Growing the Simulation Ecosystem

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.