"Computational Social Science: Obstacles and Opportunities"

Computational Social Science: Obstacles and Opportunities

New policy piece in Science about the growing, multidisciplinary field of computational social science:

The field of computational social science (CSS) has exploded in prominence over the past decade, with thousands of papers published using observational data, experimental designs, and large-scale simulations that were once unfeasible or unavailable to researchers. These studies have greatly improved our understanding of important phenomena, ranging from social inequality to the spread of infectious diseases. The institutions supporting CSS in the academy have also grown substantially, as evidenced by the proliferation of conferences, workshops, and summer schools across the globe, across disciplines, and across sources of data. But the field has also fallen short in important ways. Many institutional structures around the field—including research ethics, pedagogy, and data infrastructure—are still nascent. We suggest opportunities to address these issues, especially in improving the alignment between the organization of the 20th-century university and the intellectual requirements of the field.

Lots of sensible recommendations, but it seems the problems they highlight are too entrenched structurally. Can we improve?

I really like this point:

[I]n a networked world, how should we deal with the fact that sharing information about oneself intrinsically provides signals about those with whom one is connected?

Indeed, my collaborators and I studied this information flow in a recent 2019 paper ¹, and we found that one’s social ties contain a ton of potential predictive information.