Abstract. This article reviews computational social science methods and their relation to conventional methodology and statistics. Computational social science has three important features. Firstly, it often involves big data; data sets so large that conventional database and analysis techniques cannot handle them with ease. Secondly, dealing with these big data sets has given rise to analysis techniques that are specially developed for big data. Given the size of the data, resampling and cross-validation approaches become feasible that allow both data-driven exploration and checks on overfitting the data. A third important feature is simulation, especially agent-based simulation. Here size also matters. Agent-based simulation is well known in social science, but modern computer equipment and software allows simulations of unprecedented scale. Many of these techniques, especially the resampling and cross-validation approaches, are potentially very useful for social scientists. Given the relatively small s...

中文翻译：

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计算社会科学方法论，有人吗？

摘要。本文回顾了计算社会科学方法及其与常规方法论和统计学的关系。计算社会科学具有三个重要特征。首先，它经常涉及大数据；数据集太大，以至于常规的数据库和分析技术无法轻松处理它们。其次，处理这些大数据集产生了专门为大数据开发的分析技术。考虑到数据的大小，重采样和交叉验证方法变得可行，它允许数据驱动的探索和检查过度拟合的数据。第三个重要功能是模拟，尤其是基于代理的模拟。这里的大小也很重要。基于代理的模拟在社会科学中是众所周知的，但是现代的计算机设备和软件允许进行前所未有的规模的模拟。其中许多技术，特别是重采样和交叉验证方法，对于社会科学家而言可能非常有用。鉴于相对较小的...