MANAGEMENT & LEADERSHIP

Information, Communication & Society, 2010

Science, 2010

Social learning (learning through observation or interaction with other individuals) is widespread in nature and is central to the remarkable success of humanity, yet it remains unclear why it pays to copy, and how best to do so. To address these questions we organised a computer tournament in which entrants submitted strategies specifying how to use social learning and its asocial alternative (e.g. trial-and-error) to acquire adaptive behavior in a complex environment. Most current theory predicts the emergence of mixed strategies that rely on some combination of the two types of learning. In the tournament, however, strategies that relied heavily on social learning were found to be remarkably successful, even when asocial information was no more costly than social information. Social learning proved advantageous because individuals frequently demonstrated the highest-payoff behavior in their repertoire, inadvertently filtering information for copiers. The winning strategy (discountmachine) relied exclusively on social learning, and weighted information according to the time since acquisition. Human culture is widely thought to underlie the extraordinary demographic success of our species, manifest in virtually every terrestrial habitat (1-2). Cultural processes facilitate the spread of adaptive knowledge, accumulated over generations, allowing individuals to acquire vital life skills. One of the foundations of culture is social learning-learning influenced by observation or interaction with other individuals (3)-which occurs widely, in various forms,

— The study of Human behavior is much more complicated in various situations, especially on the spectrum of Social Networks. The study of individual behavior cannot be replicated for a group/crowd behavior which can have many social and behavioral dimensions. In the connected world where intelligence is shared among individuals and groups, there exists another kind of complexity which needs to be examined.The complexity of human behaviors as an individual or as a group on the social networks is much more versatile and erratic. The research work studies and analyzes these behaviors in a connected networked intelligent environment and as to how these behaviors are reflected towards Connected Intelligence. Consequently it defines how they can affect the intelligent analytical outcomes. Finally it comes up with a generic model which can be applied in any setup.

2013

Limited attention has been devoted on how (real-life) social networks are elicited and mapped, even less from the viewpoint of mechanism design. This paper surveys the few mechanisms that have been proposed by the experimental literature to this purpose. These mechanisms differ in their incentive structure, as well as in the means of reward they employ. We compare these elicitation devices on the basis of the estimated differences in the characteristics of the induced networks, such as the number of (mutual) links, correspondence and accuracy. Our main conclusion is that the elicited network architecture is itself dependent on the nature (and the structure) of the incentives. This, in turn, should provide the social scientist with guidelines on the most appropriate device to use, depending on the research objectives.

This study deals with online personal social networks (i.e., ego-networks) of youth 12-18 years old, in the Netherlands and investigates if and how these networks operate with respect to learning. The online ego-networks of youth, and the potential these networks have for learning, are largely unexplored. What kinds of resources do youth have access to through their networks? With whom do they connect? How can we characterize these relations in terms of the frequency they meet online and offline, emotional closeness, topics of conversation, and geographical dispersion of contacts? What kinds of networks provide learning experiences? How can we predict these networks? This study describes in detail the characteristics of these ego-networks. Furthermore, we tested the claim that learning in online networks is a likely result of frequent network activity. Particularly we questioned if popular social network activities such as sharing links, giving feedback and editing or creating artefacts together online would be related to the discovery of new information. With a multi-level analysis model we were able to differentiate the individual influences and the influence of their egonetworks on the frequency of discovering new information and overall network activity. The results showed that these network activities strongly and positively predicted discovery of new information. With respect to the people with whom youth construct their networked communities, the study show that youth connects online primarily with contacts who are similar, who live close by and who are emotionally close. In contrast to claims in the literature in which innovation and learning is associated with heterogeneous contacts, these results show that youth chooses homogeneous, emotionally close and locally based online relationships to explore their interests, to relate to and to discover new information together. A possible explanation may be that in this age group, youth are still fostering the ties to their immediate community and that being accepted and being similar may allow for a safer exploring of the world. These results suggests that rather than stating how a particular kind of tie or network predicts innovation, or is likely to provide new information, these relations need to be contextualised and understood from their local, specific settings and social dynamics.

IGI Global eBooks, 2013

Nowadays, acquisition of trustable information is increasingly important in both professional and private contexts. However, establishing what information is trustable and what is not, is a very challenging task. For example, how can information quality be reliably assessed? How can sources credibility be fairly assessed? How can gatekeeping processes be found trustworthy when filtering out news and deciding ranking and priorities of traditional media? We are studying an Internet-based solution to a human-based ancient issue and we call this solution Polidoxa, from Greek "poly" (πολύ), meaning "many" or "several" and "doxa" (δόξα), meaning "common belief" or "popular opinion". This old problem will be solved by means of ancient philosophies and processes with truly modern tools and technologies. This is why this work required a collaborative and interdisciplinary joint effort from researchers with very different backgrounds and institutes with significantly different agendas. Polidoxa aims at offering: 1) a trust-based search engine algorithm, which exploits stigmergic behaviours of users' network, 2) a trust-based social network, where the notion of trust derives from network activity and 3) a holonic system for bottom-up self-protection and social privacy. By presenting the Polidoxa solution, this work also describes the current state of traditional media as well as newer ones, providing an accurate analysis of major search engines such as Google and social network (e.g., Facebook). The advantages that Polidoxa offers, compared to these, are also clearly detailed and motivated. Finally, a Twitter application (Polidoxa@twitter) which enables experimentation of basic Polidoxa principles is presented.

Information Processing & Management, 2009

Journal of The Royal Society Interface, 2014

Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

There is extensive literature which shares the effectiveness of collaborative learning. Instructional strategies, which have been used to guide collaboration efforts include derivatives of inquirybased learning such as project based, problem based, experiential, service and challenge based learning. Initially, the World Wide Web allowed greater ease of connecting to educators, hence the potential for collaboration increased. The significant advantage, which was provided with the Web 2.0 era, was to accelerate the opportunities for educational innovations through social networks in which educators and learners are able to engage in a two way interaction both individually and in groups, collaboratively. The authors of this study believe that many of these innovations align directly with the premise of connectivism. In this study, Educational Social Networking Sites (SNSs) such as Twitter, LinkedIn, Classroom 2.0, Facebook, Google Plus, Plurk Educator’s PLN, Sophia, Learn Central, ISTE Community, WhoTeaches Edutopia, Technology Integration in Education, The 21st Century Teacher, Better Lesson Diipo, Intel Education Teachers Engage Community, Everloop, Edudemic, K12 Advantage, Collaborative Translation and Second Life virtual worlds will be investigated in terms of educational connectedness and efficacy. Specific examples will be examined which highlight the power of social networking for effective teaching and learning within the scope of Distance Education and Open Educational Resources (OER). Key Words: Educational Social Networking Sites (SNSs), Connectivism, Collaboration, Distance Education, OER.

"We study individual ability to memorize and recall information about friendship networks using a combination of experiments and survey-based data. In the experiment subjects are shown a network, in which their location is exogenously assigned, and they are then asked questions about the network after it disappears. We find that subjects exhibit three main cognitive biases: (i) they underestimate the mean degree compared to the actual network; (ii) they overestimate the number of rare degrees; (iii) they underestimate the number of frequent degrees. We then analyze survey data from two `real' friendship networks from a Silicon Valley firm and from a University Research Center. We find, somewhat remarkably, that individuals in these real networks also exhibit these biases. The experiments yield three further findings: (iv) network cognition is affected by the subject's location, (v) the accuracy of network cognition varies with the nature of the network, and (vi) network cognition has a significant effect on economic decisions."