Many complex systems are best recognized by their emergent phenomena. Collective motion of animal groups is one such class of systems that has been most studied and understood at the group level. With the advent of better tracking technologies, new opportunities exist to study individual trajectories alongside collective behavior. However, questions of how to most appropriately process and interpret this new type of data are yet to be answered. We propose a set of statistical techniques, rooted in information theory and time series analysis, to be used in attempts to uncover the dynamics of influence within animal groups. Our approaches prove useful in revealing the nature of individual interactions in a variety of Agent Based Models of collective motion. Finally, we apply the techniques to data from multiple tracking projects.
Noyce Conference Room
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