There is a silent revolution underway in scientific practice: Science is becoming computational. The abundance of computational power and state-of-the-art modeling software is a crucial driving force behind this sea change in scientific research. But that is only part of the story; there is also a fundamental new perspective. Scientists are focusing on the modeling of individual atoms, cells or animals in addition to the more traditional focus on macroscopic attributes of systems. Out of this perspective have emerged Agent-Based Modeling (ABM) languages and tools, which enable researchers to realize their conceptual models of multiple interacting individual entities in a computational format. This advance in computational science is poised to dramatically change the theory, practice and professional motivation of scientists and engineers. This project will build on the NetLogo agent-based modeling environment to develop frameworks, tools, and curricula to further bring the ABM revolution to classrooms and research labs. This research will be applicable not only within engineering, but will greatly affect the theory, use, and teaching of agent-based modeling in both the academic and business worlds in many disparate fields including the natural and social sciences.
Computational agent-based science is less about purely numerical outputs and more about emergent behaviors and qualitative larger-scale patterns, less about static and deterministic equilibria and more about dynamic and stochastic processes; therefore its very nature encourages the modeling of complex systems. This new mindset entails the development of computer architectures, methodologies, technologies and learning tools to carry forward and develop this work. This project will take up that challenge. First, it will develop frameworks describing what ABM is, how to use it, and how it interfaces with other modeling and analysis technologies. Second, this project will create and improve tools that implement the frameworks and enable ABM to take advantage of multiple input/output devices, such as sensors, actuators, and scientific imaging devices. Third, this project will generate course materials, utilize those materials and evaluate their effectiveness to teach new scientists and engineers how to utilize ABM. Doing this research simultaneously affords synergies. Through the development of frameworks to ground agent-based modeling, tools to facilitate agent-based modeling, and curriculum development to teach new scientists and engineers, our goal is to create a common conceptual and technical foundation for agent-based modeling. This foundation will facilitate a shared understanding needed for interdisciplinary collaboration and will reduce duplicated effort between the disciplines.
