Research at the Department of Sociology
Social complexity
“The whole does not equal the sum of its parts; it is something different, whose properties differ from those displayed by the parts from which it is formed.”(Durkheim, 1982). This fascinating quote by sociology’s founding father summarizes the core message of our discipline. Often, the behavior of social groups, organizations, and societies appears to be inconsistent with the motives of the individuals who constitute the respective collective. Being proud sociologists, we are convinced that the whole is often different than the sum of the parts. However, unlike Durkheim we are also convinced that collective phenomena can be explained with theories of individual behavior. But if the whole is different than the sum of the parts, what is missing on the right-hand side of the equation? This is, in a nutshell, our overarching research question.
In our work, we explore three theoretical ingredients that can explain why the whole can differ from the sum of the parts: feedback loops, social institutions, and randomness.
Feedback loops
Sociology is so fascinating but also so challenging because we do not study individuals in isolation. Thus, in order to develop a valid model of social collectives, we need more than a good understanding of individual behavior (which is also not easy). In addition, we need to understand how the behavior of individuals affects others’ behavior and how they, in turn, affect the decisions of others, and so on. These chains of reaction can generate very surprising outcomes. In one of our models, for instance, a population of agents who hold identical political opinions at the outset of the dynamics can fall apart into subgroups with maximally opposing opinions, even though the individuals do not seek to distance themselves from others.
Social Institutions
In the past decades, we have witnessed the emergence of new forms of cooperation. People surf couches of strangers; programmers make their code freely available online; laymen team up and create the biggest encyclopedia that ever existed; experts offer help in question-and-answer forums; and people use their private cars as taxicabs. All of these instances of cooperation were completely unheard of just a few years ago. Some scholars interpret this as a change in individuals’ sociality, arguing that in particular young people have very strong pro-social motives. However, there is little evidence that individual motives have changed dramatically. In contrast, new social institutions have been developed that allow individuals to get into contact with others, to place trust in them, and to punish defectors. Uber taxis are a great example, as the Uber app makes it virtually impossible for taxi drivers and customers to hornswoggle each other. This illustrates that one cannot explain the behavior of individuals without a proper understanding of the institutional setting. We study how different social institutions (e.g. signaling, peer-punishment, cheap-talk communication) affect individuals’ behavior in social situations. Plus, we develop and test theories of the evolution of social institutions.
Randomness
There is no doubt that a big part of individual behavior follows patterns that can be described by general theories. However, there is also no doubt that individuals often deviate from these behavioral patterns. These deviations are not of great interest for someone who seeks to explain the behavior of isolated individuals. However, formal models in the fields of statistical mechanics, and evolutionary game-theory predict that deviations can have a decisive impact on the behavior of collectives, even when deviations are rare and entirely random (noise). Our empirical research was the first to demonstrate that micro-deviations can indeed have such decisive effects on macro-outcomes. Of course, noise does not always have macro effects, but there are formal methods that allow one to derive testable hypotheses about the conditions under which noise matters. Testing these hypotheses will lead to a vast improvement of the understanding of social dynamics.
Fake news, social bots, and filter bubbles
It has been warned that the Internet has contributed to recent political events such as Brexit, the election of Donald Trump, the dissemination of far-right conspiracy theories, and the political sucess of populists in many western countries. Fake news, social bots, and filter bubbles, for instance, have been identified as sources of misinformation and opinion polarization. Testing whether these warnings are true, however, is very challenging as this new technology does not affect individuals in isolation. Internet users do not only consume content, but also create, evaluate, and share it; which can generate complex social dynamics. In order to better understand, evaluate, and improve new communication technology, we develop formal models of online communication systems; identify and test critical assumptions of these models as well as models´ predictions about the effects of web technology on public debate, opinion dynamics, and democratic decision making. The ultimate aim is to develop formal models that help elaborate web technology in such a way that undesired effects on social dynamics are prevented.