Social Physics: Measuring and Explaining Social Interactions as Complex Reflexive Systems
If the latest trendy phrase in cybersphere, Social Physics, should refer to anything, that should be an emergent social scientific approach to characterizing social systems as if they were particle aggregates, each component unit moving according to laws dictated by their intrinsic characteristics. The phrase should be used, however, with caution. It is not always warranted to suppose that humans behave mechanistically. They do behave in a patterned manner, they do follow norms and rules of averages, but they do also behave reflexively and have a great capacity for changing an action in the midcourse of completing it. Soros’ much under-appreciated work on reflexivity in human affairs should be more widely studied. As should be his master’s, Popper, vision on human affairs. Reflexivity introduces a teleological factor, along the deterministic logic, to social scientific work. This complicates the job of modeling and explaining human aggregates and their social interactions, yet it makes it no less useful. Phillip Ball’s masterful book is a great example of this type of approach. Pushed by scholars by Pentland, Social Physics also means a specific method of collecting large amounts of data from a variety of sources. The utility of the term is now much diminished, as it becomes another phrase covering the rather ideological term “big data.” Big data is often seen in an overly empiricist manner. Beside designating new opportunities for collecting large amounts of data on demand, the phrase comes with some ideological baggage. For some it also means a a purely inductive and rational-deterministic method for understanding human actions. Unfortunately, such an approach ends up predicting whatever prejudices, often trite, the researcher brought to the table. There is also a third approach, that might provide some new insights to understanding human interactions, especially if they are mediated by communication technologies. Pentland adds to the discussion something a tad more alluring, big data analysis conducted by whatever means, fosters innovation. He also adds to the discussion the possibility of studying human interactions as complex systems defined by human to human and human to machine to human connections. The perspective is potentially interesting and productive if, again, it avoids deterministic and purely inductive modeling.
Reality mining, for example, examines the data about what people are actually doing rather than what they are looking for or saying. Tracking a person’s movements during the day via smartphone GPS signals and credit-card transactions, he argues, are far more significant than a person’s web-browsing habits or social media comments. But Mr. Pentland argues that even the less valuable information in current flood of personal data could help open the door to what he calls “social physics.” That topic is the subject of his new book, “Social Physics: How Good Ideas Spread — The Lesson From a New Science.” Central to the concept of social physics is the ability to measure communication and transactions as never before. Then, that knowledge about the flow of ideas can be used to accelerate the pace of innovation.
via M.I.T.’s Alex Pentland: Measuring Idea Flows to Accelerate Innovation – NYTimes.com – NYTimes.com.
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