Abstract: Language trees have been observed to mirror gene trees from local to global scales. This suggests that languages and genes evolve in tandem as the communities that carry them split and diverge, but this model is inconsistent with the widespread mobility often observed between communities. Instead we require a model that explains correlations between genes and languages, while accounting for variable rates of migration and language adoption by individuals. Here we show that the critical variable is that human movements are not random, but highly structured. In the first study of its kind, finely resolved co-phylogenies of languages and genes on a multilingual Indonesian island reveal that marriage systems explain why language trees predict gene trees. Communities of related individuals speaking the same language can persist for many generations, while the languages they speak change or are replaced.

Abstract: In the 90s Robert Axelrod have proposed the canonical model of social interactions [1] explaining one of possible and important mechanisms of dissemination of culture. He have found that depending on initial conditions the system can end up in one of two states: ordered with global culture or disordered with many small subcultures. The dynamics of this model captured complexities of real interactions between people, but the square lattice which was considered is far from satisfying reflection of real-world social networks. Others have studied Axelrod's model deeper on complex networks and it turned out that the structure can have fundamental influence on the behavior of the system. Maxi San Miguel et. al. [2] made the next step by exploring the model of social interactions on coevolving random networks and finding two phase transitions with interesting properties. Unfortunately social networks are as far from randomness as from regularity. In our work we introduce four extensions changing the mechanism of edge rewiring. The models are intended to catch two kinds of interactions - preferential attachment in scientist or actors collaborations and friendship formation in everyday relations. Numerical simulations show that proposed dynamics can lead to power-law distribution of the degree of nodes and high value of clustering coefficient, still keeping the small-world effect in three models. All models are characterized by two phase transitions of different nature. We find new and universal characteristics of the second transition point - abrupt increase of the clustering coefficient, due to the formation of many small complete subgraphs inside the network. [1] R. Axelrod, The dissemination of culture, J. Conflict Res. 41, 203 (1997) [2] F. Vazquez, J. C. Gonzalez-Avella, V. M. Eguíluz, M. San Miguel, Time-scale competition leading to fragmentation and recombination transitions in the coevolution of network and states, Phys. Rev. E 76, 046120 (2007)

Abstract: The famous Zipf law states that the frequency of a word in a text is roughly proportional to the inverse of its rank. When the text is shuffled, this Zipf's law however remains unchanged. In this article, we aim to specify a universal law underlying arrangement of words by using an interval analysis. For each text, we study the fraction of rare words that have ranks above some threshold Q and the length of the return intervals between them. We focus on the frequency of intervals of length r from which we derive the cumulated probability S_Q(r) that the length of an interval is above r, and also the autocorrelation function C_Q(s) of the intervals. When the arrangement of the text is destroyed by shuffling, S_Q(r) is a simple exponential and C_Q(s) is zero for s above zero. We first analyze six long masterpieces in English, French, German, Chinese and Japanese and find that in all texts, for large enough Q values, S_Q(r) follows a clear Weibull function, with its exponent close to 0.7. The return intervals themselves are arranged in a self-similar long-range correlated fashion, where the autocorrelation function C_Q(s) follows a power law, with an exponent between 0.2 and 0.4. These features lead to a pronounced clustering of rare words in the text. We then show how our findings apply on a large scale through 1109 single-author texts. Our results reveal that the arrangement of a text quantified by the return intervals between the words above certain ranks Q, is surprisingly universal, obeying the same laws for all languages considered. We argue that the source of this universality is the human brain.

Abstract: In a complex social system, behavior is often prescribed and regulated by hundreds, or even thousands, of interacting norms. Yet it is individuals that must learn norms—through interaction, exploration heuristics, and local peer influence—and individuals that decide when and how to use them. Little is known about this crucial process. How do individuals selectively, and collectively, navigate and utilize the norms of their social system, and what impact does this process have on that system's development? We provide new answers to this question, by tracking how people use and invoke norms in a real world social system: the English-language Wikipedia. As a community-managed knowledge commons, Wikipedia relies on its norms for governance; they form shared expectations for content creation and conflict resolution. Norms are invoked both directly and indirectly in discussions and arguments on the encyclopedia’s “talk” pages. We sample approximately 11,000 Wikipedia editors and use the detailed, and dated, log of their talk page edits to track the norms each editor references over the course of 15 years. Combined with prior work on Wikipedia’s norm network, this allows us to infer the coarse-grained mental representations individuals use, to test mechanisms for norm learning, to determine how an individual’s invocation of norms is influenced by context, and to test the “moral tribes” hypothesis: that individuals cluster together in distinct regions of the larger norm network.

Abstract: The present study is the first of its kind to employ a complex dynamical systems approach to bridging the gap between social psychological research on stigma, and embodied cognition more broadly. Specifically, we extended current understanding of consequences for revealing devalued identities in close and professional relationships by examining the dynamic structure of movement and language during such disclosures. Using a range of nonlinear time series techniques, including fractal, multifractal, and continuous and categorical recurrence analyses, we investigated the role of antecedent motivational systems on the differential effectiveness of disclosure processes. Participants with concealable stigmatized identities were asked to prepare two disclosure letters to individuals that were unaware of their identity, one to a close friend or relative and the other to a professional colleague. Motivational orientation was manipulated so that half of participants were asked to focus on positive outcomes for the disclosures (approach goals) and the remaining participants were asked to focus on avoiding negative outcomes within the disclosure (avoidant goals). Participants read each letter aloud and their behavior was recorded using multiple motion tracking methods in addition to traditional audio video recordings. Results demonstrate differences in the dynamics of both human movement (i.e., postural sway complexity) and in language usage and form as a function of motivational goals and perceiver for disclosure. Discussion will include implications and future directions for how the differential dynamics of disclosure influence perceptions and may be embodied in the perceiver via complexity matching.