Frank Steven A
Department of Ecology and Evolutionary Biology, University of California, Irvine, CA 92697-2525, USA.
Behav Sci (Basel). 2019 Apr 16;9(4):40. doi: 10.3390/bs9040040.
Simple patterns often arise from complex systems. For example, human perception of similarity decays exponentially with perceptual distance. The ranking of word usage versus the frequency at which the words are used has a log-log slope of minus one. Recent advances in big data provide an opportunity to characterize the commonly observed patterns of behavior. Those observed regularities set the challenge of understanding the mechanistic processes that generate common behaviors. This article illustrates the problem with the recent big data analysis of collective memory. Collective memory follows a simple biexponential pattern of decay over time. An initial rapid decay is followed by a slower, longer lasting decay. Candia et al. successfully fit a two stage model of mechanistic process to that pattern. Although that fit is useful, this article emphasizes the need, in big data analyses, to consider a broad set of alternative causal explanations. In this case, the method of signal frequency analysis yields several simple alternative models that generate exactly the same observed pattern of collective memory decay. This article concludes that the full potential of big data analyses in the behavioral sciences will require better methods for developing alternative, empirically testable causal models.
简单模式通常源自复杂系统。例如，人类对相似性的感知会随着感知距离呈指数级衰减。单词使用的排名与单词使用频率之间的对数-对数斜率为负一。大数据的最新进展为描述常见的行为模式提供了契机。这些观察到的规律带来了理解产生常见行为的机制过程的挑战。本文通过近期对集体记忆的大数据分析来说明这一问题。集体记忆随时间遵循一种简单的双指数衰减模式。先是快速衰减，随后是较慢且持续时间更长的衰减。坎迪亚等人成功地将一个两阶段机制过程模型与该模式拟合。尽管这种拟合很有用，但本文强调在大数据分析中需要考虑一系列广泛的替代因果解释。在这种情况下，信号频率分析方法产生了几个能生成完全相同的集体记忆衰减观察模式的简单替代模型。本文得出结论，行为科学中大数据分析的全部潜力将需要更好的方法来开发替代的、可经实证检验的因果模型。
