Social Eavesdropping: A Game-Theoretic Analysis

Here we extend the classic Hawk-Dove model of animal conflict to allow for continuous variation in fighting strengths. Whereas the winner of a fight is chosen at random in the discrete game, in our continuous game, the winner of any fight is the stronger individual, and costs are higher for more evenly matched opponents. We identify the evolutionary stable strength threshold beyond which an animal should be prepared to engage in aggressive behaviour and show that this threshold increases with variance in fighting strength when the costs of aggression are insensitive to the level of strength asymmetry, but decreases with variance when the costs are sensitive to the level of asymmetry. In contrast to the classic discrete game, population-wide aggressive behaviour occurs only when the costs of fighting are zero. It is now known that animals can eavesdrop on the outcome of contests between neighbours and modify their behaviour towards observed winners and losers. We therefore further extend our model to allow for social eavesdropping within networks comprising three individuals. Whereas earlier work showed that eavesdropping increases the frequency of mutually aggressive contests in the discrete game by enhancing the value of victory, here we show that aggression thresholds in the continuous game are always higher with eavesdropping than without it: for sufficiently weak animals, avoiding the costs of challenging an observed winner over-rides the potential benefit of winning, so that eavesdropping reduces the frequency of aggressive encounters. Thus, even though strength is not directly observable, information is extracted from the variation in fighting ability that the classic Hawk-Dove game ignores.     

Social Network Analysis for Program Implementation

This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.     

Topology Analysis of Social Networks Extracted from Literature

In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author’s oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel’s story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network’s evolution over the course of the story.     

Social Network Analysis Shows Direct Evidence for Social Transmission of Tool Use in Wild Chimpanzees

Social network analysis methods have made it possible to test whether novel behaviors in animals spread through individual or social learning. To date, however, social network analysis of wild populations has been limited to static models that cannot precisely reflect the dynamics of learning, for instance, the impact of multiple observations across time. Here, we present a novel dynamic version of network analysis that is capable of capturing temporal aspects of acquisition—that is, how successive observations by an individual influence its acquisition of the novel behavior. We apply this model to studying the spread of two novel tool-use variants, “moss-sponging” and “leaf-sponge re-use,” in the Sonso chimpanzee community of Budongo Forest, Uganda. Chimpanzees are widely considered the most “cultural” of all animal species, with 39 behaviors suspected as socially acquired, most of them in the domain of tool-use. The cultural hypothesis is supported by experimental data from captive chimpanzees and a range of observational data. However, for wild groups, there is still no direct experimental evidence for social learning, nor has there been any direct observation of social diffusion of behavioral innovations. Here, we tested both a static and a dynamic network model and found strong evidence that diffusion patterns of moss-sponging, but not leaf-sponge re-use, were significantly better explained by social than individual learning. The most conservative estimate of social transmission accounted for 85% of observed events, with an estimated 15-fold increase in learning rate for each time a novice observed an informed individual moss-sponging. We conclude that group-specific behavioral variants in wild chimpanzees can be socially learned, adding to the evidence that this prerequisite for culture originated in a common ancestor of great apes and humans, long before the advent of modern humans.     

基于社会网络分析和共词分析的制药技术领域研究

目的:深入研究制药技术领域的发展态势。方法:本研究以Pub Med数据库中收录的制药技术领域研究论文为研究对象,采用社会网络分析、共词分析等定量方法,结合药学专业知识的定性分析,从论文数量年度分布、国家/地区分布及合作网络、研究主题等多个角度开展研究。结果:研究发现:第一,全球制药技术领域共有研究论文95381篇,近几年正处于快速发展阶段;第二,论文发表集中在美国、中国和日本等国家;第三,在定量分析的基础上,结合药学专业知识,共得到15个研究主题,包括点击化学、晶型药物、纳米微球药物等。结论:美国在制药技术领域处于主导地位,中国发文量已经具备一定优势,但发文量和国际影响力与美国相比还是存在较大差距;通过对制药技术领域研究主题的深入分析可以全面把握制药技术领域的研究现状和发展态势。     

Incipient Social Groups: An Analysis via In-Vivo Behavioral Tracking

Social psychology is fundamentally the study of individuals in groups, yet there remain basic unanswered questions about group formation, structure, and change. We argue that the problem is methodological. Until recently, there was no way to track who was interacting with whom with anything approximating valid resolution and scale. In the current study we describe a new method that applies recent advances in image-based tracking to study incipient group formation and evolution with experimental precision and control. In this method, which we term “in vivo behavioral tracking,” we track individuals’ movements with a high definition video camera mounted atop a large field laboratory. We report results of an initial study that quantifies the composition, structure, and size of the incipient groups. We also apply in-vivo spatial tracking to study participants’ tendency to cooperate as a function of their embeddedness in those crowds. We find that participants form groups of seven on average, are more likely to approach others of similar attractiveness and (to a lesser extent) gender, and that participants’ gender and attractiveness are both associated with their proximity to the spatial center of groups (such that women and attractive individuals are more likely than men and unattractive individuals to end up in the center of their groups). Furthermore, participants’ proximity to others early in the study predicted the effort they exerted in a subsequent cooperative task, suggesting that submergence in a crowd may predict social loafing. We conclude that in vivo behavioral tracking is a uniquely powerful new tool for answering longstanding, fundamental questions about group dynamics.     

Social Structure of a Semi-Free Ranging Group of Mandrills (Mandrillus sphinx): A Social Network Analysis

The difficulty involved in following mandrills in the wild means that very little is known about social structure in this species. Most studies initially considered mandrill groups to be an aggregation of one-male/multifemale units, with males occupying central positions in a structure similar to those observed in the majority of baboon species. However, a recent study hypothesized that mandrills form stable groups with only two or three permanent males, and that females occupy more central positions than males within these groups. We used social network analysis methods to examine how a semi-free ranging group of 19 mandrills is structured. We recorded all dyads of individuals that were in contact as a measure of association. The betweenness and the eigenvector centrality for each individual were calculated and correlated to kinship, age and dominance. Finally, we performed a resilience analysis by simulating the removal of individuals displaying the highest betweenness and eigenvector centrality values. We found that related dyads were more frequently associated than unrelated dyads. Moreover, our results showed that the cumulative distribution of individual betweenness and eigenvector centrality followed a power function, which is characteristic of scale-free networks. This property showed that some group members, mostly females, occupied a highly central position. Finally, the resilience analysis showed that the removal of the two most central females split the network into small subgroups and increased the network diameter. Critically, this study confirms that females appear to occupy more central positions than males in mandrill groups. Consequently, these females appear to be crucial for group cohesion and probably play a pivotal role in this species.     

The Social Impacts of Nanotechnology: an Ethical and Political Analysis

This paper attempts some predictions about the social consequences of nanotechnology and the ethical issues they raise. I set out four features of nanotechnology that are likely to be important in determining its impact and argue that nanotechnology will have significant social impacts in—at least—the areas of health and medicine, the balance of power between citizens and governments, and the balance of power between citizens and corporations. More importantly, responding to the challenge of nanotechnology will require confronting “philosophical” questions about the sort of society we wish to create and the role that technology might play in creating it. This in turn will require developing institutions and processes that allow the public to wield real power in relation to technological trajectories. My ultimate contention is that the immediate task established by the likely social impacts of nanotechnology is not so much to develop an ethics of nanotechnology as to facilitate an ethical conversation about nanotechnology.

Explorative Analysis of Wuhan Intra-Urban Human Mobility Using Social Media Check-In Data

Social media check-in data as a geo-tagged information source have been used for revealing spatio-temporal patterns in the field of social and urban study, such as human behavior or public issues. This paper investigates a case study and presents a new method of representing the mobility of people within a city from check-in data. By dividing the data in a temporal sequence, this study examines the overall mobility in the case study city through the gradient/difference of population density with a series of time after computing the population density from the check-in data using an incorporated Thiessen polygon method. By classifying check-in data with their geo-tags into several groups according to travel purposes, and partitioning the data according to administrative district boundaries, various moving patterns for those travel purposes in those administrative districts are identified by scrutinizing a series of spatial geometries of a weighted standard deviational ellipse (WSDE). Through deep analyses of those data by the adopted approaches, the general pattern of mobility in the case city, such as people moving to the central urban area from the suburb from 4 am to 8 am, is ascertained, and different characteristics of movement in those districts are also depicted. Furthermore, it can tell that in which district less movement is likely for a certain purpose (e.g., for dinner or entertainment). This study has demonstrated the availability of the proposed methodology and check-in data for investigating intra-urban human mobility.     

Social Network Analysis Predicts Health Behaviours and Self-Reported Health in African Villages

The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.     

A 3D-Video-Based Computerized Analysis of Social and Sexual Interactions in Rats

A large number of studies have analyzed social and sexual interactions between rodents in relation to neural activity. Computerized video analysis has been successfully used to detect numerous behaviors quickly and objectively; however, to date only 2D video recording has been used, which cannot determine the 3D locations of animals and encounters difficulties in tracking animals when they are overlapping, e.g., when mounting. To overcome these limitations, we developed a novel 3D video analysis system for examining social and sexual interactions in rats. A 3D image was reconstructed by integrating images captured by multiple depth cameras at different viewpoints. The 3D positions of body parts of the rats were then estimated by fitting skeleton models of the rats to the 3D images using a physics-based fitting algorithm, and various behaviors were recognized based on the spatio-temporal patterns of the 3D movements of the body parts. Comparisons between the data collected by the 3D system and those by visual inspection indicated that this system could precisely estimate the 3D positions of body parts for 2 rats during social and sexual interactions with few manual interventions, and could compute the traces of the 2 animals even during mounting. We then analyzed the effects of AM-251 (a cannabinoid CB1 receptor antagonist) on male rat sexual behavior, and found that AM-251 decreased movements and trunk height before sexual behavior, but increased the duration of head-head contact during sexual behavior. These results demonstrate that the use of this 3D system in behavioral studies could open the door to new approaches for investigating the neuroscience of social and sexual behavior.     

Evolution of Treatment Regimens in Multiple Myeloma: A Social Network Analysis

Background

Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM) to understand the patterns of competing treatment selections.

Methods

We used social network analysis (SNA) to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.

Results

135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four “steps” within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.

Conclusion

Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.