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On this webpage you will find information on my NWO VENI grant for "Learning in Networks" (2013-2016).
Publications resulting from NWO VENI grant:
- Compliance Behavior in Networks: Evidence from a Field Experiment (joint with F. Drago and C. Traxler), American Economic Journal: Applied Economics 12(2), (2020), 1-40.
Abstract: This paper studies the spread of compliance behavior in neighborhood networks in Austria. We exploit a field experiment that varied the content of mailings sent to potential evaders of TV license fees. The data reveal a strong treatment spillover: untreated households are more likely to switch from evasion to compliance in response to mailings received by their network neighbors. Digging deeper into the properties of the spillover, we find that it is concentrated among close neighbors of the targets and increases with the treated households’ diffusion centrality. Local concentration of equally treated households implies a lower spillover.
- Experiments on Belief Formation in Networks (joint with V. Grimm), Journal of the European Economic Association 18(2), (2020).
Abstract: We study belief formation in social networks using a laboratory experiment. Participants in our experiment observe an imperfect private signal on the state of the world and then simultaneously and repeatedly guess the state, observing the guesses of their network neighbors in each period. Across treatments we vary the network structure and the amount of information participants have about the network. Our first result shows that information about the network structure matters and in particular affects the share of correct guesses in the network. This is inconsistent with the widely used naive (deGroot) model. The naive model is, however, consistent with a larger share of individual decisions than the competing Bayesian model, whereas both models correctly predict only about 25%–30% of consensus beliefs. We then estimate a larger class of models and find that participants do indeed take network structure into account when updating beliefs. In particular they discount information from neighbors if it is correlated, but in a more rudimentary way than a Bayesian learner would.
- Learning in Network Games (joint with J. Kovarik and J.G. Romero), Quantitative Economics 9(1) (2018), 85-139. [Online Appendix] [Data] [Best Paper Award]
Abstract: We report the findings of experiments designed to study how people learn in network games. Network games offer new opportunities to identify learning rules, since on networks (compared to, e.g., random matching) more rules differ in terms of their information requirements. Our experimental design enables us to observe both which actions participants choose and which information they consult before making their choices. We use these data to estimate learning types using finite mixture models. Monitoring information requests turns out to be crucial, as estimates based on choices alone show substantial biases. We also find that learning depends on network position. Participants in more complex environments (with more network neighbors) tend to resort to simpler rules compared to those with only one network neighbor.
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