This revolution has already begun in the relatively young field of network science. Prior to the digital revolution, this discipline did have a few famous studies—such as Mark Granovetter’s “The Strength of Weak Ties”. However, reliable data was very hard to come by, and so network science struggled to move beyond its roots in the pure mathematics of graph theory. Today, network scientists have more data than they know what to do with, and luminaries such as Albert-László Barabási and Duncan Watts are blazing a trail and doing some very impressive work.
The data makes all the difference. Ganovetter’s study involved simply talking to people and asking them about their experiences. Now, we can simply tap into Twitter’s API and get an enormous, random sample. We can map out the social graph of the accounts in our sample, and study how information spreads, or fails to, within this structure. And network scientists weren’t waiting around for Twitter’s structured data to come along—Barabási and his peers were examining the linked structure of the web practically from the beginning of the web itself.
The result has been a model of networks that has proven to be very broadly applicable beyond the bounds of human connections. As Barabási explains:
We now know that clustering is present on the Web; we have spotted it in the physical lines that connect computers on the Internet; economists have detected it in the network describing how companies are linked by joint ownership; ecologists see it in food webs that quantify how species feed on each other in ecosystems; and cell biologists have learned that it characterizes the fragile network of molecules packed within a cell.
In other words, our new sources of data on human behavior may not only lead to new discoveries about ourselves, but about the nature of the world we live in.
The application for economics—particularly macroeconomics—is fairly well known. Companies like Linden Labs and Valve have created game universes with real operational economies. The latter has hired an economist whose focus on the European currency crisis was applicable to their own attempts to integrate some of their digital economies. He has already come out with some interesting work on what goes on in these virtual worlds.
The debates around fiscal and monetary policy always seem more like battles between warring religions than attempts to move a science forward. As Noah Smith puts it, there’s not much you can do when the data fundamentally sucks. But game economies may resolve these debates once and for all. It is now conceivable to take a unified gaming economy, split it up and randomly sort players into particular isolated economies, and test out different monetary or fiscal approaches on said economies. Granted, game c
ompanies would need some reason to do this in the first place, but it’s possible that developing booming economies might be in their interest if they find a way to make it impact their bottom line. And regardless, it is at least conceivable now—whereas it was impossible before.
We should expect that not only will young and mature social sciences be improved by this new wealth of good data, but whole new disciplines will emerge to address questions we couldn’t have dreamed to ask before. If ever there was a time for enthusiasts and practitioners of social science to be excited for the future, it is now.
by Adam Gurri November 4, 2013
