadCAD (complex adaptive systems computer-aided design) is a python based, unified modeling framework for stochastic dynamical systems and differential games for research, validation, and Computer Aided Design of economic systems created by BlockScience. It is capable of modeling systems at all levels of abstraction from Agent Based Modeling (ABM) to System Dynamics (SD), and enabling smooth integration of computational social science simulations with empirical data science workflows.

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 the state rather than direct from an 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.

In essence, cadCAD tool allows us to represent a company’s or community’s current business model along with a desired future state and helps make informed, rigorously tested decisions on how to get from today’s stage to the future state. It allows us to use code to solidify our conceptualized ideas and see if the outcome meets our expectations. We can iteratively refine our work until we have constructed a model that closely reflects reality at the start of the model, and see how it evolves. We can then use these results to inform business decisions.

Simulation Instructional:

• System Model Configuration

• System Simulation Execution

Installation:

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. Replace <TOKEN> with an issued token in the script below.

pip3 install pandas pathos fn funcy 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: /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 (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 Simulations (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