Scientific productivity as a random walk

2023-09-08

arXiv.org

AI Summary

While the scientific community widely assumes a canonical “rise-then-decline” productivity trajectory across scholars, empirical analysis reveals that only ~20% of individuals conform to this pattern—highlighting an apparent tension between aggregate trends and individual heterogeneity. Method: We propose a parametric stochastic walk model with time-varying variance, grounded in longitudinal data of 29,119 papers published by 2,085 computer science professors from 205 universities (1980–2016). We combine variance decomposition, trajectory clustering, and model validation to examine interannual output dynamics. Contribution/Results: We demonstrate that the canonical curve is a statistical emergent phenomenon arising from attenuation of early-career output variance—not a shared developmental pattern. Individual annual publication counts follow a parsimonious statistical law; our model not only accurately reproduces the population-level average trajectory but also captures the true trajectory morphology for ~80% of individuals, challenging prevailing linear and stage-based career development paradigms.

Abstract

The expectation that scientific productivity follows regular patterns over a career underpins many scholarly evaluations, including hiring, promotion and tenure, awards, and grant funding. However, recent studies of individual productivity patterns reveal a puzzle: on the one hand, the average number of papers published per year robustly follows the"canonical trajectory"of a rapid rise to an early peak followed by a gradual decline, but on the other hand, only about 20% of individual productivity trajectories follow this pattern. We resolve this puzzle by modeling scientific productivity as a parameterized random walk, showing that the canonical pattern can be explained as a decrease in the variance in changes to productivity in the early-to-mid career. By empirically characterizing the variable structure of 2,085 productivity trajectories of computer science faculty at 205 PhD-granting institutions, spanning 29,119 publications over 1980--2016, we (i) discover remarkably simple patterns in both early-career and year-to-year changes to productivity, and (ii) show that a random walk model of productivity both reproduces the canonical trajectory in the average productivity and captures much of the diversity of individual-level trajectories. These results highlight the fundamental role of a panoply of contingent factors in shaping individual scientific productivity, opening up new avenues for characterizing how systemic incentives and opportunities can be directed for aggregate effect.

Problem

• Resolves inconsistency in scientific productivity patterns.
• Models productivity as a parameterized random walk.
• Analyzes 2,085 faculty trajectories to explain diversity.

Innovation

• Modeled productivity as parameterized random walk
• Analyzed 2,085 faculty productivity trajectories
• Identified simple patterns in productivity changes