Munjung Kim

Scientists shift their research interests—or “move” the space of knowledge—sparking new ideas and novel methodologies. While prior studies have explored how individual characteristics or direct collaborations shape scientists’ research topic shifts, they have largely overlooked the impact of the broader, dynamically evolving behavior of the peer community. Here, we create a continuous mapping of authors’ positions in a high-dimensional topic space to examine how scientists “move” through the knowledge space in relations with their peers. Drawing inspiration from the boids model of collective animal behaviors, we characterize individual research movements using simple rules: alignment (matching the movement of peer scientists), cohesion (clustering with peers on similar topics), and separation (avoiding overlap when peers are too close). Our empirical analysis demonstrates that authors exhibit these propensities, with alignment and cohesion being modulated with the academic prominence. Furthermore, we develop a generative model grounded in these simple rules, which effectively predict future topic shifts based on observed peer dynamics. These findings reveal that the complex evolution of research interests may be driven by simple collective dynamics principles that resemble animal behaviors in physical space.

The rapid rise of AI is poised to disrupt the labor market. , AI is not a monolith; its impact depends on both the nature of the innovation and the jobs it affects. While computational approaches are emerging, there is no consensus on how to systematically measure an innovation's disruptive potential. Here, we calculate the disruption index of 3,237 U.S. AI patents (2015-2022) and link them to job tasks to distinguish between "consolidating" AI innovations that reinforce existing structures and "disruptive" AI innovations that alter them. Our analysis reveals that consolidating AI primarily targets physical, routine, and solo tasks, common in manufacturing and construction in the Midwest and central states. By contrast, disruptive AI affects unpredictable and mental tasks, particularly in coastal science and technology sectors. Surprisingly, we also find that disruptive AI disproportionately affects areas already facing skilled labor shortages, suggesting disruptive AI technologies may accelerate change where workers are scarce rather than replacing a surplus. Ultimately, consolidating AI appears to extend current automation trends, while disruptive AI is set to transform complex mental work, with a notable exception for collaborative tasks.

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Although understanding disruptive breakthroughs and their drivers hinges upon accurately quantifying disruptiveness, the core metric used in previous studies—the disruption index—remains insufficiently understood and tested. Here, after demonstratinits conflicting evaluations for simultaneous discoveries or “multiples”, we propose a new, continuous measure of disruptiveness based on a neural embedding framework that addresses these limitations. Our measure not only better distinguishes disruptive works, such as Nobel Prize-winning papers, from others, but also reveals simultaneous disruptions by allowing us to examine the “twins” that have the most similar future context. By offering a more robust and precise lens for identifying disruptive innovations and simultaneous discoveries, our study provides a foundation for deepening insights into the mechanisms driving scientific breakthroughs while establishing a more equitable basis for evaluating transformative contributions.

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Advisor : Yong-Yeol Ahn, (transferred from Indiana University with advisor)

Advisor : Yong-Yeol Ahn