Social influence and Polarization

We often see that during interaction people influence each other and grow more similar in opinions, values, and behavior. Classical modeling studies have demonstrated that social influence should, thus, lead to convergence cascades in which social groups end up in perfect uniformity. Empirical research, on the other hand, does not confirm these convergence tendencies. On the contrary, many empirical studies show that opinion diversity often remains stable and can actually increase over time. Trying to solve this puzzle, we have developed and tested new theories which can explain social diversity despite social influence.

We sought to explain two patterns of diversity. First, we sought to develop a model of opinion clustering, the existence of several internally homogeneous but mutually distinct subgroups. The animation movie shows typical clustering dynamics generated by one of our models. Second, we have developed new explanations of polarization, the development of maximally different subgroups. The surface graph shows a typical polarization process generated by one of our models.

We have developed several new models which are based on alternative mechanisms. All models, however, have in common that they explain clustering and polarization as emergent phenomena. In other words, models generate these social patterns without making the assumption that individuals seek to increase opinion diversity. Actually, one of our models does not even assume that individuals want to be different from any other member of the population. Still, it predicts that an initially homogeneous population splits up into maximally dissimilar subgroups.

In this project I collaborate closely with Andreas Flache, Dirk Helbing and Károly Takács

Clustering dynamics generated by one of our models

The emergence of opinion polarization in a population that was intially characterized by consensus

Relevant publications:

Takasumi Kurahashi-Nakamura, Michael Mäs, Jan Lorenz (in press) Robust clustering in generalized bounded confidence models. Journal of Artificial Societies and Social Simulation.
Mäs, Michael, and Lukas Bischofberger (2015) Will the Personalization of Online Social Networks Foster Opinion Polarization? SSRN working paper
Mäs, Michael, and Andreas Flache (2013) Differentiation without distancing. Explaining bi-polarization of opinions without negative influence. PLoS One 8(11)
Mäs, Michael, Andreas Flache, Károly Takács, and Karen Jehn (2013) In the short term we divide, in the long term we unite: Demographic crisscrossing and the effects of faultlines on subgroup polarization. Organization Science 24: 716-736
Mäs Michael, Andreas Flache and Dirk Helbing (2010) Individualization as Driving Force of Clustering Phenomena in Humans. PLoS Computational Biology 6
Flache, Andreas and Michael Mäs. 2008. How to get the timing right? A computational model of how demographic faultlines undermine team performance and how the right timing of contacts can solve the problem. Computational and Mathematical Organization Theory 14:23-51.
Mäs, Michael, Andreas Flache, and James Kitts (2013) Cultural Integration and Differentiation in Groups and Organizations. in Perspectives on Culture and Agent-based Simulations. Integrating Cultures, edited by Virginia Dignum and Frank Dignum. Springer

Media coverage of my research:

article on wissenrockt.de
article in ETH life
radio interview with Andras Flache on radioeins (20.11.2010)
radio interview with me on DRadio Wissen (7.1.2011)

You might also want to play around with a typical simulation model:

Click here to play around with Axelrod's model of cultural dissemination
Click here to play around with a model of social influence in networks