Many of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network formation, inspired by empirical evidence on Twitter data: actors continuously search for the best UGC provider. We theoretically and numerically analyze the network equilibria properties under different meeting probabilities: while featuring common real-world networks properties, e.g., scaling law or small-world effect, our model predicts that the expected in-degree follows a Zipf’s law with respect to the quality ranking. Notably, the results are robust against the effect of recommendation systems mimicked through preferential attachment based meeting approaches. Our theoretical results are empirically validated against large data sets collected from Twitch, a fast-growing platform for online gamers.

Social networks emerge as a result of actors’ linking decisions. We propose a game-theoretical model of socio-strategic network formation on directed weighted graphs, in which every actors’ benefit is a parametric trade-off between centrality measure, brokerage opportunities, clustering coefficient, and sociological network patterns. We use two different stability definitions to infer individual behavior of homogeneous, rational agents from network structure, and to quantify the impact of cooperation. Our theoretical analysis confirms results known for specific network motifs studied previously in isolation, yet enables us to precisely quantify the trade-offs in the space of user preferences. To deal with complex networks of heterogeneous and irrational actors, we construct a statistical behavior estimation method using Nash equilibrium conditions. We provide evidence that our results are consistent with empirical, historical, and sociological observations on real-world data-sets. Furthermore, our method offers sociological and strategic interpretations of random networks models, such as preferential attachment and small-world networks.

IntroductionModeling is an essential part in research of organic light-emitting devices (OLEDs) and speeds up their development. An accurate model can be used to gain further insight into device operation and for optimizing device performance. Difficulties to simulate state-of-theart commercialized OLEDs arise from the fact that these devices are not only made of an active thin-film layer stack, but also contain thick incoherent layers such as color filters or scattering layers that enhance the light out-coupling efficiency of the device. Moreover, the area of OLED devices for lighting applications can reach several square centimeters and potential losses within electrodes cannot be neglected. For the first time, we present a comprehensive model for OLEDs spanning from microscopic charge transport and exciton dynamics to large-area panels. Our approach is able to simulate the influence of electrical conductivity enhancement in large-area electrodes and light extraction enhancement induced by incoherent scattering layers or interfaces. In a first part of this paper we repeat the fundamentals of the microscopic model, previously implemented in the commercial simulation software SETFOS [1]. In the second part we introduce the macroscopic approach for optical and electrical modeling.

A central question in multi-agent strategic games deals with learning the underlying utilities driving the agents' behaviour. Motivated by the increasing availability of large data-sets, we develop an unifying data-driven technique to estimate agents' utility functions from their observed behaviour, irrespective of whether the observations correspond to (Nash) equilibrium configurations or to action profile trajectories. Under standard assumptions on the parametrization of the utilities, the proposed inference method is computationally efficient and finds all the parameters that rationalize the observed behaviour best. We numerically validate our theoretical findings on the market share estimation problem under advertising competition, using historical data from the Coca-Cola Company and Pepsi Inc. duopoly.