Integrating explanation and prediction in computational social science
Integrating explanation and prediction in computational social science
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DOI：
10.1038/s41586-021-03659-0
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发表时间：
2021-06-30
期刊：
影响因子：
64.8
通讯作者：
Yarkoni, Tal
中科院分区：
文献类型：
--
作者：
Hofman, Jake M.;Watts, Duncan J.;Yarkoni, Tal
计算社会科学不仅仅是大型数字数据库以及构建和分析它们所需的计算方法。它也代表了不同领域的融合，以及不同的思考和做科学的方式。本透视图的目标是澄清这些方法之间的差异，并提出如何有效地整合它们。为此，我们做了两个贡献。第一个是沿着沿着两个维度思考研究活动的模式--工作的解释性程度，专注于识别和估计因果效应，以及对结果预测的测试考虑程度--以及这两个优先事项如何互补，而不是相互竞争。我们的第二个贡献是倡导计算社会科学家把更多的注意力放在预测和解释的结合上，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。
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.