Model
Digital Document
Publisher
Florida Atlantic University
Description
A fundamental question in Complexity Science is how numerous dynamic processes
coordinate with each other on multiple levels of description to form a complex
whole - a multiscale coordinative structure (e.g. a community of interacting people,
organs, cells, molecules etc.). This dissertation includes a series of empirical, theoretical
and methodological studies of rhythmic coordination between multiple agents
to uncover dynamic principles underlying multiscale coordinative structures. First,
a new experimental paradigm was developed for studying coordination at multiple
levels of description in intermediate-sized (N = 8) ensembles of humans. Based
on this paradigm, coordination dynamics in 15 ensembles was examined experimentally,
where the diversity of subjects movement frequency was manipulated to induce
di erent grouping behavior. Phase coordination between subjects was found to be
metastable with inphase and antiphase tendencies. Higher frequency diversity led
to segregation between frequency groups, reduced intragroup coordination, and dispersion
of dyadic phase relations (i.e. relations at di erent levels of description).
Subsequently, a model was developed, successfully capturing these observations. The
model reconciles the Kuramoto and the extended Haken-Kelso-Bunz model (for large- and small-scale coordination respectively) by adding the second-order coupling from
the latter to the former. The second order coupling is indispensable in capturing
experimental observations and connects behavioral complexity (i.e. multistability) of
coordinative structures across scales. Both the experimental and theoretical studies
revealed multiagent metastable coordination as a powerful mechanism for generating
complex spatiotemporal patterns. Coexistence of multiple phase relations gives rise
to many topologically distinct metastable patterns with di erent degrees of complexity.
Finally, a new data-analytic tool was developed to quantify complex metastable
patterns based on their topological features. The recurrence of topological features
revealed important structures and transitions in high-dimensional dynamic patterns
that eluded its non-topological counterparts. Taken together, the work has paved the
way for a deeper understanding of multiscale coordinative structures.
coordinate with each other on multiple levels of description to form a complex
whole - a multiscale coordinative structure (e.g. a community of interacting people,
organs, cells, molecules etc.). This dissertation includes a series of empirical, theoretical
and methodological studies of rhythmic coordination between multiple agents
to uncover dynamic principles underlying multiscale coordinative structures. First,
a new experimental paradigm was developed for studying coordination at multiple
levels of description in intermediate-sized (N = 8) ensembles of humans. Based
on this paradigm, coordination dynamics in 15 ensembles was examined experimentally,
where the diversity of subjects movement frequency was manipulated to induce
di erent grouping behavior. Phase coordination between subjects was found to be
metastable with inphase and antiphase tendencies. Higher frequency diversity led
to segregation between frequency groups, reduced intragroup coordination, and dispersion
of dyadic phase relations (i.e. relations at di erent levels of description).
Subsequently, a model was developed, successfully capturing these observations. The
model reconciles the Kuramoto and the extended Haken-Kelso-Bunz model (for large- and small-scale coordination respectively) by adding the second-order coupling from
the latter to the former. The second order coupling is indispensable in capturing
experimental observations and connects behavioral complexity (i.e. multistability) of
coordinative structures across scales. Both the experimental and theoretical studies
revealed multiagent metastable coordination as a powerful mechanism for generating
complex spatiotemporal patterns. Coexistence of multiple phase relations gives rise
to many topologically distinct metastable patterns with di erent degrees of complexity.
Finally, a new data-analytic tool was developed to quantify complex metastable
patterns based on their topological features. The recurrence of topological features
revealed important structures and transitions in high-dimensional dynamic patterns
that eluded its non-topological counterparts. Taken together, the work has paved the
way for a deeper understanding of multiscale coordinative structures.
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