Blogpost by Ganna Pogrebna: “Behavioral Data Science is a new, emerging, interdisciplinary field, which combines techniques from the behavioral sciences, such as psychology, economics, sociology, and business, with computational approaches from computer science, statistics, data-centric engineering, information systems research and mathematics, all in order to better model, understand and predict behavior.
Behavioral Data Science lies at the interface of all these disciplines (and a growing list of others) — all interested in combining deep knowledge about the questions underlying human, algorithmic, and systems behavior with increasing quantities of data. The kinds of questions this field engages are not only exciting and challenging, but also timely, such as:
- How can people’s wellbeing at scale be measured and improved using behavioral data science?
- How can we improve the entire supply chain in creative industries and produce movies, which viewers really want to see?
- How can we better understand machine behavior and algorithmic behavior?
- How can we better model social systems by mapping risk through time?
- How can we design and deliver personalized services ethically and responsibly?
Behavioral Data Science is capable of addressing all these issues (and many more) partly because of the availability of new data sources and partly due to the emergence of new (hybrid) models, which merge behavioral science and data science models. The main advantage of these models is that they expand machine learning techniques, operating, essentially, as black boxes, to fully tractable, and explainable upgrades. Specifically, while a deep learning model can generate accurate prediction of why people select one product or brand over the other, it will not tell you what exactly drives people’s preferences; whereas hybrid models, such as anthropomorphic learning, will be able to provide this insight….(More)”