Animal populations, such as fish and zooplankton in the oceans, and terrestrial species from bison to locusts and midges, can self-organize into schools and swarms through the interactions of individuals with each other and their environment. This project aspires to achieve a new level of understanding of the emergence of such population-level behaviors through the development, implementation, and validation of coarse-grained, systems-level numerical methods for individual-based models of collections of organisms. Systems-level tasks enabled through this computational approach include long-time predictions, stability and bifurcation analysis (which details how observed behavior depends on environmental and organism properties), control, and optimization.
Understanding aggregation behavior is of both theoretical and applied importance, for problems as diverse as fisheries management and pest control. The details of animal behavior involve individual-level dynamics that are too complicated to analyze rigorously. What is of interest, however, are the macroscopic dynamics of populations. This project will build a tool for providing this understanding, in a systematic, flexible, and effective way. The algorithms and ideas that will be developed will also have an impact outside of biology, with potential applications in computational chemistry, materials science, and economics.
