  This research represents an attempt to ignite the growth of a crowd sourced
simulation ecosystem of reusable subcomponents for Agent-Based Models. Due to
the inherent complexity of simulations, developing this ecosystem will be more
difficult than other knowledge sharing ecosystems, such as machine learning
libraries. This difficulty is due to the number of disparate parts that must
work together to provide a verified and validated simulation. Not only is it
difficult to ensure interoperability of component parts, but each part can also
have a significant number of possible variations. These variations can include
everything from simple choices such as agent order to trained machine learning
models with various architectures. However, due to the dynamics of complex
systems, the need for subcomponents cannot be ignored. Otherwise, the
environment will consist of an incomprehensible number of standalone models,
without reusable parts and without reproducibility. The goal of this research
is to create a seed to encourage the development and sharing of the basic
components of a simulation (data ingestion, behaviors and processes, and
platform extensions) that will grow and develop into a robust ecosystem that
democratizes simulations development and usage for both researchers and
practitioners. A robust simulation ecosystem will help humanity further explore
and probe the depths of complex systems, enhancing understanding and helping
humanity evolve.
