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.
We define CSS as the development and application of computational methods to complex, typically large-scale, human (sometimes simulated) behavioral data (1). Its intellectual antecedents include research on spatial data, social networks, and human coding of text and images. Whereas traditional quantitative social science has focused on rows of cases and columns of variables, typically with assumptions of independence among observations, CSS encompasses language, location and movement, networks, images, and video, with the application of statistical models that capture multifarious dependencies within data. A loosely connected intellectual community of social scientists, computer scientists, statistical physicists, and others has coalesced under this umbrella phrase.
Misalignment of Universities
Generally, incentives and structures at most universities are poorly aligned for this kind of multidisciplinary endeavor. Training tends to be siloed. Integrating computational training directly into social science (e.g., teaching social scientists how to code) and social science into computational disciplines (e.g., teaching computer scientists research design) has been slow. Collaboration is often not encouraged, and too often is discouraged. Computational researchers and social scientists tend to be in different units in distinct corners of the university, and there are few mechanisms to bring them together. Decentralized budgeting models discourage collaboration across units, often producing inefficient duplication…
"Computational social scientists must make the case that the result will be more than the publication of journal articles of interest primarily to other researchers. They must articulate how the combination of academic, industrial, and governmental collaboration and dedicated scientific infrastructure will solve important problems for society—saving lives; improving national security; enhancing economic prosperity; nurturing inclusion, diversity, equity, and access ; bolstering democracy; etc..."
How many experiments do you think you’ve participated in over the past year? We’re talking about randomized controlled trials—experiments designed to test the impact of different treatments by randomly assigning you and other participants (often called subjects) to various treatment conditions, like those you might have participated in if you took Psych 101 or if you’ve tried out an experimental drug.So, what’s your number? At first blush, you might think the answer is zero. But unless you live in a bunker with no Internet access, you’ve likely participated in many experiments over the past year. If you logged onto Facebook right now, there’s a good chance you’d be an unwitting subject in a variety of the company’s ongoing experiments as you scroll through your News Feed and peruse the ads being shown to you. You are also likely to be a test subject if you search for an item on Google, watch a movie on Netflix, respond to email surveys, or call companies for customer support.In a dramatic departure from its historic role as an esoteric tool for academic research, the randomized controlled trial has gone mainstream. Historically, experimental methods may have been alien to the managerial toolkit. These days, companies like Google wouldn’t dare make a major change in their platforms without first looking at experiments to understand how it would influence user behavior...Luca, Michael (2020-02-06T22:58:59). The Power of Experiments (The MIT Press) . The MIT Press. Kindle Edition.
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