Here I collect the most recent important news about my work.

New working paper on the complexity approach to sociology

posted Feb 24, 2018, 6:33 AM by Michael Maes   [ updated Feb 24, 2018, 6:36 AM ]

I just uploaded a new article to the SSNR. You can download it here.

In the sociological literature, there is an increasing number of contributions with an approach inspired by the field of complexity science. Here, I identify important commonalities between typical sociological research problems and phenomena studied by complexity researchers, arguing that both fields focus on macro-phenomena that emerge from the behavior of micro-entities. Next, I compare the approach of complexity science with prominent methodological approaches to sociology. I argue that complexity research shares core principles with both structural and structural-individualistic approaches to sociology. However, in particular the approach put forward by analytical sociologists overlaps on the crucial dimensions with complexity research. I also discuss differences between complexity research and the contemporary sociological mainstream, identifying aspects where contemporary sociological research can profit from adopting a complexity perspective. Arguing that large parts of the sociological literature seem to have lost focus on sociology’s original contribution to the understanding of human behavior, I frankly formulate six recommendations on how to refocus contemporary sociological research.  


posted Feb 22, 2018, 9:14 AM by Michael Maes   [ updated Feb 22, 2018, 9:19 AM ]

"Piled Higher and Deeper" by Jorge Cham

I got promoted and got tenure. Thanks RuG and thanks to everyone who supported me in the past years. 

The comic is from:  "Piled Higher and Deeper" by Jorge Cham (

Paper published in Sociological Methods and Research

posted Jul 20, 2017, 2:46 PM by Michael Maes   [ updated Nov 30, 2017, 2:30 AM ]

Great news. Sociological Methods and Research accepted a joint paper with Dirk Helbing. In this paper, we report results of two experiments testing hypotheses about the effects of noise on the micro-level on macro-outcomes.

While there is no doubt that human behavior follows certain patterns, there is also obvious that humans also often deviate from these patterns. Most social scientists do not pay attention to these deviations. However, many theories predict that deviations from the behavioral patterns of individuals can have decisive impact on the behavior of social collectives even when deviations are rare and random. In this paper, we tested this notion, showing that macro-deviations can have macro-effects and that it can be accurately predicted when deviations matter.

You can download the online-first paper here.

Title: Random deviations improve micro-macro predictions. An empirical test.

Abstract: Many sociological theories make critically different macro-predictions when their micro-assumptions are implemented stochastically rather than deterministically. Deviations from individuals’ behavioral patterns described by micro-theories can spark cascades that change macro-outcomes, even when deviations are rare and random. With two experiments, we empirically tested whether macro-phenomena can be critically shaped by random deviations. 96 percent of participants’ decisions were in line with a deterministic theory of bounded rationality. Despite this impressive micro-level accuracy, the deterministic model failed to predict the observed macro- outcomes. However, a stochastic version of the same micro-theory largely improved macro-predictions. The stochastic model also correctly predicted the conditions under which deviations mattered. Results also supported the hypothesis that non-random deviations can result in fundamentally different macro-outcomes than random deviations. In conclusion, we echo the warning that deterministic micro-theories can be misleading. Our findings show that taking into account deviations in sociological theories can improve explanations and predictions. 

Social influence and opinion polarization on the Internet

posted Jun 20, 2017, 11:35 AM by Michael Maes   [ updated Jun 20, 2017, 11:36 AM ]

We finished a working paper summarizing findings of two field experiments and a natural experiment on social influence and processes of opinion polarization on the Internet.  You can find the working paper here 

Title: Micro Influence and Macro Dynamics of Opinions 

Abstract: There is ongoing debate about the effects of social influence on the micro level and resulting opinion polarization on the macro level. We propose a general model that captures prominent, competing micro-level theories of social influence. Conducting a lab-in-the-field experiment, we observe that individual opinions shift linearly towards the mean of the distribution of other opinions. With this finding, we predict the macro-level opinion dynamics resulting from social influence. We test our predictions using data from a second lab-in-the-field experiment and find that social influence reduces opinion polarization. We corroborate these findings with additional field data.    

German podcast features my research on nudging and cascades

posted Sep 20, 2016, 7:27 AM by Michael Maes   [ updated Sep 20, 2016, 7:35 AM ]

The 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.

You can listen to the podcast (in German) here. The paper is published here

The following animation movie provides an impression of the dynamics that we studied in our paper.

Two experiments do not support the negative-influence assumption

posted Jun 23, 2016, 3:10 AM by Michael Maes   [ updated Jun 23, 2016, 3:11 AM ]

Together with Károly Takács, and Andreas Flache I published results from two laboratory experiments testing the assumption that individuals tend to increase opinion differences to disliked others (negative influence). The paper is here.

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.

Paper published in Social Networks

posted May 20, 2016, 5:38 AM by Michael Maes   [ updated Jun 24, 2016, 1:11 AM ]

Social Networks published my paper on the diffusion of norm violation in networks. You can find the paper here.

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.

Paper published in Journal of Economic Theory

posted Dec 23, 2015, 7:32 AM by Michael Maes   [ updated Jan 27, 2016, 5:58 AM ]

The 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. 

Keynote at a Computer Science conference

posted Sep 23, 2015, 10:26 AM by Michael Maes   [ updated Jan 27, 2016, 5:59 AM ]

I was invited to give a keynote at 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.

When?    27 October 2015, 9:30-10:30 
Where?    Bertinoro, FC, Italy
What can social-influence models teach us about the design of personalized recommender systems? 

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 am back in Groningen

posted Feb 11, 2015, 8:30 AM by Michael Maes   [ updated Jan 27, 2016, 5:59 AM ]

Since February 2015 I am assistant professor at the Department of Sociology and the ICS at the University of Groningen, the Netherlands. I am very much looking forward to both teaching sociology in Groningen and starting new research projects with my old friends from the ICS.

On the other hand, I will mis my friends and colleagues from ETH very much. We had a great time and we conducted wonderful research. Thanks to Thomas Chadefaux, Olivia Wooley, Heinrich Nax, Thomas Grund, Lukas Bischofberger, Stefano Balietti, Max Schich, Karsten Donnay, Tobias Kuhn, Richard Mann, Izabela Moise, Evangelos Pournaras, Stefano Bennati, Stefano Duca, Matthias Leiss, Christian Schulz, Dario Biasini, Amin Mazloumian, and Dieter Huber. I also want to thank people from other departments at ETH, in particular Andreas Diekmann, Stefan Wehrli, Ryan Murphy, Kurt Ackermann, Silvana Jud, Robert ten Brincke, and Claudia Jenny.

Finally, I want to thank Dirk Helbing for his support during the past years. He gave me the opportunity to conduct research projects that were very extremely risky, expensive, and time consuming. I am very grateful for this.

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