What is Behavioural Data Science?

Behavioural Data Science is an emerging, trans-disciplinary field which interconnects behavioural sciences, such as psychology, cognitive sciences, economics, sociology, and business, with fundamental and pure sciences like computer science, statistics, mathematics, engineering, operations research and applied sciences to leverage a far better model to understand and predict human and systems behavior.

Behavioral Data Science is capable of addressing humans and systems behaviour like human well being, supply chain issues in industries, preferences in watching a movie to your personal interest with food and identifying various risk factors. 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.

It is important to understand, behavioral data science addresses not only consumer behavior. Most often people consider Behavioural Data Science as Behavioural Analytics. However, the later is just one element in the first. The human behavior strand provides a range of methodological tools, which originate from research in psychology, decision theory, and behavioral science in order to show how standard methods used in these fields can be enriched by data science techniques to explain human behavior utilizing large datasets.

Behavioral data science emerges as a direct response to the need for studying behavior “in the wild”, outside the “sterile” laboratory setting and controlled environments. Recent advances in computer science, statistics, and mathematics offer several methods which try to model human behaviour. Specifically, the methodology of machine learning and, more recently, deep learning allows us to generate predictions useful for many different facets of human life. Yet, there are many aspects of human life and decision making where machine learning and deep learning fail to provide reliable and accurate results.

One of the reasons why AI fails in many cases to correctly anticipate human behaviour is that AI algorithms tend to ignore existing insights from decision theory and behavioural science. And this is where Behavioral Data Science becomes very helpful. By combining behavioural science models with AI algorithms, we are able to significantly improve and simplify predictions of human behaviour in a wide variety of contexts.

Optimists foresee a world of ‘super-abundance’ with machines satisfying humanity’s basic needs while solving our most serious social challenges, such as malnutrition, food insecurity, poverty, disease, disability and climate change. Behavioral Data Science of the future will also identify, map and explain the interactions between society and AI systems with a view to establishing a robust evidence base that can inform policy responses.

The field’s ambition is to identify ways to embed human values into the heart and operation of AI systems, establishing methods to verify their integrity, accountability and resilience thereby ensuring that they, and the data which feeds them, ultimately operate in the service of successful, democratic, digitally empowered yet human-centred communities.

If you are a passionate Behavioural Scientist, join our circles at https://www.syne.com/circle/behavioural-scientists