AI 中文总结
研究知识进步速度与研究人员数量等因素的非线性关系，提出并分析相互作用粒子系统模型，发现社区规模扩大时知识进步速度回报递减，建议研究人员随社区发展在知识吸收上投入更多时间。
AI 中文摘要
据统计观察，知识体系的进步速度与诸如活跃研究人员数量或特定领域发表的研究论文数量等因素并非线性相关。此外，随着知识体系的增长，个体研究人员必须在通过创新产生新知识和吸收他人产生的知识之间找到恰当平衡。在此，我们提出并分析了一对相互作用的粒子系统模型，该模型体现了研究社区知识进步的一些典型特征，表现为知识空间中的随机行波。两个粒子系统都表明，随着研究社区规模的扩大，知识进步速度的回报递减，且随着研究社区的发展，研究人员应在知识吸收上比创新投入更多时间。
英文摘要
It has been statistically observed that the speed at which a corpus of knowledge advances does not scale linearly with quantities like the number of active researchers or number of research papers published in a given field. Furthermore, as a body of knowledge grows, individual researchers must somehow strike the right balance between generating new knowledge through innovation and assimilating knowledge generated by others. Here, we propose and analyse a pair of interacting particle system models representing some stylised features of the advancement of knowledge in a research community, captured as a stochastic travelling wave in knowledge space. Both particle systems exhibit a diminishing return in the knowledge advancement speed as research communities grow larger, and suggest that researchers should spend more time on assimilation of knowledge than innovation as their research community grows.
