There’s Nothing Wrong With a Little “Physics Envy”

When I was kid, I came across this book, Foundation, on my mom’s bookshelf.  As I skimmed the back, I noticed a few key words: “future”, “space”, and “science”.   Being the super-nerdy little guy that I was, I was sold.  I sat down to read it immediately.

I quickly discovered, much to my dismay, that there were no laser guns or explosions, and abandoned Foundation right there on the floor.  What do you want?  I was 12.  I had a very particular taste in books, and that included a preference for space violence.  All I found in Foundation was a bunch of gibberish about some fictional science called “psychohistory” and its ability to accurately predict the behavior of large groups of people.  Bor-ing.

All of this came back to me the other day when I came across an article in The Economist about how the vast amount of data made available by online social networks is impacting the social sciences.  The unnamed author compares current research using this trove of information to the fictional science at the heart of the Foundation series.  Song Chaoming of Northeastern University, a physicist who “moonlights as a social scientist”, has actually developed an algorithm that predicts a person’s location using their cell phone records with an average accuracy rate of 93%.  Another physicist from Northeastern University doing work outside of his usual realm is Alessandro Vespignani, who is using a variety of data sources to model a worldwide travel network and predict the spread of diseases.  Boleslaw Szymanski of the Rensselaer Polytechnic Institute in New York is using social networks to study the power of “committed minorities” to change political discourse and Dirk Helbing of the Swiss Federal Institute of Technology even plans to make a computer model of the entire human society.

There is something counterintuitive about the whole affair, something that cries out from the very pit of the soul, “But we’re human!  We’re special!  We cannot be understood like ‘atoms banging around in the dark!’”  Some philosophers of science often question the wisdom of applying the reductionism so common in physics to social and psychological phenomenon (hence the phrase “physics envy”), and they are often correct.  There is a reason that we are applying network theory to economics in this course: because the reductionisms of macro- and microeconomics fail miserably at some point or another and must be modified and appended.

While the network-centric research mentioned above reeks of the fantastical dreams of science fiction (as noted by our unnamed Economist author), while it reduces the mystery and strangeness of social experience to sets of laws, rules, and heuristics, it does so successfully.  It falls into the category Dan Dennett calls “good reductionism”, reductionism that actually amplifies and increases our intuitive understanding of our world instead of explaining it away.  The new data made available to us by modern technology and social networks is providing grist for the mill of network theory in the same way the new technologies in the early 20th century provided previously unavailable data to physicists and allowed for the study of world on the largest and smallest scales.  There’s nothing wrong with a little physics envy.


One thought on “There’s Nothing Wrong With a Little “Physics Envy””

  1. I like that the article demonstrates the importance of models we use with very abstract concepts to understand basic functions. Economists have to justify why their models and assumptions do not apply to the “real” world and why they use models that contain a lot of abstractions that do not reflect our everyday life. It is true that a lot of details are not taken into account but economists develop models to investigate basic functions and essential influences of factors. A good example to argue for an economist is a simple map of a certain region. Of course there are many details missing, but taking a look at the reduced and important information like a street, highway or a river provides us with the information we need for a direction.

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