Social Graphs: Network Science and Mises

After blathering about social graphs through my last few blog entries, I finally had an intelligent idea: check the local used book store for volumes about social graphs.  Some real research is far better than my stream of consciousness, right?

And lo and behold, I came across Duncan Watts’s book Six Degrees: the Science of a Connected Age.  After leafing through it, I decided that the book was deep enough to warrant purchasing, and I took it home.  Only later did I realize that Duncan is one of the (very few!) people actually creating the new field of network science.

Network science is the brand-new field of science that uses tremendous amounts of computational horsepower to study the structure and behavior of large-scale networks, down to the level of the individual node.  Network science is such a new field because without large, fast computers, it is impossible to reveal any worthwhile results about networks with millions of nodes.

For example, starting on page 56 of his book Watts discusses the efforts of a mathematician at the University of Chicago named Anatol Rapaport who studied epidemiology in the 1950s.  Rapaport was taking a social graph approach to the problem, and actually achieved some initial results using just pencil and paper.  Here is the paragraph that ends Watts’s section on Rapaport:

“Back in Rapaport’s day, this realization was pretty much the end of the road, and reading his original papers you can see that he knew it.  Perhaps if the University of Chicago group had had the same kind of computers that we have today, they might have cracked the problem wide open, and network theory might have taken a very different route.  But they didn’t.  Blinded by a lack of data and hobbled by a dearth of computational power, the theory of random-biased nets struggled as far as its few protagonists could take it with their mathematical intuition, and then effectively disappeared.  It really was an idea for a future age, and like many such ideas, it had to do its time in purgatory.”

When I read this paragraph, I was reminded immediately of Mises.  Especially by the use of the word “purgatory.”  We all know that Mises spent his life in academic purgatory, and we all believe that he was way before his time.  But maybe Mises was also way before his time because the tools that he needed weren’t invented yet.

Mises was right to reject statistical economics – use of statistics automatically reveals that the user has no idea what is actually going on.  A quote from Human Action, section 5 of his “Prices” chapter: “The first variety is represented by the statisticians who aim at discovering economic laws from the study of economic experience.  They aim to transform economics into a quantitative science.  Their program is condensed in the motto of the Econometric Society: Science is measurement.  The fundamental error implied in this reasoning has been shown above …”

But while mathematical, Watts’s work is not statistical, and he includes almost no equations in his book.  Watts is using logic and computational horsepower to find new structure in networks.  He is studying dynamic networks to discover new theories behind their behavior.  One wonders how much further Mises could have gone if he had had a supercomputer in his day.

Social Graphs and Transparency (and Rwanda)

Continuing my thoughts about social graphs, I thought that I would list some assumptions that (the social graph of) my brain currently makes on behalf of social graphs.  If they are written down they can at least be refuted.  So I currently assume that the growth of social graphs will:

1. Grow economies, as both producers and consumers of similar goods and services can more easily find each other

2. Improve education, as a person’s personal graph starts to grow beyond his or her own parent, teacher, boss, pastor, and news anchor

3. Improve government, as formerly-secret political connections come to light

4. Improve diversity, as persons now have a platform and connections to raise awareness of their own thoughts 

5. Lessen violence, as social graphs stretch the boundaries of our own monkeyspheres

6. Increase the number of personalized products and services (as a result of points 1 and 4 above) 

7. Increase the information about markets as consumers compare notes on specific products and services 

And the list goes on. 

Employing a bit of inductive reasoning, one sees that the above list is all about transparency.  In my mind, social graphs increase the transparency of economies, governments, knowledge, products, and even people.  Connections made by social graphs tend to remove the blinders of propaganda, ignorance, dogma, racism, nationalism, and so forth.

An amazing story of connection comes from Paul Rusesabagina, the hotel manager of the Mille Collines during the Rwandan genocide.  Early in the genocide, as Paul was driving his friends and neighbors to hide in the Hotel Diplomat, an army officer ordered him to get out of his car and shoot his friends and neighbors that were with him.  Paul started to use his amazing gifts of persuasion to try to avoid obeying the order, when he discovered that the officer could not look him in the eye!  Paul took this as a sign that he could actually win the argument, and thus save all the lives in his car.  He proceeded to use this new-found knowledge to save hundreds of Rwandan refugees during the genocide. 

And basically Paul’s new-found knowledge was this: looking people in the eye forces them to build a connection to you, forces them to include you in their monkeysphere.  And that connection forces them to have to become a psychopath if they then decide to kill you.  So every time Paul was confronted, he made sure to stand eye-to-eye with his assailant, by getting out of his car or up from his chair, or whatever was necessary.

So I posit that the growth of connections, and even the growth of visualization of those connections, builds the transparency needed to remove the blinders that have helped wars, violence, and coercion to thrive.  When the propaganda of our federal government demonizes Islam, only the transparency of social graphs will shed light on the actual circumstances.  When the socialism of our federal government enslaves the masses, only the transparency of social graphs will show each person as the complete human beings that they are.

(I pause to wonder if the larger social graphs of the younger generation enlarge that generation’s monkeyspheres, and thus logically make them less susceptible to the collectivist lie, and thus more libertarian.) 

Now the above statements assume that current social graphs will avoid balkanization caused by the former barriers of nation, race, and dogma.  But since the power of networks is realized only by their joining together (of not only people but also other networks), any graph that does not grow to some critical mass will wither and die.  And the measure of that critical mass will only grow as social graphs compete with each other.

Possible future blog topics from the above: social graphs and personalized products and services, social graphs and libertarianism, measuring the critical mass of a social graph, monkeyspheres, visualization, transparency and privacy, etc.