Arbesman Samuel
Department of Health Care Policy, Harvard Medical School, Boston, MA USA Institute for Quantitative Social Science, Harvard University, Cambridge, MA USA 617-432-7421.
Scientometrics. 2011 Feb;86(2):245-250. doi: 10.1007/s11192-010-0232-6.
It has long been known that scientific output proceeds on an exponential increase, or more properly, a logistic growth curve. The interplay between effort and discovery is clear, and the nature of the functional form has been thought to be due to many changes in the scientific process over time. Here I show a quantitative method for examining the ease of scientific progress, another necessary component in understanding scientific discovery. Using examples from three different scientific disciplines - mammalian species, chemical elements, and minor planets - I find the ease of discovery to conform to an exponential decay. In addition, I show how the pace of scientific discovery can be best understood as the outcome of both scientific output and ease of discovery. A quantitative study of the ease of scientific discovery in the aggregate, such as done here, has the potential to provide a great deal of insight into both the nature of future discoveries and the technical processes behind discoveries in science.
长期以来，人们都知道科学产出呈指数增长，或者更确切地说，呈逻辑斯蒂增长曲线。努力与发现之间的相互作用是显而易见的，而且人们认为这种函数形式的本质是由于科学过程随时间发生的许多变化。在这里，我展示了一种用于检验科学进步难易程度的定量方法，这是理解科学发现的另一个必要组成部分。通过来自三个不同科学学科——哺乳动物物种、化学元素和小行星——的例子，我发现发现的难易程度符合指数衰减。此外，我还展示了如何将科学发现的速度最好地理解为科学产出和发现难易程度两者的结果。像这里所做的对科学发现总体难易程度的定量研究，有可能为未来发现的本质以及科学发现背后的技术过程提供大量的见解。
