Likun Cao

Likun Cao, Jie Hua, James Evans

This paper investigates mechanisms underlying scientific stratification in the transition from elite to mass science. Existing scholarship has examined stratification through the Matthew effect framework, but this approach is increasingly limited as mass, team-based research becomes dominant. While scientists now share institutions and lineages, substantial career outcome differences remain unexplained. We propose integrating demographic concepts into science studies. Drawing parallels between biological families and scholarly lineages as fundamental reproductive units, we adapt the birth order concept to examine how doctoral student sequence within a lineage shapes career trajectories. Using data on over one million U.S. doctoral graduates, we find that later students of the same advisor systematically underperform earlier ones across multiple achievement dimensions, both short and long term. Examining underlying mechanisms reveals that although advisors invest comparable resources in all students, later students receive less cognitive stimulation from mature scholars than peers and specialize in narrower niches under peer differentiation pressure. Both of these factors constrain intellectual development and subsequent success. By introducing a demographic framework, this paper offers new perspectives on scientific stratification and demonstrates how demographic concepts can fruitfully analyze broader social and epistemic systems.

Likun Cao, James Evans

This study offers a new perspective on the depth-versus-breadth debate in innovation strategy, by modeling inventive search within dynamic collective knowledge systems, and underscoring the importance of timing for technological impact. Using frontier machine learning to project patent citation networks in hyperbolic space, we analyze 4.9 million U.S. patents to examine how search strategies give rise to distinct temporal patterns in impact accumulation. We find that inventions based on deep search, which relies on a specialized understanding of complex recombination structures, drive higher short-term impact through early adoption within specialized communities, but face diminishing returns as innovations become "locked-in" with limited diffusion potential. Conversely, when inventions are grounded in broad search that spans disparate domains, they encounter initial resistance but achieve wider diffusion and greater long-term impact by reaching cognitively diverse audiences. Individual inventions require both depth and breadth for stable impact. Organizations can strategically balance approaches across multiple inventions: using depth to build reliable technological infrastructure while pursuing breadth to expand applications. We advance innovation theory by demonstrating how deep and broad search strategies distinctly shape the timing and trajectory of technological impact, and how individual inventors and organizations can leverage these mechanisms to balance exploitation and exploration.

Likun Cao, Rui Pan, James Evans

Existing studies of innovation emphasize the power of social structures to shape innovation capacity. Emerging machine learning approaches, however, enable us to model innovators' personal perspectives and interpersonal innovation opportunities as a function of their prior experience. We theorize and then quantify subjective perspectives and their interaction based on innovator positions within the geometric space of concepts inscribed by dynamic machine-learned language representations. Using data on millions of scientists, inventors, screenplay writers, entrepreneurs, and Wikipedia contributors across their respective creative domains, here we show that measured subjective perspectives predict which ideas individuals and groups will creatively attend to and successfully combine in the future. Across all cases and time periods we examine, when perspective diversity is decomposed as the difference between collaborators' perspectives on their creation, and background diversity as the difference between their experiences, the former consistently anticipates creative achievement while the latter portends its opposite. We analyze a natural experiment and simulate creative collaborations between AI agents designed with various perspective and background diversity, which support our observational findings. We explore mechanisms underlying these findings and identify how successful collaborators leverage common language to weave together diverse experiences obtained through trajectories of prior work. These perspectives converge and provoke one another to innovate. We examine the significance of these findings for team formation and research policy.