Social Dynamics

THE sprawling expanse of Dadaab constitutes the largest refugee camp in the world by population, according to the United Nations High Commissioner for Refugees （UNHCR）. Situated in Kenya＇s Garissa County near the border with Somalia, this camp has been a city of exiles since 1993 and currently hosts almost 257,000 refugees who mainly come from Somalia. To put it into perspective, if Dabaab were a normal urban settlement, it would be Kenya＇s fourth largest city by population with an economy worth about $100 million, a result of donor funds delivered here every year.

CONSENSUS FORMATION OF TWO-LEVEL OPINION DYNAMICS

Opinion dynamics have received significant attention in recent years. This paper proposes a bounded confidence opinion model for a group of agents with two different confidence levels. Each agent in the population is endowed with a confidence interval around her opinion wiih radius αd or （1 - α）d, where α∈ （0, 1/2] represents the differentiation of confidence levels. We analytically derived the critical confidence bound dc = 1/（4α） for the two-level opinion dynamics on Z. A single opinion cluster is formed with probability 1 above this critical value regardless of the ratio p of agents with high/low confidence. Extensive numerical simulations are performed to illustrate our theoretical results. Noticed is a clear impact of p on the collective behavior： more agents with high confidence lead to harder agreement. It is also experimentally revealed that the sharpness of the threshold dc increases with a but does not depend on p.

On Dynamic Social Factors for Development of China’s Human Rights Theory

China＇s mainstream human rights theory has undergone tremendous changes since the reform-and-opening scheme was kicked off in the early 1980s. ＂Human rights,＂ which used to be seen as a concept of capitalist ideology, is now an important part of the Chinese system of socialist values.

Formidable females redux:male social integration into female networks and the value of dynamic multilayer networks

The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other,and how these in turn might influence group dynamics.Here,we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus.Our previous analyses of this phenomenon used a monolayer approach,and our aim here is to extend these analyses using a dynamic multilayer approach.To do so,we constructed a temporal series of male and female interaction layers.We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male's centrality in the female grooming layer and changes in male Elo ratings.Our results confirmed our original findings:changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings.However,the multilayer network approach offered additional insights into this social process,identifying how changes in a male's centrality cascade through the other network layers.This dynamic view indicates that the changes in Elo ratings are likely to be short-lived,but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole,especially on reducing intermale aggression(i.e.,aggression directed by males toward other males).We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days,using a variety of methods.Such data are inherently multilevel and multilayered,and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.

Knockouts of high-ranking males have limited impact on baboon social networks

Social network structures can crucially impact complex social processes such as collective behaviour or the transmission of information and diseases. However, currently it is poorly understood how social networks change over time. Previous studies on primates suggest that ＇knockouts＇ （due to death or dispersal） of high-ranking individuals might be important drivers for structural changes in animal social networks. Here we test this hypothesis using long-term data on a natural population of ba- boons, examining the effects of 29 natural knockouts of alpha or beta males on adult female social networks. We investigated whether and how knockouts affected （i） changes in grooming and association rates among adult females, and （2） changes in mean degree and global clustering coefficient in these networks. The only significant effect that we found was a decrease in mean degree in grooming networks in the first month after knockouts, but this decrease was rather small, and grooming networks re- bounded to baseline levels by the second month after knockouts. Taken together our results indicate that the removal of high-ranking males has only limited or no lasting effects on social networks of adult female baboons. This finding calls into question the hypothesis that the removal of high-ranking individuals has a destabilizing effect on social network structures in social animals [Current Zoology 61 （1）： 107-113, 2015].

Finite Size Effects in the Dynamics of Opinion Formation

For some models of relevance in the social sciences we review some exam-ples in which system size plays an important role in the final outcome of the dynamics.We discuss the conditions under which changes of behavior can appear only when the number of agents in the model takes a finite value.Those changes of behavior can be related to the apparent phase transitions that appear in some physical models.We show examples in the Galam's model of opinion transmission and the Axelrod's model of culture formation stressing the role that the network of interactions has on the main results of both models.Finally,we present the phenomenon of system-size stochastic resonance by which a forcing signal(identified as an advertising agent)is optimally amplified by a population of the right(intermediate)size.Our work stresses the role that the system size has in the dynamics of social systems and the inappropriateness of taking the thermodynamic limit for these systems.

The biogeography of group sizes in humpback dolphins(Sousa spp.)

Humpback dolphins(Sousa spp.)are obligate shallow-water and resident species,and they typically live in fission–fusion societies composed of small-sized groups with changeable membership.However,we have scant knowledge of their behavioral ecology,starting with potential factors influencing inter-population variability of their group sizes.Here,we compiled a new global dataset of humpback dolphin group sizes based on 150 published records.Our data indicated an inter-specific consistency of group-living strategy among the 4 species in the Sousa genus,as these species preferred living in small-sized groups with a mean size of mostly no more than 10,a minimum size of single individual or small pairs,and a maximum size of several tens or≈100.In addition,we clearly showed the geographic variations in group sizes of humpback dolphins at a global scale.We found that the geographic variations in humpback dolphin group sizes were primarily associated with the latitude,sea surface temperature,and abundance.To conclude,our findings provide insights into social dynamics and socioecological trade-offs of humpback dolphins,and help better understand how these resident animals adapted to their shallow-water habitats from the perspectives of biogeography and socioecology.

A dynamic logistic regression for network link prediction

In social network analysis, link prediction is a problem of fundamental importance. How to conduct a comprehensive and principled link prediction, by taking various network structure information into consideration,is of great interest. To this end, we propose here a dynamic logistic regression method. Specifically, we assume that one has observed a time series of network structure. Then the proposed model dynamically predicts future links by studying the network structure in the past. To estimate the model, we find that the standard maximum likelihood estimation(MLE) is computationally forbidden. To solve the problem, we introduce a novel conditional maximum likelihood estimation(CMLE) method, which is computationally feasible for large-scale networks. We demonstrate the performance of the proposed method by extensive numerical studies.