Predicting language diversity with complex network

Evolution and propagation of the world's languages is a complex phenomenon, driven, to a large extent, by social interactions. Multilingual society can be seen as a system of interacting agents, where the interaction leads to a modification of the language spoken by the individuals. Two people can reach the state of full linguistic compatibility due to the positive interactions, like transfer of loanwords. But, on the other hand, if they speak entirely different languages, they will separate from each other. These simple observations make the network science the most suitable framework to describe and analyze dynamics of language change. Although many mechanisms have been explained, we lack a qualitative description of the scaling behavior for different sizes of a population. Here we address the issue of the language diversity in societies of different sizes, and we show that local interactions are crucial to capture characteristics of the empirical data. We propose a model of social interactions, extending the idea from, that explains the growth of the language diversity with the size of a population of country or society. We argue that high clustering and network disintegration are the most important characteristics of models properly describing empirical data. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change

A Blessing and a Curse? Political Institutions in the Growth and Decay of Generalized Trust: A Cross-National Panel Analysis, 1980–2009

Despite decades of research on social capital, studies that explore the relationship between political institutions and generalized trust–a key element of social capital–across time are sparse. To address this issue, we use various cross-national public-opinion data sets including the World Values Survey and employ pooled time-series OLS regression and fixed- and random-effects estimation techniques on an unbalanced panel of 74 countries and 248 observations spread over a 29-year time period. With these data and methods, we investigate the impact of five political-institutional factors–legal property rights, market regulations, labor market regulations, universality of socioeconomic provisions, and power-sharing capacity–on generalized trust. We find that generalized trust increases monotonically with the quality of property rights institutions, that labor market regulations increase generalized trust, and that power-sharing capacity of the state decreases generalized trust. While generalized trust increases as the government regulation of credit, business, and economic markets decreases and as the universality of socioeconomic provisions increases, both effects appear to be more sensitive to the countries included and the modeling techniques employed than the other political-institutional factors. In short, we find that political institutions simultaneously promote and undermine generalized trust

In the short term we divide, in the long term we unite: demographic crisscrossing and the effects of faultlines on subgroup polarization

Do strong demographic faultlines breed opinion polarization in work teams? We integrate two theories that have been used to explain faultline effects. The first, the approach of Lau and Mumighan [Lau DC, Mumighan JK (1998) Demographic diversity and faultlines: The compositional dynamics of organizational groups. Acad. Management Rev. 23(4325-340], suggests that in teams with strong faultlines the mechanisms of homophilous selection of interaction partners and persuasive influence cause subgroup polarization, defined as the split of the team into subgroups holding opposing opinions. The second, from sociological and anthropological traditions, emphasizes that crisscrossing actors bridge faultlines because they share demographic attributes with several subgroups. Demographically crisscrossing actors help to prevent polarization in social groups. We argue that Lau and Mumighan's theory implicitly factors in the effects of crisscrossing actors. However, we show that the authors overlooked crucial implications of their theory because they did not consider crisscrossing actors explicitly. Most importantly, we demonstrate that demographic crisscrossing implies that even teams with strong faultlines will overcome polarization in the long run, although they might suffer from it in the short term. We develop and analyze a formal computational model of the opinion and network dynamics in work teams to show the consistency of our reasoning with Lau and Mumighans' theory. The model also revealed another counterintuitive effect: strong faultlines lead to structures of interaction that make teams with strong faultlines faster in arriving at a stable consensus than teams with weak faultlines