28,291 research outputs found
The Majority Illusion in Social Networks
Social behaviors are often contagious, spreading through a population as
individuals imitate the decisions and choices of others. A variety of global
phenomena, from innovation adoption to the emergence of social norms and
political movements, arise as a result of people following a simple local rule,
such as copy what others are doing. However, individuals often lack global
knowledge of the behaviors of others and must estimate them from the
observations of their friends' behaviors. In some cases, the structure of the
underlying social network can dramatically skew an individual's local
observations, making a behavior appear far more common locally than it is
globally. We trace the origins of this phenomenon, which we call "the majority
illusion," to the friendship paradox in social networks. As a result of this
paradox, a behavior that is globally rare may be systematically overrepresented
in the local neighborhoods of many people, i.e., among their friends. Thus, the
"majority illusion" may facilitate the spread of social contagions in networks
and also explain why systematic biases in social perceptions, for example, of
risky behavior, arise. Using synthetic and real-world networks, we explore how
the "majority illusion" depends on network structure and develop a statistical
model to calculate its magnitude in a network
Social network analysis shows direct evidence for social transmission of tool use in wild chimpanzees
The authors are grateful to the Royal Zoological Society of Scotland for providing core funding for the Budongo Conservation Field Station. The fieldwork of CH was funded by the Leverhulme Trust, the Lucie Burgers Stichting, and the British Academy. TP was funded by the Canadian Research Chair in Continental Ecosystem Ecology, and received computational support from the Theoretical Ecosystem Ecology group at UQAR. The research leading to these results has received funding from the People Programme (Marie Curie Actions) and from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013) REA grant agreement n°329197 awarded to TG, ERC grant agreement n° 283871 awarded to KZ. WH was funded by a BBSRC grant (BB/I007997/1).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.Publisher PDFPeer reviewe
A Survey on Studying the Social Networks of Students
Do studies show that physical and online students' social networks support
education? Analyzing interactions between students in schools and universities
can provide a wealth of information. Studies on students' social networks can
help us understand their behavioral dynamics, the correlation between their
friendships and academic performance, community and group formation,
information diffusion, and so on. Educational goals and holistic development of
students with various academic abilities and backgrounds can be achieved by
incorporating the findings attained by the studies in terms of knowledge
propagation in classroom and spread of delinquent behaviors. Moreover, we use
Social Network Analysis (SNA) to identify isolated students, ascertain the
group study culture, analyze the spreading of various habits like smoking,
drinking, and so on. In this paper, we present a review of the research showing
how analysis of students' social networks can help us identify how improved
educational methods can be used to make learning more inclusive at both school
and university levels and achieve holistic development of students through
expansion of their social networks, as well as control the spread of delinquent
behaviors.Comment: Huso 201
A Relational Event Approach to Modeling Behavioral Dynamics
This chapter provides an introduction to the analysis of relational event
data (i.e., actions, interactions, or other events involving multiple actors
that occur over time) within the R/statnet platform. We begin by reviewing the
basics of relational event modeling, with an emphasis on models with piecewise
constant hazards. We then discuss estimation for dyadic and more general
relational event models using the relevent package, with an emphasis on
hands-on applications of the methods and interpretation of results. Statnet is
a collection of packages for the R statistical computing system that supports
the representation, manipulation, visualization, modeling, simulation, and
analysis of relational data. Statnet packages are contributed by a team of
volunteer developers, and are made freely available under the GNU Public
License. These packages are written for the R statistical computing
environment, and can be used with any computing platform that supports R
(including Windows, Linux, and Mac).
Robots as Powerful Allies for the Study of Embodied Cognition from the Bottom Up
A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe
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