A Framework for Large Scale Complex Adaptive Systems Modeling, Simulation, and Analysis

Complex adaptive systems (CAS) exhibit properties beyond complex systems such as self-organization, adaptability and modularity. Designing models of CAS is typically a nontrivial task as many components are made up of subcomponents and rely on a large number of complex interactions. Studying features of these models also requires specific work for each system. Moreover, running these models as simulations with a large number of entities requires a large amount of processing power. We propose a framework that consists of a modeling language, analysis tools, and simulation engine, called CASTLE. We propose a language, Complex Adaptive Systems Language (CASL), which is designed for simple creation of CAS models while remaining domain agnostic. In particular, an extension to CASL, called CASL-SG, that introduces the concept of 'semantic grouping' allows for large scale simulations to execute on relatively modest hardware. A component of our framework, the observation module, aims to provide an extensible and expandable set of metrics to study key features of CAS such as aggregation, adaptability, and modularity, compatibility with existing analysis tools, while also allowing for more domain-specific techniques.