Here I collect the most recent important news about my work.
Braincast podcast featured my research on nudging and cascades of norm violation in social networks. In only 20 minutes, the podcast explains what sociology is about, summarizes my research with Karl-Dieter Opp on norm violation in networks, and debates problems of nudging.
The following animation movie provides an impression of the dynamics that we studied in our paper.
Both classical social psychological theories and recent formal models of opinion differentiation and bi-polarization assign a prominent role to negative social influence. Negative influence is defined as shifts away from the opinion of others and hypothesized to be induced by discrepancy with or disliking of the source of influence. There is strong empirical support for the presence of positive social influence (a shift towards the opinion of others), but evidence that large opinion differences or disliking could trigger negative shifts is mixed. We examine positive and negative influence with controlled exposure to opinions of other individuals in one experiment and with opinion exchange in another study. Results confirm that similarities induce attraction, but results do not support that discrepancy or disliking entails negative influence. Instead, our findings suggest a robust positive linear relationship between opinion distance and opinion shifts.
One important reason why I love this paper is that it emerged from a wonderful collaboration with Karl-Dieter Opp, my former professor from Leipzig. It was great fun to work with him and I look forward to our future work.
When is ignorance bliss? Disclosing true information and cascades of norm violation in networks
It has been hypothesized that disclosing a population’s true rate of norm violation increases norm-violating behavior. Withholding such information might, thus, prevent the attenuation of useful norms. Analyzing a classical threshold model with flexible thresholds, we show that disclosing the true rate of norm violation can spark cascades of norm violation but can also have the opposite effect, decreasing norm violation and strengthening norm acceptance. The direction of the cascade depends on the initial rate of norm violation. Furthermore, the disclosure effect depends on whether or not the rate of norm violation is disclosed repeatedly, the structure of the social network, and whether individuals’ norm acceptance is inelastic or open to peer-influence.
We want to thank the two reviewers for their very positive evaluations and many helpful comments. Thanks to their input the paper improved very much. Thanks also to the editor for the very quick handling.
This short animation movie gives you an idea of the dynamics that we modeled.
Journal of Economic Theory just published one of my favourite papers.
This paper is the result of a wonderful collaboration with my coauthor Heinrich Nax, helpful input from many friends and colleagues, and extremely valuable comments by our reviewers. Thanks.
The paper is available for free here.
Title: A behavioral study of “noise” in coordination games
Abstract:‘Noise’ in this study, in the sense of evolutionary game theory, refers to deviations from prevailing behavioral rules. Analyzing data from a laboratory experiment on coordination in networks, we tested ‘what kind of noise’ is supported by behavioral evidence. This empirical analysis complements a growing theoretical literature on ‘how noise matters’ for equilibrium selection. We find that the vast majority of decisions (96%) constitute myopic best responses, but deviations continue to occur with probabilities that are sensitive to their costs, that is, less frequent when implying larger payoff losses relative to the myopic best response. In addition, deviation rates vary with patterns of realized payoffs that are related to trial-and-error behavior. While there is little evidence that deviations are clustered in time or space, there is evidence of individual heterogeneity.
PRIMA 2015, a really nice conference on Multi-Agent-Systems. My talk will focus on my past work on social influence in networks and processes of opinion polarisation and will discuss implications for the design of personalised recommender systems.
, FC, Italy
Personalization dramatically changed the Internet. Search engines provide results tailored to the interests of each individual user. Online markets recommend products based on the purchases of other customers who bought similar products in the past. Social networks rank incoming messages according to users’ interests. Personalization is of great help for users and is a multibillion-dollar business area. However, pundits warn that personalization creates cocoons of like-minded users, which makes the Internet boring and uninspiring. More worryingly, however, it has been warned that exposing users to ideas, news, and information that support their views will reinforce their opinions and, thus, foster the polarization of political opinions. These warnings received increasing attention, as opinion polarization might endanger societal cohesion and pose a challenge for political decision-making, as it impedes political consensus formation also on non-controversial issues. Reviewing the literature on social influence in networks and the conditions of opinion polarization, I will demonstrate in this talk that state-of-the art theory leaves us with great uncertainty about the consequences of personalization. In fact, two highly accepted models of opinion dynamics make opposing predictions about the consequences of personalization: Persuasion models, on the one hand, predict that personalization will increase polarization. Rejection models, on the other hand, imply that personalization will foster consensus rather than polarization. There is, thus, a pressing need to clarify which model better captures the effects of personalization. Second, I will describe the design of controlled experiments conducted on online social networks that allow calibrating the agents of existing influence models, which will make it possible to derive reliable predictions about the consequences of web personalization. Third, I will discuss implications of social-influence models for the development of personalized recommender systems. I will sketch different approaches to developing systems that generate personalized outcomes without fostering opinion polarization. I will show that such systems cannot be developed without an accurate model of social influence. On a more general level, I will conclude that the development of technologies on the Internet that have the potential to affect societal dynamics should be guided by theoretical and empirical research. In models of complex systems, even small and seemingly innocent differences in the assumptions about the underlying micro-mechanisms can have critical effects on macro-outcomes. As information technology affects micro-mechanisms, it crucial to understand possible consequences before it is too late to intervene.
I was appointed member of the editorial board of JASSS, the Journal of Artificial Societies and Social Simulation. JASSS is an interdisciplinary journal for the exploration and understanding of social processes by means of computer simulation. Since its first issue in 1998, it has been a world-wide leading reference for readers interested in social simulation and the application of computer simulation in the social sciences.
Our working paper on the effects of web personalization on opinion polarization was awarded the best paper award of the Workshop on Social Influence at th International Conference on Social Informatics (SocInfo 2014).
Pundits and scholars have warned that the personalization of the web and in particular of online social networks fosters interaction between likeminded users and amplifies the polarization of political opinions. We criticize, however, that this warning is based on one particular polarization model and that an alternative and equally prominent theory implies the exact opposite effect, predicting that personalization fosters consensus rather than polarization. We develop a general modeling framework to compare the predictions of the competing models. Using agent-based modeling, we formally demonstrate that the two theories make contradicting predictions and study the conditions of polarization. In conclusion, we call for empirical research on the competing assumptions of the two models, discussing major roadblocks and novel methods to overcome them.
You can download the working paper here.
We developed a new experimental design to test whether or not individuals engage in conflict between social groups because they seek to harm outgroup members. Challenging prominent social psychological theories, we did not find support for such negative social preferences. Nevertheless, subjects heavily engaged in group conflict. Results support the argument that processes that act within social groups motivate engagement in conflict between groups even in the absence of negative social preferences. In particular, we found that “cheap talk” communication between group members fuels conflict. Analyses did not support the notion that the effect of communication results from guilt-aversion processes.
You can find the paper here.
The two conceptualizations of social differentiation seem to be very similar. However, we demonstrate in a new paper that the two differentiation mechanisms lead to different patterns of cultural polarization, radicalization, factionalism, and integration. We also show that they generate these patterns under very different conditions. To this end, we developed an agent-based model that builds on the key assumptions of classical social-influence models supplemented with assumptions about social differentiation, conceptualized as either distancing or striving for uniqueness. This general model allowed us to compare the predictions of the two differentiation models. In closing, we discuss the implications for cultural dynamics in organizations.
This paper was coauthored by Andreas Flache and James Kitts. It appeared in a book called Perspectives on Culture and Agent-based Simulations. Please let me know if you have problems downloading the paper.