Understanding the dynamical patterns and hot streaks for creative careers
Restricted (Penn State Only)
- Author:
- Liu, Lu
- Graduate Program:
- Information Sciences and Technology (PHD)
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 08, 2022
- Committee Members:
- David Hunter, Outside Unit & Field Member
Sarah Rajtmajer, Major Field Member
C Lee Giles, Chair & Dissertation Advisor
Dashun Wang, Special Member
Mary Beth Rosson, Program Head/Chair
Kenneth Huang, Major Field Member - Keywords:
- the science of science
hot streaks
creative careers
exploration and exploitation - Abstract:
- Recent years have witnessed substantial development in the science of science, an interdisciplinary field aiming to understand in a quantitative fashion the evolution of science, with the potential to create and capture enormous value for science and humanity. The data revolution together with the advanced computational tools open profound opportunities for research in the domain. A particularly promising direction is the lifecycle of creativity, as new data are transforming career-related research from small-scale survey to large-scale profiles with comprehensive publication records, and from scientific careers to broader creative domains. In this dissertation, I will introduce my recent contributions on the quantitative understanding of career dynamics and hot streaks, by applying computational tools in statistics, network science and machine learning to massive career profiles. First, we investigate the hot streak phenomena across scientific, artistic, and cultural careers, and find that hit works within a career show a high degree of temporal regularity, with each career being characterized by bursts of high-impact works occurring in sequence. We demonstrate that the cluster of hit works can be explained by a simple hot-streak model. The model reveals that works produced during hot streaks garner substantially more impact, which suggests that uncovered hot streak phenomenon fundamentally drives the collective impact of an individual. Next, we develop deep learning and network science methods to build high-dimensional representations of publications, art images, and films, allowing us to trace an individual’s career trajectory on the underlying creative space. We investigate the creative strategies around the onset of hot streaks, and find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results not only deepen our quantitative understanding of patterns that govern individual ingenuity and success, but also have implications for identifying and nurturing talents across a wide range of creative domains.