Abstract

Deductive and theory-driven research starts by asking questions. Finding tentative answers to these questions in the literature is next. It is followed by gathering, preparing and modelling relevant data to empirically test these tentative answers. Inductive research, on the other hand, starts with data representation and finding general patterns in data. Ahn suggested, in his keynote speech at the seventh International Conference on Computational Social Science (IC²S²) 2021, that the way this data is represented could shape our understanding and the type of answers we find for the questions. He discussed that specific representation learning approaches enable a meaningful embedding space and could allow spatial thinking and broaden computational imagination. In this commentary, I summarize Ahn’s keynote and related publications, provide an overview of the use of spatial metaphor in sociology, discuss how such representation learning can help both inductive and deductive research, propose future avenues of research that could benefit from spatial thinking, and pose some still open questions.

中文翻译：

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计算社会科学中的空间思考

摘要

演绎和理论驱动的研究从提出问题开始。接下来是在文献中寻找这些问题的初步答案。接下来是收集、准备和建模相关数据，以实证检验这些初步答案。另一方面，归纳研究从数据表示和发现数据中的一般模式开始。^{Ahn 在 2021 年第七届计算社会科学国际会议 (IC 2} S ² )的主题演讲中表示，这些数据的表示方式可能会影响我们的理解以及我们为问题找到的答案类型。他讨论了特定的表示学习方法可以实现有意义的嵌入空间，并可以允许空间思维并拓宽计算想象力。在这篇评论中，我总结了 Ahn 的主题演讲和相关出版物，概述了空间隐喻在社会学中的使用，讨论了这种表征学习如何帮助归纳和演绎研究，提出了可以从空间思维中受益的未来研究途径，以及提出一些仍然悬而未决的问题。