Increasingly, the engineering of complex systems requires consideration of an intricate web of components and their interaction in diverse social and technical environments. Simulation can assist in designing and testing socio-technical systems by allowing the potential space of outcomes to be explored under given designs. Agent-based models have been developed as a method for building models of complex systems, with great success. Agents may be designed to represent system components and to specify the interactions between them in an incredible level of detail. While popular, the full potential of the methodology to support engineering of complex systems has not been reached, however, because of a set of key challenges. First, there exists a relative lack of robust methods for calibrating agent-based models to theory. Second, there is a paucity of reliable approaches for extracting coarse-grained, system level information as it emerges in agent-based simulations. Third, there is a dearth of schemes for handling uncertainty in the application of agent-based rules to system behavior. Fourth, computation of agent-based models is inefficient when agents are numerous in volume and richly-specified in behavior. Together, these impediments constrain the ability of agent-based modeling to enable prediction, to support decisions, and to facilitate the design, control, and optimization of complex systems. The main objective of this project is to broaden the extensibility of agent-based modeling beyond these constraints. This will be achieved by developing novel computational methods to fuse agent-based modeling, uncertainty measurement and quantification, and mathematics for pattern-extraction.

This project will expand the capabilities of agent-based modeling in supporting the design, engineering, and testing of complex systems. Our initial focus is to develop a prototype scheme that can be applied to complex socio-behavioral systems, but the project is of potential relevance across a diverse array of substantive areas. Indeed, one of our central aims is to provide the glue that can bridge diverse schemes for agent-based simulation across application areas. This could be incredibly useful in reconciling agent-based modeling into a larger "ecology" of mathematical modeling and computation, fundamentally expanding the range of questions that can be posed and systems that can be explored in simulation, while better linking simulation to real-world dynamics.