Analyzes the self-organizing dramaturgies of dance works that generate performance systematically. Provides effective resources for those who wish to study or realize the potential of performance generating systems in dance within the fields of choreography and dance dramaturgy, dance education, community dance, or dance psychology. 17 b&w illus.
A PDF version of the introduction of this book is available for free in Open Access: Performance Generating Systems in Dance. It has been made available under a Creative Commons Attribution 4.0 International Public license.
Performance generating systems are systematic and task-based dramaturgies that generate performance for or with an audience. In dance, such systems differ in ways that matter from more closed choreographed scores and more open forms of structured improvisation. Dancers performing within these systems draw on predefined and limited sources while working on specific tasks within constraining rules. The generating components of the systems provide boundaries that enable the performance to self-organize into iteratively shifting patterns instead of becoming repetitive or chaotic. This book identifies the generating components and dynamics of these works and the kinds of dramaturgical agency they enable. It explains how the systems of these creations affect the perception, cognition and learning of dancers and why that is a central part of how they work. It also examines how the combined dramaturgical and psychological effects of the systems performatively address individual and social conditions of trauma that otherwise tend to remain unchangeable and negatively impact the human capacity to learn, relate and adapt. The book provides analytical frameworks and practical insights for those who wish to study or apply performance generating systems in dance within the fields of choreography and dance dramaturgy, dance education, community dance or dance psychology. Featured cases offer unique insight into systems created by Deborah Hay and Christopher House, William Forsythe, Ame Henderson, Karen Kaeja and Lee Su-Feh.
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