Author Type

Graduate Student

Date of Award

Fall 10-30-2025

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

November 2025

Department

Physics

Degree Name

Doctor of Philosophy (PhD)

Thesis/Dissertation Advisor [Chair]

Christopher Beetle

Abstract

Synergetics is an emerging paradigm which quantitatively describes and predicts the dynamics of complex living and nonliving systems. It has illuminated how complex systems can spontaneously self-organize by coordinating the activity of their constituent components toward some coherent, collective behavior. This coordinated activity can produce complex macroscopic spatiotemporal patterns without the need for central planning. In this dissertation, we develop and explore generalizations of the Haken-Kelso-Bunz (HKB) model of coordination dynamics. These generalizations include modeling higher-dimensional systems of coupled multistable oscillators with arbitrary diversity and coupling parameters, where the system components have the capacity to “learn” or adapt their intrinsic dynamics through their interactions with one another during multistable coordination. By comparing the coordination dynamics of otherwise isolated subsystems with their dynamics when coupled to third party systems, we show how the coordination dynamics of those subsystems can be modified through their embedding in larger complexes. We show how coupled HKB dyads can have their coordination dynamics modified to be either bistable, monostable, or metastable through their interactions with a third oscillator, a previously unobserved phenomenon called third-party entrainment. Combining this work with generalizations of the HKB model incorporating learning, we show that third-party entrainment can be used to accelerate learning within adaptive multistable networks. Additionally, we discuss practical applications of this theoretical work, including to the results of an empirical study investigating social interactions between groups of older adults with Alzheimer’s Disease and Related Dementias interacting with younger adult facilitators. We use the qualitative insights of the theoretical results, as well as standard methods from data science, to identify predictors of enhanced social engagement to design group interventions for improving the older adults’ social engagement. Finally, we discuss the outlook for the field of Synergetics, including potential applications to neuroscience, where third-party entrainment can describe how diverse brain networks communicate with each other and can be used to design personalized neurostimulation treatment protocols to facilitate healthy coordination between brain regions otherwise locked into pathological coordination patterns associated with disease. We also discuss potential applications for developing artificial intelligence systems which utilize the principles of Synergetics to mimic human consciousness.

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