Computational methods can better help us understand the history and current landscape of the performing arts. For example, we can use network analysis and simulations to study how collaborations within theater companies develop over time, and how specific management decisions lead to different collaborative patterns. For this, we can take advantage of the records of theater productions, which are increasingly available in digitized form. Recent advances in AI can also be used to extract detailed information from videos and texts. For example, we can fine-tune object detection models to identify culturally-specific performing objects in video recordings of performances, and determine how their usage has changed over time. These methods might not useful for all projects, and approaches such as ethnography and practice based-research might be better suited for certain research questions. However, in cases where scale matters and when large datasets are available, computational methods can offer new perspectives on the performing arts that can complement other approaches.
This event is co-hosted by the UCLA School of Theatre, Film & Television Center for Performance Studies and UCLA Remap.
