Others are even more skeptical. Stanley Wasserman, a professor of statistics, psychology, and sociology at Indiana University, says the model is too simplistic to lead to much insight about human behavior. He’s also skeptical about whether any model based purely on abstract mathematical principles—like this one—can accurately portray how people behave. He says predictive models built from experiential data are more reliable.
“The impact [of this work] depends on the general level of acceptance of math in the social sciences by sociologists,” says Krzysztof Kulakowski, a professor of physics and applied computer science at Akademia Gorniczo-Hutnicza University of Science and Technology. Kulakowski has also worked on the problem.
Kleinberg admits that it isn’t certain how mathematical models could prove practical. But he thinks the work suggests some interesting directions. For example, he notes that the new model could help identify key players in a worsening conflict. There were moments during the conflicts the researchers modeled when social subgroups floated between the two main rivals; certain people in these groups could be pivotal. A model like this might call attention to those people in a real conflict and give negotiators a chance to influence them.
Models like the one developed at Cornell could also help improve online social networks. Kleinberg notes that sometimes peoples’ positive or negative sentiments—when rating a product, for example—reflect their social connections rather than their genuine opinions. Social networks could use a model like this to catch this effect and test methods of filtering it out. Social networks might also use a model like this to be more sensitive after members of a group have fallen out with each other. In other words, they might finally know not to recommend that you become friends with your hostile ex-partner’s sister.