Relevant bibliographies by topics / Social intelligence

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Social intelligence'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Social intelligence"

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GANDHI.A, SONIA, and MANIYAMMAI N. MANIYAMMAI.N. "Emotional Intelligence - An Ingredient of Social Intelligence." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 44–45. http://dx.doi.org/10.15373/2249555x/apr2014/229.

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Herzig, Andreas, Emiliano Lorini, and David Pearce. "Social Intelligence." AI & SOCIETY 34, no. 4 (December 28, 2017): 689. http://dx.doi.org/10.1007/s00146-017-0782-8.

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Wawra, Daniela. "Social Intelligence." European Journal of English Studies 13, no. 2 (August 2009): 163–77. http://dx.doi.org/10.1080/13825570902907193.

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Schneider, Karl R. "Social intelligence." Information Processing & Management 28, no. 4 (July 1992): 543–45. http://dx.doi.org/10.1016/0306-4573(92)90023-s.

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Turaev, Abdunabi Rustamovich. "The Social Essence Of Intelligence." American Journal of Social Science and Education Innovations 03, no. 04 (April 21, 2021): 88–92. http://dx.doi.org/10.37547/tajssei/volume03issue04-13.

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In this article, the social essence and manifestation features of intelect have been studied epistemologically. The essence of the concept of “perception” is revealed through the study of Intelectni gnoseological. There was also a comparative analysis of the psyche of animals and the process of perception in humans.
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SÎRBU, Marinela, and Roxana MAIER. "THE EMOTIONAL INTELLIGENCE – SOCIAL INTELLIGENCE RELATIONSHIP IN THE PARTICULAR CASE OF PSYCHOTHERAPISTS AND SOCIAL WORKERS." Review of the Air Force Academy 14, no. 2 (December 8, 2016): 143–48. http://dx.doi.org/10.19062/1842-9238.2016.14.2.18.

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Annapoorani, B. "Social Intelligence and Social Media." Shanlax International Journal of Arts, Science and Humanities 11, S1-Nov (November 20, 2023): 72–76. http://dx.doi.org/10.34293/sijash.v11is1-nov.6858.

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This paper explores the dynamic interplay between social intelligence and social media in the contemporary digital landscape. Social intelligence, defined as the ability to understand, navigate, and succeed in social situations, is significantly influenced by the pervasive presence of social media platforms. The essay delves into how social media shapes and is shaped by social intelligence, highlighting the reciprocal relationship between the two. It discusses the positive impact of social media in enhancing social intelligence through exposure to diverse perspectives, fostering communication skills and building cultural awareness. However, it also addresses the ethical considerations associated with leveraging social intelligence for influence through social media. The abstract emphasizes the need for responsible and transparent use of social intelligence in the digital realm, recognizing the transformative potential of this relationship on individual and collective social dynamics.
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Fitri, Rahmadhani, Ganda Hijrah Selaras, Rika Andima, Rizki Rahman Putra, Resti Fevria, and Indra Hartanto. "Multiple Intellegences of Social Sciences and Linguistics Students Grade X Crossing Biology Interest." Bioeducation Journal 3, no. 2 (December 31, 2019): 101–12. http://dx.doi.org/10.24036/bioedu.v3i2.241.

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Multiple intelligences are the development of intelligence of the brain or intelligence quotient (IQ), emotional intelligence or emotional quotient (EQ), and spiritual intelligence or Spiritual Quotient (SQ). The intelligence consists of nine levels of intelligence is known as the Multiple Intelligences (MI) which includes: linguistic intelligence, mathematical-logical intelligence, visual spatial intelligence, physical-kinesthetic intelligence, musical intelligence, interpersonal intelligence, intrapersonal intelligence, naturalist intelligence, and existential lntelligence. This theory shows that there is no human activity that uses only one intelligence, but uses all intelligence that exists in humans. Intelligence is a single talent that students use in situations of solving any problem. Every student has a different intelligence. This is because there are several factors that influence it, namely the hereditary factor, interest factor, formation factor, maturity factor, and freedom factor.This research are descriptive study which aims to reveal the level of MI of social sciences and linguistcs student grade X crossing biology interest at SMAN 1V Koto KampungDalam. Results of the study, it was faund that the most dominant intelligence in social science students is naturalist intelligence. In the linguistics scienscestudents, the most dominant intelligence is existential intelligence.
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Nijholt, A., Oliviero Stock, and Toyoaki Nishida. "Social intelligence design in ambient intelligence." AI & SOCIETY 24, no. 1 (February 18, 2009): 1–3. http://dx.doi.org/10.1007/s00146-009-0192-7.

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Ivashkevych, Eduard, and Vitalii Spivak. ""Intelligence, Social and Emotional Intelligence: Correlation of Concepts in Modern Psychology "." Collection of Research Papers "Problems of Modern Psychology", no. 61 (September 28, 2023): 9–29. http://dx.doi.org/10.32626/2227-6246.2023-61.9-29.

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The purpose of our article is to carry out an analysis of the author’s research on social intelligence according to Structural and Functional Approach; describe our researches of Social Intelligence; to propose own definition of “emotional intelligence”; to show the correlation of concepts “intelligence”, “social intelligence” and “emotional intelligence” in Modern Psychology. Methods of the research. The following theoretical methods of the research were used to solve the tasks formulated in the article: the categorical method, structural and functional methods, the methods of the analysis, systematization, modeling and generalization. The results of the research. We think, that social intelligence is a system of cognitive characteristics of the individual. It consists of three basic components: social-perceptive abilities, social imagination and social technique of communication. So, the effectiveness of subject-subject communication largely depends on the formation of social intelligence. Social intelligence is considered as a certain cognitive component of communicative competence, which is defined as the ability of the individual to accept the position, point of view of another person, to predict his/her behavior, to solve effectively various problems arising between subjects of dialogical interaction. Conclusions. We believe that emotional intelligence is defined as a set of non-cognitive abilities, competencies or skills that affect a person’s ability to face challenges in the external environment, the emotional intelligence should be attributed to the empathic aspect of social intelligence. That is, we will consider emotional intelligence as a component of social intelligence. Let’s justify our own point of view. In this context, emotional intelligence is a set of general personality’s abilities, interconnected four skills, such as: awareness of one’s own emotions, the ability to determine what emotion the person feels at a given moment in time, to determine what basic emotions consists of this understanding; the ability to manage one’s own emotions (to change the intensity of emotions), to determine the source and the cause of their occurrence, the degree of usefulness; understanding other people’s emotions, determining emotional states by their verbal and non-verbal manifestations; management of other people’s emotions, providing purposeful action on emotions. At the same time, we’d like to make a generalization regarding the definition of emotional intelligence. Emotional intelligence, as we see, is the ability of an individual to perceive, evaluate and express emotions adequately; the individual’s ability to generate feelings when they contribute to thinking, to understand emotions and knowledge related to these emotions, the individual’s ability to regulate emotions, contributing to one’s own emotional and intellectual growth.
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Dissertations / Theses on the topic "Social intelligence"

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Corso, Lisa. "Social Intelligence: Social Skills Competence and Emotional Intelligence in Gifted Adolescents." TopSCHOLAR®, 2002. http://digitalcommons.wku.edu/theses/647.

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Two schools of thought diverge into an ongoing debate as regards to the social intelligence of gifted youth. One view holds that the gifted are often maladjusted (Chronbach, 1960; Hollingworth, 1942). The contrary view is that they are more likely to be well adjusted, with overall above average social and emotional intelligence (Allen, 2000; Chesser, 2001; Kihlstrom & Cantor, 2000; Neihart, 1999). The current research is consistent with views supporting enhanced overall social and emotional intelligence of gifted youth. Some researchers have argued that emotional intelligence and social skills competence are subsets of social intelligence (Chesser, 2000; Greenspan, 1979; Kihlstrom & Cantor, 2000; Morgan, 1996; Salovey & Mayer, 1990,1993). In order to sample the domains of social intelligence in gifted adolescents, a measure of emotional intelligence (Bar-On Emotional Quotient - Inventory: Youth Version) was combined with a measure of social skills competence (Social Skills Rating Scale- Secondary Student and Parent Forms). Participants were students (n = 100) in a very selective summer program for gifted adolescents, and parent respondents (n = 76). This research addresses the following four hypotheses as regards to the relationship between social skills competence and emotional intelligence in gifted adolescents. It was hypothesized that gifted adolescents were expected to score in the above average range on emotional intelligence. Gifted adolescents scored significantly higher than norm samples on the scales of Adaptability, Stress Management and the Total EQ composite. It was also hypothesized that gifted students have above average social skills ratings. Gifted students scored significantly higher than average on all scales of the Social Skills Rating System (SSRS) Secondary Student form. Third, it was hypothesized that the constructs of emotional intelligence and social skills competence are related as regards to the self ratings of gifted adolescents. The SSRS Total Scale and all scales of Bar-On EQ-i: YV were significantly correlated. Fourth, parental ratings were consistent with the hypothesis that they would rate their adolescents as having overall average social skills. The means for overall social skills on the Total Scale and Cooperation subscale were in the average range. The Assertion subscale was found to be below average. Parent ratings were significantly above average in the areas of Responsibility and Self-Control, which may be strengths for gifted youth. A significant positive relationship was found between the composites for social skills competence and emotional intelligence. Some differences were noted between the sub-scales of these constructs, suggesting that gifted individuals may tend to have a specific profile of strengths and weaknesses in these domains. This relationship is also consistent with hierarchical theories of social intelligence maintaining that social skills and emotional skills are separate areas of related abilities. These findings suggest that social intelligence domains are important in drawing a complete profile of differential abilities in gifted students. In talent identification, it may be useful to combine measures of social skills competence and emotional intelligence with cognitive evaluations to provide a wider range of information as regards to the abilities of the gifted.
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Murry, Robyn Anne. "Social intelligence, general intelligence, and field-dependence-independence /." The Ohio State University, 1988. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487588939090887.

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Conzelmann, Kristin [Verfasser]. "Social intelligence and auditory intelligence : useful constructs? / Kristin Conzelmann." Magdeburg : Universitätsbibliothek, 2012. http://d-nb.info/1053914350/34.

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Kaminski, Nicholas James. "Social Intelligence for Cognitive Radios." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/25786.

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This dissertation introduces the concept of an artificial society based on the use of an action based social language combined with the behavior-based approach to the construction of multi-agent systems to address the problem of developing decentralized, self-organizing networks that dynamically fit into their environment. In the course of accomplishing this, social language is defined as an efficient method for communicating coordination information among cognitive radios inspired by natural societies. This communication method connects the radios within a network in a way that allows the network to learn in a distributed holistic manner. The behavior-based approach to developing multi-agent systems from the field of robotics provides the framework for developing these learning networks. In this approach several behaviors are used to address the multiple objectives of a cognitive radio society and then combined to achieve emergent properties and behaviors. This work presents a prototype cognitive radio society. This society is implemented, using low complexity hardware, and evaluated. The work does not focus on the development of optimized techniques, but rather the complementary design of techniques and agents to create dynamic, decentralized self-organizing networks
Ph. D.
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Li, Shuang S. M. Massachusetts Institute of Technology. "Machine social intelligence in Virtualhome." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129367.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 49-53).
This thesis introduces a Watch-And-Help (WAH) challenge and a multi-agent environment for testing social intelligence in multiple agents. In the challenge, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the same task (social perception), and ii) coordinate with the human-like agent to solve the task in an unseen environment as fast as possible (human-AI collaboration). For this challenge, we build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines. Experimental results demonstrate that in order to achieve success in the challenge, an AI agent has to accurately understand and predict the human-like agent's behaviors, and adapt its collaborative plan accordingly in novel environments.
by Shuang Li.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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DeBusk, Kendra Portia Adrienne Howard. "Emotional intelligence, personality, social networks, and social perception." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3452.

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Emotional Intelligence (EI) is a relatively new concept in the field of psychology, introduced by Salovey and Mayer in 1990. Research on EI has found associations among EI and social network size, health and well-being, and job performance.(Austin, Saklofske, & Egan, 2003; Brackett, Mayer, & Warner, 2003; Petrides & Furnham, 2003; Saklofske, Austin, & Minski, 2001). Two different types of EI, trait EI and ability EI, have been identified in the literature. Trait EI was identified by Petrides and Furnham, and is a non-cognitive ability which allows an individual to regulate his/her mood, recognize and make the most of emotions, and utilize social skills, and is measured by self report. Ability EI is the ability of an individual to understand, generate, and manage emotions. Ability EI is measured using a performance measure which assesses the capacity of an individual to perceive emotions in him/herself, others, and the environment. Emotional intelligence has been linked to aspects of well-being, such as social network quality. In order to examine how EI related to social networks, both trait and ability EI were measured along with the Big Five factors of personality and social network quality and size. A study of 268 participants investigated the relationships amongst trait EI, personality, and social network quality and size. The Big Five factors of personality were all significantly positively correlated with EI (p< .01), and were also significantly correlated with social network quality and size. EI was significantly related to social network quality and size. When controlling for personality, EI was no longer significantly correlated with any of the social network quality or size variables. A subset of participants (n=78) completed an ability measure of EI, the Mayer Salovey Caruso Emotional Intelligence Test (MSCEIT). There were no significant correlations between MSCEIT scores and any of the other variables. A follow-up study was then carried out looking at the relationship of the original study variables with ability EI (MSCEIT), life stress, measured using the Uplifts and Hassles scale, and depression, measured using the Beck Depression Inventory (BDI), in the original participants. The results from this study indicated that emotional stability was significantly correlated with the Uplifts portion of the life stress scale, but not with Hassles. Conscientiousness was significantly negatively correlated with both the Hassle subscale of life stress and the BDI score. Emotional stability was also significantly negatively correlated with the BDI score. The total ability EI score measured by the MSCEIT did not show significant relationships with any other variables. Given that EI has been linked to social network quality and size, and one of the facets of EI is the capacity of an individual to recognize emotions in others, it would seem that individuals who are high in EI should have larger and better quality social networks as they are theoretically able to recognize and appropriately respond to the emotions of others. In order to test this, a social perception inspection time task was carried out in which participants were required to identify if a face was happy, sad, or angry. The faces used were both Caucasian and Far-East Asian, the hypothesis being that a person high in EI would recognize the facially expressed emotions regardless of whether the face shown was of their own race or not. Results from this study indicated that EI was not related to correctly identifying facial expressions. The results of these studies are discussed along with suggestions for future research in this area.
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Kleiman-Weiner, Max. "Computational foundations of human social intelligence." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120621.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Brain and Cognitive Sciences, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 199-211).
This thesis develops formal computational cognitive models of the social intelligence underlying human cooperation and morality. Human social intelligence is uniquely powerful. We collaborate with others to accomplish together what none of us could do on our own; we share the benefits of collaboration fairly and trust others to do the same. Even young children work and play collaboratively, guided by normative principles, and with a sophistication unparalleled in other animal species. Here, I seek to understand these everyday feats of social intelligence in computational terms. What are the cognitive representations and processes that underlie these abilities and what are their origins? How can we apply these cognitive principles to build machines that have the capacity to understand, learn from, and cooperate with people? The overarching formal framework of this thesis is the integration of individually rational, hierarchical Bayesian models of learning, together with socially rational multi-agent and game-theoretic models of cooperation. I use this framework to probe cognitive questions across three time-scales: evolutionary, developmental, and in the moment. First, I investigate the evolutionary origins of the cognitive structures that enable cooperation and support social learning. I then describe how these structures are used to learn social and moral knowledge rapidly during development, leading to the accumulation of knowledge over generations. Finally I show how this knowledge is used and generalized in the moment, across an infinitude of possible situations. This framework is applied to a variety of cognitively challenging social inferences: determining the intentions of others, distinguishing who is friend or foe, and inferring the reputation of others all from just a single observation of behavior. It also answers how these inferences enable fair and reciprocal cooperation, the computation of moral permissibility, and moral learning. This framework predicts and explains human judgment and behavior measured in large-scale multi-person experiments. Together, these results shine light on how the scale and scope of human social behavior is ultimately grounded in the sophistication of our social intelligence.
by Max Kleiman-Weiner.
Ph. D.
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Von, Bayern Auguste Marie Philippa. "Cognitive foundations of jeckdaw social intelligence." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612468.

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Whitby, Blay. "The social implications of artificial intelligence." Thesis, Middlesex University, 2003. http://eprints.mdx.ac.uk/7984/.

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For 18 years. I have been publishing books and papers on the subject of the social implications of Artificial Intelligence (AI). This is an area which is has been, and remains, in need of more academic attention of a serious nature than it currently receives. It will be useful to attempt a working definition of the field of AI at this stage. There is a considerable amount of disagreement as to what does and does not constitute AI and this often has important consequences for discussions of the social implications of the field. In brief, I define AI as the study of intelligent behaviour (in humans, animals, and machines) and the attempt to find ways in which such behaviour could be engineered in any type of artefact. My position on the definition of AI as a field of activity is set out in full in various places in the works submitted for this application. Most important are Chapter 2 of Whitby, 1988b, Chapter 3 of Whitby, 1996, and Whitby, 2000. This definition is distinctive (though not unique). For the purposes of discussion of social implications, its most distinctive feature is that it does not require the imitation or replication of human intellectual attributes. Because, under this definition, AI is not limited to the study of and attempt to build human-like intelligence the discussion of its social implications is rendered much broader. Also, because AÏ encompasses the attempt to engineer intelligent behaviour in any type of artefact, discussion of its social implications will need to consider the way in which AI technology, methods, and attitudes can permeate other different areas. This will include a wide range of technologies which include an AI element and a wide range of disciplines which are influenced by AI ideas. Thus the social implications of AI are turned into an immensely important field of study, since AI technology will steadily continue to permeate other technologies and thereby society as a whole. Many of the social implications of this technological process are nonobvious and surprising. If we are to make sensible, timely, and practical policy decisions and legislation then it is important to be as clear as possible about likely technological developments and their social implications. We may initially attempt to characterise various approaches by other authorities on the social implications of AI. Thèse range from the wildly spéculative such as Warwick (1988) and Moravec (1988) to the mainly technical, for example Michie (1986). At the wildly spéculative end of this continuum represented by Professor Warwick there are scare stories involving robots taking over the earth. (See for example Warwick, 1998 pp. 21-38) At the other end of the continuum, there are writers who often see AI as entirely positive, or as having no social implications at all. Most authorities will, or at least should, occupy a position somewhere between these extremes. However, in giving serious académie considération to this area, one needs to respond to this entire range of approaches. That is to say that one must (as a minimum) both be conversant with probable technical developments and also carefully and critically respond to speculations about the nature of future society. In my research I have consistently attempted to do just this. This is obviously a cross-disciplinary exercise and the differing methodologies of different disciplines présent further problems in determining the best (or an approximation to the best) approach. For a number of reasons, which will be fully explored in this statement, my approach has concentrated (mainly, though not exclusively) on the attempt to provide guidance to those actually concerned with the technical and scientific development of AI. The published books and papers submitted as part of this application span a period of 16 years. Thèse works form a cohérent body of research around the area of the social implications of AI. This body both develops the theme of the need for professionalism in AI and answers the criticisms of other writers in the area. They involve a full response to other writers in this area, over the entire continuum described above. This is a large, coherent, important, and generally well-regarded body of work which is in every relevant sense equivalent to that required for a PhD. by research.
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Chamberlain, Jon. "Harnessing collective intelligence on social networks." Thesis, University of Essex, 2015. http://repository.essex.ac.uk/15693/.

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Crowdsourcing is an approach to replace the work traditionally done by a single person with the collective action of a group of people via the Internet. It has established itself in the mainstream of research methodology in recent years using a variety of approaches to engage humans in solving problems that computers, as yet, cannot solve. Several common approaches to crowdsourcing have been successful, including peer production (in which the participants are inherently interested in contributing), microworking (in which participants are paid small amounts of money per task) and games or gamification (in which the participants are entertained as they complete the tasks). An alternative approach to crowdsourcing using social networks is proposed here. Social networks offer access to large user communities through integrated software applications and, as they mature, are utilised in different ways, with decentralised and unevenly-distributed organisation of content. This research investigates whether collective intelligence systems are facilitated better on social networks and how the contributed human effort can be optimised. These questions are investigated using two case studies of problem solving: anaphoric coreference in text documents and classifying images in the marine biology domain. Social networks themselves can be considered inherent, self-organised problem solving systems, an approach defined here as ‘groupsourcing’, sharing common features with other crowdsourcing approaches; however, the benefits are tempered with the many challenges this approach presents. In comparison to other methods of crowdsourcing, harnessing collective intelligence on social networks offers a high-accuracy, data-driven and low-cost approach.
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Books on the topic "Social intelligence"

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Goleman, Daniel. Social Intelligence. New York: Random House Publishing Group, 2006.

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Miorandi, Daniele, Vincenzo Maltese, Michael Rovatsos, Anton Nijholt, and James Stewart, eds. Social Collective Intelligence. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08681-1.

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Zhuge, Hai. Cyber-Physical-Social Intelligence. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-7311-4.

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Cantor, Nancy. Personality and social intelligence. Englewood Cliffs, N.J: Prentice-Hall, 1987.

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1948-, Kihlstrom John F., ed. Personality and social intelligence. Englewood Cliffs, N.J: Prentice-Hall, 1987.

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Sternberg, Robert J., and Aleksandra Kostić, eds. Social Intelligence and Nonverbal Communication. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34964-6.

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Banati, Hema, Siddhartha Bhattacharyya, Ashish Mani, and Mario Köppen, eds. Hybrid Intelligence for Social Networks. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65139-2.

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Liang, Xun. Social Computing with Artificial Intelligence. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7760-4.

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Shukla, Rajesh Kumar, Jitendra Agrawal, Sanjeev Sharma, Narendra S. Chaudhari, and K. K. Shukla, eds. Social Networking and Computational Intelligence. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2071-6.

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Blaise, Cronin, ed. Information, development and social intelligence. London: Taylor Graham, 1996.

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Book chapters on the topic "Social intelligence"

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Holloway, Rachel, and Patrick Morse. "Social Intelligence." In Encyclopedia of Personality and Individual Differences, 5073–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1837.

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Levesque, Roger J. R. "Social Intelligence." In Encyclopedia of Adolescence, 2793–94. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_409.

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Belton, Daniel A., Ashley M. Ebbert, and Frank J. Infurna. "Social Intelligence." In Global Encyclopedia of Public Administration, Public Policy, and Governance, 5687–91. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-20928-9_2393.

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Belton, Daniel A., Ashley M. Ebbert, and Frank J. Infurna. "Social Intelligence." In Global Encyclopedia of Public Administration, Public Policy, and Governance, 1–5. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31816-5_2393-1.

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Holloway, Rachel, and Patrick Morse. "Social Intelligence." In Encyclopedia of Personality and Individual Differences, 1–3. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-28099-8_1837-1.

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Richardson, Ken. "Social Intelligence." In The Evolution of Intelligent Systems, 160–73. London: Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230299245_9.

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Sadiku, Matthew N. O., and Sarhan M. Musa. "Social Intelligence." In A Primer on Multiple Intelligences, 43–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77584-1_4.

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Levesque, Roger J. R. "Social Intelligence." In Encyclopedia of Adolescence, 3687–88. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-33228-4_409.

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Belton, Daniel A., Ashley M. Ebbert, and Frank J. Infurna. "Social Intelligence." In Global Encyclopedia of Public Administration, Public Policy, and Governance, 12105–9. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-66252-3_2393.

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Williams, Mary-Anne. "Robot Social Intelligence." In Social Robotics, 45–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34103-8_5.

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Conference papers on the topic "Social intelligence"

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GENG, JINGZHUO. "LEGAL ANALYSIS OF COPYRIGHT OF ARTIFICIAL INTELLIGENCE PRODUCTS IN THE ERA OF WEAK ARTIFICIAL INTELLIGENCE." In 2023 9TH INTERNATIONAL SYMPOSIUM ON SOCIAL SCIENCE. Destech Publications, Inc., 2023. http://dx.doi.org/10.12783/dtssehs/isss2023/36090.

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At present, although artificial intelligence technology is still in the stage of weak artificial intelligence, it depends on the development of digital computing, which breaks the monopoly position of human beings in the field of literary creation, and then the legal subject and behavior of copyright have intelligent tendency, which puts forward new problems in judicial practice. Through the analysis of the nature of AI-generated works and the distinction between different categories, this paper makes a concrete analysis of the copyright of AI-generated works, and then draws the conclusion that the copyright of AI-generated works belongs to people. By using the criterion of "contact + substantial similarity", it is concluded that the self-editing articles produced by artificial intelligence based on deep learning do not belong to infringement. The expression and utilization of manuscript washing in artificial intelligence products constitutes infringement and does not belong to fair use. Whether the use of the original material by the ideological use of manuscript washing constitutes infringement requires a case judgment.
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Jung, Merel M. "Towards Social Touch Intelligence." In ICMI '14: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2663204.2666281.

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Honour, AnaLisa, Santosh Balajee Banisetty, and David Feil-Seifer. "Perceived Social Intelligence as Evaluation of Socially Navigation." In HRI '21: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3434074.3447226.

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Holeyannavar, Karthik, Arunkumar C. Giriyapur, and Rakesh P. Tapaskar. "Artificial Intelligence Based Intelligent Social Humanoid Robot – Ajit 2.0." In 2019 International Conference on Intelligent Computing and Control Systems (ICCS). IEEE, 2019. http://dx.doi.org/10.1109/iccs45141.2019.9065883.

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Nishida, Toyoaki. "Social Intelligence Design for Cultivating Shared Situated Intelligence." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.170.

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Dey, Lipika, Tirthanker Dasgupta, and Priyanka Sinha. "Social Sensing and Enterprise Intelligence." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3192323.

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Correia, Filipa, Francisco S. Melo, and Ana Paiva. "Group Intelligence on Social Robots." In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 2019. http://dx.doi.org/10.1109/hri.2019.8673089.

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Tuliakova, K. R., and M. A. Goriacheva. "Social intelligence and modern society." In INFORMATION TECHNOLOGIES AND MANAGEMENT IN HIGHER EDUCATION AND SCIENCES. PART 3. Baltija Publishing, 2022. http://dx.doi.org/10.30525/978-9934-26-277-7-231.

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Stone, Matthew. "Language, embodiment and social intelligence." In the Fifth International Natural Language Generation Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1708322.1708325.

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"Social Sciences and Intelligence Management." In 2023 IEEE 3rd International Conference on Social Sciences and Intelligence Management (SSIM). IEEE, 2023. http://dx.doi.org/10.1109/ssim59263.2023.10468816.

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Reports on the topic "Social intelligence"

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Bradshaw, Gary, and J. M. Giesen. Dynamic Measures of Spatial Ability, Executive Function, and Social Intelligence. Fort Belvoir, VA: Defense Technical Information Center, March 2003. http://dx.doi.org/10.21236/ada414704.

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Taafe-McMenamy, Damian R. Overcoming Intermediary Bias Through the Use of Social Media Intelligence. Fort Belvoir, VA: Defense Technical Information Center, April 2015. http://dx.doi.org/10.21236/ad1001887.

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Young, Andrew Young, and Stefaan G. Verhulst Verhulst. The Potential of Social Media Intelligence to Improve People’s Lives: Social Media Data for Good. GovLab, September 2017. http://dx.doi.org/10.15868/socialsector.40384.

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Perdigão, Rui A. P. Earth System Dynamic Intelligence - ESDI. Meteoceanics, April 2021. http://dx.doi.org/10.46337/esdi.210414.

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Earth System Dynamic Intelligence (ESDI) entails developing and making innovative use of emerging concepts and pathways in mathematical geophysics, Earth System Dynamics, and information technologies to sense, monitor, harness, analyze, model and fundamentally unveil dynamic understanding across the natural, social and technical geosciences, including the associated manifold multiscale multidomain processes, interactions and complexity, along with the associated predictability and uncertainty dynamics. The ESDI Flagship initiative ignites the development, discussion and cross-fertilization of novel theoretical insights, methodological developments and geophysical applications across interdisciplinary mathematical, geophysical and information technological approaches towards a cross-cutting, mathematically sound, physically consistent, socially conscious and operationally effective Earth System Dynamic Intelligence. Going beyond the well established stochastic-dynamic, information-theoretic, artificial intelligence, mechanistic and hybrid techniques, ESDI paves the way to exploratory and disruptive developments along emerging information physical intelligence pathways, and bridges fundamental and operational complex problem solving across frontier natural, social and technical geosciences. Overall, the ESDI Flagship breeds a nascent field and community where methodological ingenuity and natural process understanding come together to shed light onto fundamental theoretical aspects to build innovative methodologies, products and services to tackle real-world challenges facing our planet.
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Frink, Dwight D., and Gerald R. Ferris. Personal and Structural Influences on Performance in Dynamic Environments: An Investigation of Social Skill/Intelligence and Social Contingencies. Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada416400.

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Tumley, William H. The Impact of Social Intelligence and Impression Management on Perceived Leadership Potential and Group Cohesiveness. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada397672.

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Shaneyfelt, Wendy, John T. Feddema, and Conrad D. James. A Surety Engineering Framework and Process to Address Ethical Legal and Social Issues for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), August 2016. http://dx.doi.org/10.2172/1561812.

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Gentelet, Karine, Lily-Cannelle Mathieu, and Alexandra Bahary-Dionne. Digital technology and artificial intelligence as a tool for social justice: Initiatives by and for marginalized groups and communities. Observatoire international sur les impacts sociétaux de l'IA et du numérique, April 2024. http://dx.doi.org/10.61737/nnhl5600.

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This exploratory research looks at initiatives using artificial intelligence (AI) or digital deployed locally in different parts of the world by groups, communities, or civil society organizations, to achieve their social justice goals.
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Glasby, Jon, Ian Litchfield, Sarah Parkinson, Lucy Hocking, Denise Tanner, Bridget Roe, and Jennifer Bousfield. New and emerging technology for adult social care - the example of home sensors with artificial intelligence (AI) technology. NIHR, October 2022. http://dx.doi.org/10.3310/hsdr-tr-134314.

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Yatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, February 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.

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The article investigates functional techniques of extralinguistic expression in multimedia texts; the effectiveness of figurative expressions as a reaction to modern events in Ukraine and their influence on the formation of public opinion is shown. Publications of journalists, broadcasts of media resonators, experts, public figures, politicians, readers are analyzed. The language of the media plays a key role in shaping the worldview of the young political elite in the first place. The essence of each statement is a focused thought that reacts to events in the world or in one’s own country. The most popular platform for mass information and social interaction is, first of all, network journalism, which is characterized by mobility and unlimited time and space. Authors have complete freedom to express their views in direct language, including their own word formation. Phonetic, lexical, phraseological and stylistic means of speech create expression of the text. A figurative word, a good aphorism or proverb, a paraphrased expression, etc. enhance the effectiveness of a multimedia text. This is especially important for headlines that simultaneously inform and influence the views of millions of readers. Given the wide range of issues raised by the Internet as a medium, research in this area is interdisciplinary. The science of information, combining language and social communication, is at the forefront of global interactions. The Internet is an effective source of knowledge and a forum for free thought. Nonlinear texts (hypertexts) – «branching texts or texts that perform actions on request», multimedia texts change the principles of information collection, storage and dissemination, involving billions of readers in the discussion of global issues. Mastering the word is not an easy task if the author of the publication is not well-read, is not deep in the topic, does not know the psychology of the audience for which he writes. Therefore, the study of media broadcasting is an important component of the professional training of future journalists. The functions of the language of the media require the authors to make the right statements and convincing arguments in the text. Journalism education is not only knowledge of imperative and dispositive norms, but also apodictic ones. In practice, this means that there are rules in media creativity that are based on logical necessity. Apodicticity is the first sign of impressive language on the platform of print or electronic media. Social expression is a combination of creative abilities and linguistic competencies that a journalist realizes in his activity. Creative self-expression is realized in a set of many important factors in the media: the choice of topic, convincing arguments, logical presentation of ideas and deep philological education. Linguistic art, in contrast to painting, music, sculpture, accumulates all visual, auditory, tactile and empathic sensations in a universal sign – the word. The choice of the word for the reproduction of sensory and semantic meanings, its competent use in the appropriate context distinguishes the journalist-intellectual from other participants in forums, round tables, analytical or entertainment programs. Expressive speech in the media is a product of the intellect (ability to think) of all those who write on socio-political or economic topics. In the same plane with him – intelligence (awareness, prudence), the first sign of which (according to Ivan Ogienko) is a good knowledge of the language. Intellectual language is an important means of organizing a journalistic text. It, on the one hand, logically conveys the author’s thoughts, and on the other – encourages the reader to reflect and comprehend what is read. The richness of language is accumulated through continuous self-education and interesting communication. Studies of social expression as an important factor influencing the formation of public consciousness should open up new facets of rational and emotional media broadcasting; to trace physical and psychological reactions to communicative mimicry in the media. Speech mimicry as one of the methods of disguise is increasingly becoming a dangerous factor in manipulating the media. Mimicry is an unprincipled adaptation to the surrounding social conditions; one of the most famous examples of an animal characterized by mimicry (change of protective color and shape) is a chameleon. In a figurative sense, chameleons are called adaptive journalists. Observations show that mimicry in politics is to some extent a kind of game that, like every game, is always conditional and artificial.
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