Citation is a fundamental way for humans to acquire and expand on existing knowledge. Although many laws and regularities of citation dynamics have been discovered from scientific citations, it is unclear whether and to what extent these regularities are inherent in how humans seek, use, and create knowledge. We show that, despite many stark differences between these systems, the citation dynamics in science, law, and patents share universal patterns. Given the differences in procedure and incentives that exist between judges, inventors, and scientists, our findings suggest that universal citation dynamics may be innate to any cumulative human knowledge system. Our model demonstrates that the evolution of collective attention and a handful of fundamental mechanisms can produce observed universal patterns of citation dynamics.
Yong-Yeol (YY) Ahn is an Associate Professor at Indiana University, School of Informatics, Computing, and Engineering. He was a Visiting Professor at MIT during 2020-2021. Before joining Indiana University, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute, after earning his PhD in Statistical Physics from KAIST in 2008. His research focuses on developing network science and machine learning methods, and applying them to complex social and biological systems. He is a recipient of several awards, including Microsoft Research Faculty Fellowship and LinkedIn Economic Graph Challenge.
Yong-Yeol (YY) Ahn is an Associate Professor at Indiana University, School of Informatics, Computing, and Engineering. He was a Visiting Professor at MIT during 2020-2021. Before joining Indiana University, he worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute, after earning his PhD in Statistical Physics from KAIST in 2008. His research focuses on developing network science and machine learning methods, and applying them to complex social and biological systems. He is a recipient of several awards, including Microsoft Research Faculty Fellowship and LinkedIn Economic Graph Challenge.
