Social I

Leaders are often identified in empirical studies by either their position in an organizationally defined hierarchy or by sociometric survey. However, such methods conflate behavioral antecedents and outcomes, including subjective co-participant attributions, with assessment of behaviors themselves. In this study, we propose the “interruption network” as a model of small group structure based on a nonverbal behavior—conversational interruptions—that has been previously validated as a correlate of social status.

Societal transformations are necessary to address critical global challenges, such as mitigation of anthropogenic climate change and reaching UN sustainable development goals. Recently, social tipping processes have received increased attention, as they present a form of social change whereby a small change can shift a sensitive social system into a qualitatively different state due to strongly self-amplifying (mathematically positive) feedback mechanisms.

In agent societies, social norms emerge from interactions and sharing of information between members of a society [1]. Social norms play a crucial role in many contexts in which (human) agents interact and make decisions, such as politics or organizations. Topics of emergence and enforcement of such norms have been studied in various fields, e.g. multi-agent systems [2]. The economic literature has studied the interplay between exogenously defined social norms and incentive mechanisms, but has widely failed to properly address the emergence of social norms [3].

Contemporary urban design is primarily driven by intuition and heuristic methods. Every year, more people move to urban areas in record scale and cities have grown dense and vertical to accommodate this unabated trend. This increasing density has encouraged cities like Singapore to design vertically integrated developments that comprises hospitals, supermarkets, shopping complex, public transportations, restaurants, parks, and other community facilities. However, the current design approaches do not account for, nor tap into the complexity of the system under study.

Although data on ethnicity is important for understanding society members, it is not easy to collect and analyze this data. In this study, we use a large-scale surname dataset and Recurrent Neural Network to build a surname-ethnicity classifier. We also address how to extract and utilize ethnic networks to analyze social phenomena by applying this classifier.

We study the homogeneous symmetrical threshold model with independence (noise) by pair approximation and Monte Carlo simulations on Erdős-Rényi and Watts-Strogatz graphs. The model is a modified version of the famous Granovetter's threshold model: with probability p a voter acts independently, i.e., takes randomly one of two states ±1; with complementary probability 1−p, a voter takes a given state, if a sufficiently large fraction (above a given threshold r) of individuals in its neighborhood is in this state.

Networks are well-established tools to represent social systems, and, thanks to the increased availability of temporally resolved data, temporal networks are now widely used to model their dynamics. Temporal network data are usually presented as a succession of point events (if the data is in continuous time), or as a series of static snapshots networks aggregated on successive time windows. In the latter case, the time window length is arbitrary and does not necessarily correspond to any intrinsic timescale.