A few weeks ago I wrote about social physics, a new discipline that aims to help us better understand and predict the behavior of human groups. Social physics is based on the premise that all event-data representing human activity, - e,g,, phone call records, credit card purchases, taxi rides, web activity, - contain a special set of group behavior patterns. As long as the data involves human activity, - regardless of the type of data, the demographic of the users or the size of the data sets, - similar behavioral dynamics apply. These patterns can be used to detect emerging behavioral trends before they can be observed by other data analytics techniques.
Physics, biology and other natural sciences have long relied on universal patterns or principles to detect a faint signal within a large data set, - i.e., the proverbial needle in a haystack. It’s what has enabled the discovery of very short lived elementary particles in physics, - like the Higgs boson in 2013, - amidst the huge amounts of data generated by high energy particle accelerators. In biology, it’s given rise to DNA sequencing and its growing list of applications in medicine, biotechnology and other disciplines.
It’s not surprising that evolutionary biology and natural selection have led to similar universal patterns in the behavior of human crowds. Humans and our ancestors have evolved with the drive to learn from each other because it’s been a major part of our survival over millions of years. And if a new behavior, - whether the result of an innovative idea like the discovery of tools, or a mutation like a larger brain size, - helps a human group better adapt to a changing environment, natural selection will favor the survival of that group over others.
Social physics originated in MIT’s Human Dynamics Lab based on research by professor Alex (Sandy) Pentland, his then postdoctoral associate Yaniv Altshuler and their various collaborators. In 2014, Pentland and Altshuler co-founded Endor, an Israeli-based startup that leverages social physics methods to make fast accurate predictions by analyzing data derived from human behavior.