Fortunately, the collisions of molecules have their counterpart in human interaction. While molecules collide, people connect, in various sorts of social networks. So while the basic idea behind sociophysics has been around for a while, it really didn’t take off until the new understanding of networks started grabbing headlines.
Social networks have now provided physicists with the perfect playground for trying out their statistical math. Much of this work has paid little heed to game theory, but papers have begun to appear exploring the way that variants on Nash’s math become important in social network contexts. After all, von Neumann and Morgenstern themselves pointed out that statistical physics provided a model giving hope that game theory could describe large social groups. Nash saw his concept of game theory equilibrium in the same terms as equilibrium in chemical reactions, which is also described by statistical mechanics. And game theory provides the proper mathematical framework for describing how competitive interactions produce complex networks to begin with. So if the offspring of the marriage between statistical physics and networks is something like Asimov’s psychohistory, game theory could be the midwife.
Network math offers many obvious social uses. It’s just what the doctor ordered for tracking the spread of an infectious disease, for instance, or plotting vaccination strategies. And because ideas can spread like epidemics, similar math may govern the spread of opinions and social trends, or even voting behavior.
This is not an entirely new idea, even within physics. Early attempts to apply statistical physics to such problems met with severe resistance, though, as Serge Galam has testified. Galam was a student at Tel-Aviv University during the 1970s, when statistical mechanics was the hottest topic in physics, thanks largely to some Nobel Prize–winning work by Kenneth Wilson at Cornell University. Galam pursued his education in statistical physics but with a