Microsoft Research, New York, NY, USA.
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
Nature. 2021 Jul;595(7866):181-188. doi: 10.1038/s41586-021-03659-0. Epub 2021 Jun 30.
Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.
计算社会科学不仅仅是大型数字数据库，还包括构建和分析这些数据库所需的计算方法。它还代表了不同领域的融合，这些领域有着不同的思考和开展科学研究的方式。本文的目的是提供一些关于这些方法之间差异的清晰性，并提出如何将它们有效地整合起来。为此，我们做出了两个贡献。第一个是一个用于思考研究活动的框架，该框架沿着两个维度展开——工作的解释程度，侧重于识别和估计因果效应，以及对结果预测的检验程度——以及这两个优先级如何相互补充，而不是相互竞争。我们的第二个贡献是主张计算社会科学家更多地关注预测和解释的结合，我们称之为综合建模，并概述了实现这一目标的一些实际建议。
