時間：2020 / 9 / 17 （週四）下午 2 點
地點：中研院 人社中心 第一會議室
The trends of applying big-data and AI methods in behavioral and social sciences in the 2010s are, to some extent, circular—the rise of big-data and AI methods leads to a re-appreciation of traditional research methods and subsequent development of hybrid approaches. To elaborate on the circularity, this talk will review the relevant literature published in the past decade from the perspectives of data collection, data analysis, and study reproducibility. Specifically, in terms of data collection, behavioral and social sciences were grounded in small data, grew an interest in big data for their potential of testing universality of research findings, and then turned back to collect relatively quality-assured small data. In terms of data analysis, behavioral and social scientists developed theories predominantly using explanatory statistical models, being attracted to but at the same time felt perplexed by highly accurate predictive models that were based on machine learning, and then finally found ways of making predictive models explainable. In terms of study reproducibility, although collection and analysis of big data held the promise of improving sample size, sample diversity, and thus the reproducibility of results and inferences in behavioral and social
sciences, ironically the study methods themselves were becoming irreproducible because the rapidly evolving cyber environments from which research data were gathered might have irreversibly changed, or
the technical threshold of repeating the same analysis was insurmountably high to most researchers in the field. How can behavioral and social scientists respond to the aforementioned changes and impacts brought about by big-data and AI methods?