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 graduate decline, but on the other hand, only about 20% of individual researchers' productivity follows 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.

翻译：科学产出在职业生涯中遵循规律性模式的预期，是许多学术评价（包括招聘、晋升与终身教职、奖项授予及科研经费分配）的基础。然而，近期对个体产出模式的研究揭示了一个悖论：一方面，研究员年均发表论文数量的平均值稳健遵循"经典轨迹"——快速上升至早期高峰，随后逐渐下降；但另一方面，仅约20%的个体研究者的产出符合这一规律。我们通过将科学产出建模为参数化随机游走过程解决了这一悖论，证明经典轨迹可归因于职业生涯早期至中期产出变化方差的递减。通过实证刻画205所博士授予单位的计算机科学领域2,085条产出轨迹（涵盖1980-2016年间29,119篇论文）的可变结构，我们（i）发现了职业生涯早期及逐年产出变化的显著简单模式，并（ii）证明随机游走模型既能复现平均产出中的经典轨迹，又能捕捉到多数个体层面轨迹的多样性。这些结果揭示了偶然性因素丛集在塑造个体科学产出中的核心作用，为研究如何引导系统性激励与机会以实现聚合效应开辟了新路径。