GROWTH OF COLLECTIVE INTELLIGENCE BY LINKING KNOWLEDGE WORKERS THROUGH SOCIAL MEDIA

Collective intelligence can be defined, very broadly, as groups of individuals that do things collectively, and that seem to be intelligent. Collective intelligence has existed for ages. Families, tribes, companies, countries, etc., are all groups of individuals doing things collectively, and that seem to be intelligent. However, over the past two decades, the rise of the Internet has given upturn to new types of collective intelligence. Companies can take advantage from the so-called Web-enabled collective intelligence. Web-enabled collective intelligence is based on linking knowledge workers through social media. That means that companies can hire geographically dispersed knowledge workers and create so-called virtual teams of these knowledge workers (members of the virtual teams are connected only via the Internet and do not meet face to face). By providing an online social network, the companies can achieve significant growth of collective intelligence. But to create and use an online social network within a company in a really efficient way, the managers need to have a deep understanding of how such a system works. Thus the purpose of this paper is to share the knowledge about effective use of social networks in companies. The main objectives of this paper are as follows: to introduce some good practices of the use of social media in companies, to analyze these practices and to generalize recommendations for a successful introduction and use of social media to increase collective intelligence of a company

Model for Human, Artificial & Collective Consciousness (Part I)

Borrowing the functional modeling approach common in systems and software engineering, an implementable model of the functions of human consciousness proposed to have the capacity for general problem solving ability transferable to any domain, or true self-aware intelligence, is presented. Being a functional model that is independent of implementation, this model is proposed to also be applicable to artificial consciousness, and to platforms that organize individuals into what is defined here as a first order collective consciousness, or at higher orders into what is defined here as Nth order collective consciousness. Part I of this two-part article includes: Summary; Introduction; Set of Postulates One; Set of Postulates Two; Overview of the Model; Model of Homeostasis; Model of the Functional Units; Model of the Body System; Model of the Other Basic Life Processes; Model of the Other Functional Systems; Model of Perceptions in the Perceptual Fields; Model of Body Processes as Paths in the Perceptual Field; & Model of Conscious Awarenes

Model for Human, Artificial & Collective Consciousness (Part I)

Borrowing the functional modeling approach common in systems and software engineering, an implementable model of the functions of human consciousness proposed to have the capacity for general problem solving ability transferable to any domain, or true self-aware intelligence, is presented. Being a functional model that is independent of implementation, this model is proposed to also be applicable to artificial consciousness, and to platforms that organize individuals into what is defined here as a first order collective consciousness, or at higher orders into what is defined here as Nth order collective consciousness. Part I of this two-part article includes: Summary; Introduction; Set of Postulates One; Set of Postulates Two; Overview of the Model; Model of Homeostasis; Model of the Functional Units; Model of the Body System; Model of the Other Basic Life Processes; Model of the Other Functional Systems; Model of Perceptions in the Perceptual Fields; Model of Body Processes as Paths in the Perceptual Field; & Model of Conscious Awarenes

Introduction:AI, inclusion, and ‘everyone learning everything’

This chapter provides an introduction to the book—Artificial Intelligence and Inclusive Education: speculative futures and emerging practices. It examines the potential intersections, correspondences, divergences, and contestations between the discourses that typically accompany, on the one hand, calls for artificial intelligence technology to disrupt and enhance educational practice and, on the other, appeals for greater inclusion in teaching and learning. Both these areas of discourse are shown to envision a future of ‘education for all’: artificial intelligence in education (AIEd) tends to promote the idea of an automated, and personalised, one-to-one tutor for every learner, while inclusive education often appears concerned with methods of involving marginalised and excluded individuals and organising the communal dimensions of education. However, these approaches are also shown to imply important distinctions: between the attempts at collective educational work through inclusive pedagogies and the drive for personalised learning through AIEd. This chapter presents a critical view of the quest for personalisation found in AIEd, suggesting a problematic grounding in the myth of the one-to-one tutor and questionable associations with simplistic views of ‘learner-centred’ education. In contrast, inclusive pedagogy is suggested to be more concerned with developing a ‘common ground’ for educational activity, rather than developing a one-on-one relationship between the teacher and the student. Inclusive education is therefore portrayed as political, involving the promotion of active, collective, and democratic forms of citizen participation. The chapter concludes with an outline of the subsequent contributions to the book

Time, terror and the technological imagination : Frankenstein's fictional legacy in the scientific age : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in English at Massey University, Palmerston North, New Zealand

There is a long-standing belief that there is an opposing discourse between science and the humanities in relation to the future of humankind. Attitudes towards the environment have changed radically in the last 200 years from a natural view to one where we dominate and re-order our environment to suit ourselves and to further the material self-interests of human beings, regardless of cultural and ecological consequences. In order for human beings to properly understand what is happening and why, we must begin to restore the balance between our relationship with Nature and our new technological worldview. The Introduction firstly addresses issues relating to the changing relationship between human beings and their environment over the last two centuries, and how literature and film have accurately predicted our collective future. It is my objective to illustrate how Mary Shelley's Frankenstein has remained one of the most potent pieces of literature foreshadowing the future of humankind, and the timeless quality of the theme of the controller out of control. The main text focuses on Mary Shelley's Frankenstein, and how the novel embodied humankind's growing anxieties and fears about our technological ambivalence, and I give an overview of how Frankenstein has paved the way for further literary and cinematic predictions of our future in artificial and synthesised environments dominated by the new frontier of genetic engineering, artificial intelligence, virtual reality and beyond, and how these technologies will impact on our cultural worldview and the future evolution of humankind

focusing on groupthink and collective intelligence aspect

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 황준석.Knowledge is one of the important sources for the progress of mankind. The importance of knowledge has long been emphasized in various fields, and over time independent experts, systems, and studies dealing only with knowledge have emerged. The recent rapid development of technology required more quantity and quality knowledge in our society, and the knowledge became a competitive itself. The old knowledge creation process had highlighted a person's role. In particular, the creation of knowledge by a small group of experts, by excellent individuals, has contributed the most to the production of knowledge. However, the emergence of online spaces due to information and communication technologies and the use of big data have begun to change the human knowledge creation process unprecedentedly. The production of knowledge based on individual capability gradually began to be replaced by new technologies and crowds. The combination of new technology proposed a new intellectual system called collective intelligence, which was utilized as the main drivers of decision making and knowledge generation in modern social organizations. However, collective intelligence had some limitations. First, the integration of individual knowledge is difficult because collective intelligence generally represents a high level of decentralization and horizontal hierarchy. A new method of knowledge integration for collective intelligence was required because a simple method of opinion integration, such as the majority rule, could hinder synergetic effects of collective intelligence and could rather result in defective knowledge by groupthink. Another problem is the evaluation of knowledge. The evaluation of knowledge becomes more important when the problem has no single optimal solution. Since an organization without an appropriate level of criticism and evaluation is difficult to produce quality knowledge. Thats why different methods are required to evaluate individual and organizational knowledge. In addition, in order to produce knowledge successfully, various conditions must be satisfied. For that reason, most of the prior studies on collective intelligence have focused on the conditions of successful collective intelligence. What if the conditions of collective intelligence are not satisfied? The answer to this was in the concept of groupthink introduced before the concept of collective intelligence. Groupthink is defined as a group tendency overlooking criticism, evaluation and consideration of alternatives in order to achieve organizational consensus. Groupthink, contrary to collective intelligence, has been pointed out as a source for the failure of organizational decision-making. So, the relevant studies have focused on finding solutions to identify and solve the causes of groupthink in order to prevent organizational fiasco. The goal of this dissertation is to understand the way for organizational knowledge creation based on two concepts: groupthink and collective intelligence. In order to complete my research goal, three small topics were raised. First, we have to account for groupthink phenomenon which has been the most pervasively used as one of the major sources of group failures. Second, the bridge between groupthink and collective intelligence should be built for finding out the factors enhancing organizational knowledge creation. Third, some strategical aspects are needed. From the self-organization and socio-technological perspective, this dissertation proposes an effective strategy for organizational knowledge creation. The first study in chapter 3 tried to give an answer to the first topic, Can we eliminate groupthink from the organization?. Based on the different perspectives of groupthink proposed in chapter 3, switching factors that transform groupthink into collective intelligence are derived. In chapter 4, we discuss the effect of switching factors and efficient strategies using them. Findings in chapter 4 can give an answer to the question Is there any link between groupthink and collective intelligence?. Chapter 5, the last study of this dissertation, aims to propose effective strategies for the use of technologies such as big data analytics and online platform. More details of each study are shown below. The first study, "Is groupthink really inevitable?": focusing on the self-organization mechanism, is about the emergent mechanism of groupthink. The study covers two topics in detail. The first is to verify Janis' groupthink model the most well-known. This presented the limitations of Janis' linear model of groupthink and suggested the need for different perspectives. The second was to simulation of groupthink phenomenon occurrence from a self-organization perspective. The results of the simulation experiments showed that groupthink is a phenomenon that can occur naturally in cooperative situations. The findings of this study show that it is more important to make the collective thinking phenomenon productive through appropriate measures than to completely eliminate it from the organization. The goal of the second study, that is titled "The Optimal Strategy of Organizational Knowledge Creation in Groupthink Situation", is twofold. First, identifying the switching factors for the organization in groupthink to transform into collective intelligence, and secondly, investigating the optimal strategy utilizing the switching factors. In this study, three factors were derived from the previous literature: knowledge conflict, reconsideration of alternatives, and organizational memory. To verify the effects of the three switching factors, an agent-based model simulation was conducted, and the results showed that all switching factors were effective in improving the quality of organizational knowledge, but not in the diversity. In order to derive the optimal strategy based on switching factors, the meta-data of the simulation was used to perform the meta-frontier analysis. The results show that the combination of knowledge conflict and reconsideration has the highest efficiency, whereas the combination of knowledge conflict and organizational knowledge has the lowest efficiency. The last study, "The effect of the use of emerging technologies on the organizational knowledge creation: focusing on the use of big data analysis and online platform," identified how the use of new technology affects the production of organizational knowledge. The study focused on the use of big data and the use of online platforms. Based on the survey data, the impacts of the use of each technology on the groupthink and collective intelligence were identified. Through the above studies, this paper put forward the method of improving the efficiency of the organizational knowledge creation process. Guidelines for establishing organizational strategies using switching factors can be suggested, and the level of use of big data and online platforms can be suggested to encourage collective intelligence.지식은 인류의 진보를 위한 중요한 원천 중 하나이다. 지식의 중요성 동안 다양한 분야에서, 시간이 지나면서 독립 전문가들, 시스템 및 연구 결과는 오로지 지식을 다루는 등장했다 강조되고 있다. 최근 기술의 급속한 발전은 우리 사회에 더 많은 양과 질 높은 지식을 필요로 했고, 그 지식은 경쟁 자체가 되었다. 초기의 지식 창출 과정은 개인 또는 소수의 전문가 집단의 역할을 강조했다. 특히 전문가들의 훌륭한 개인이 작은 규모에 의해 지식의 창출, 지식의 생산에 가장 기여한다고 여겨져 왔다. 그러나, 온라인상에서 공간 정보 통신 기술 출현 및 빅 데이터의 사용은 전례 없이 인간의 지식 생산 과정을 바꾸기 시작했다. 지식의 생산 개인 능력에 따라 점차 새로운 기술과 많은 사람들에 의해 대체되기 시작했다. 새로운 기술과 조직 협력의 조합은 조직적 의사 결정의 주요 동인으로 활용되는 새로운 지식 시스템인 집단 지성이라고 불리는 방안을 제안되기 시작했다. 이러한 방식은 현대 사회 조직들의 지식 창출의 중요한 축을 담당하고 있다. 위키피디아는 온라인 플랫폼 이 집단 지성을 이용하는 가장 성공적인 분야이다. 이 플랫폼은 무작위의 사람들이 참여하며, 단지 지식과 수정 저장될 수 있는 인터페이스를 준다. 세계적으로 가장 큰 지식 플랫폼인 위키피디아의 성공은 군중 속에서 지식 전문가 집단의 개입 없이 통합된 상호작용으로써 이지식 생태계의 높은 수준을 만든다는 것을 증명했으며, 또한 지식 창출의 주 동력이 재능 있는 개인들 에서 조직으로 옮겨 가고 있다는 걸 증명하였다. 하지만 집단 지성의 일부 한계 가지고 있었다. 첫째, 집단 지성은 일반적으로 높은 수준의 분권화와 수평 계층 구조를 갖기 때문에, 개별 지식의 통합 어렵다. 단순한 의견 통합 방식은 집단지성의 상승효과를 방해하고 집단사고로 인한 결함 있는 지식 생산을 야기할 수 있기 때문에, 집단지성을 위한 새로운 지식 통합 방식이 요구된다. 또 다른 문제는 지식의 평가에 있다. 특히 지식에 대한 평가는 문제가 하나의 해결책을 갖지 않을 때 더욱 중요해진다. 이것이 새로운 지식 평가 방식이 필요한 이유이다. 또한 지식 생산을 성공적으로 달성하기 위해서는 다양한 조건들이 충족되어야 한다. 그 때문에 집단 지능에 관한 선행연구의 대부분은 성공적인 집단 지능의 조건에 초점을 맞추고 있다. 만약 집단지성의 조건이 충족되지 않는다면? 이에 대한 해답은 집단지능 관점이 채택되기 전에 도입된 집단 사고의 개념에 있었다. 집단 사고는 조직의 합의를 이루기 위해 대안에 대한 비판, 평가 및 고려를 간과하는 집단적 경향으로 정의된다. 집단 사고는 집단지성과는 달리 조직적 의사결정 실패의 원인으로 지적되어 왔다. 그래서 관련 연구는 조직적인 실패를 막기 위해 집단 사고의 원인을 규명하고 해결할 해결책을 찾는 데 초점을 맞추고 있다. 그러나 집단지성과 집단적 사고는 모두 조직적 지식 창출이나 의사결정의 과정에서 자연스럽게 발생하는 현상이다. 하지만 집단사고의 원인을 찾는 것이 진정한 해결책이 될 수 있는지에 대해서는 의문이 존재한다. 집단 지성과 집단사고 현상은 조직의 지식창출 또는 의사결정 과정에서 발생한다. 그들의 결과물과 무관하게, 조직은 그들의 목표달성을 위하여 꾸준히 지식창출 행위를 수행해야 한다. 그러나 문제는 결과에 대한 평가가 이루어지기 이전에는 그들의 조직이 현재 집단사고와 집단지성 중 어떤 상황에 있는지를 알아내기가 어렵다는 점이다. 수 많은 연구들이 조직 지식 창출과 관리를 효과적으로 하기 위하여 이론과 가설들을 제시하여 왔다. 그러나 불행히도 집단사고와 집단지성의 전환의 관점에서 이루어진 연구는 거의 없었다. 이 논문의 목적은 집단 사고와 집단 지성이라는 두 가지 개념을 바탕으로 조직 지식 창출의 방법을 이해하는 것이다. 나의 연구목표를 완성하기 위해 세 가지 작은 주제가 제기되었다. 첫째, 우리는 집단 실패의 주요 원인 중 하나로 가장 널리 사용되어 온 집단 사고 현상을 고려해야 한다. 둘째, 집단 사고와 집단지성을 연결하는 다리는 조직 지식 창조를 강화하는 요인을 찾아내기 위해 세워져야 한다. 셋째, 몇 가지 전략적인 측면이 필요하다. 자기 조직화와 사회 기술적 관점에서 본 논문은 조직 지식 창출을 위한 효과적인 전략을 제안한다. 제3장의 첫 번째 연구는 '조직에서 집단 사고를 없앨 수 있을까?'라는 첫 번째 주제에 대한 답을 주려고 노력했다. 제3장에서 제안된 집단 사고의 다른 관점들에 근거하여 집단 사고의 집단지성으로 전환하는 요인을 도출한다. 제4장에서는 전환 요인의 효과와 이를 이용한 효율적인 전략에 대해 논한다. 제4장에서의 결과들은 '집단 사고와 집단지능 사이에 어떤 연관성이 있는가?'라는 질문에 대한 답을 줄 수 있다. 제5장 본 논문의 마지막 연구에서는 빅데이터 분석, 온라인 플랫폼 등의 기술 활용을 위한 효과적인 전략을 제안하는 것을 목표로 한다. 각 연구의 자세한 내용은 다음과 같다, 첫 번째 연구 "Is groupthink really inevitable?: based on self-organization aspect"는 집단 사고의 긴급한 메커니즘에 관한 것이다. 이 연구는 두 가지 주제를 상세히 다루고 있다. 첫번째는 Janis의 집단 사고 모델을 가장 잘 알려진 것으로 검증하는 것이다. 이것은 집단 사고에 대한 Janis의 선형 모델의 한계를 제시하고 다른 관점의 필요성을 제시했다. 두 번째는 자기 조직적 관점에서 집단 사고 현상이 발생하는 시뮬레이션이었다. 시뮬레이션 실험의 결과는 집단 사고가 협력적인 상황에서 자연스럽게 일어날 수 있는 현상이라는 것을 보여주었다. 이 연구의 결과는 집단적 사고 현상을 조직으로부터 완전히 제거하는 것보다 적절한 조치를 통해 생산적으로 만드는 것이 더 중요하다는 것을 보여준다. 두 번째 연구인 "The optimal knowledge creation strategy of organizations in groupthink situations"의 목표는 두 가지다. 첫째, 집단사고에서 조직의 전환 요인을 파악하여 집단지능으로 전환하고, 둘째, 전환 요인을 활용한 최적 전략을 조사한다. 본 연구에서는 지식 충돌, 대안의 재고, 조직 기억의 세 가지 요소가 선행 문헌들에서 도출되었다. 세 가지 전환 요인의 효과를 검증하기 위해 행위자 기반 모델 시뮬레이션을 실시하였고, 그 결과 모든 전환 요인이 조직 지식의 질을 향상시키는 데 효과적으로 나타났으나 다양성 증대에는 큰 효과가 없었다. 전환 요인에 기초한 최적의 전략을 도출하기 위해, 시뮬레이션의 메타 데이터를 활용하여 메타 프런티어 분석을 수행했다. 그 결과는 지식 충돌과 대안의 재고의 조합이 가장 효율성이 높은 반면 지식 충돌과 조직 기억의 조합은 효율성이 가장 낮다는 것을 보여준다. 마지막 연구인 "Effect of emerging technologies on the organizational knowledge creation: the use of big data analytics and online platforms"는 연구에서는 신기술의 활용이 조직 지식의 생산에 어떤 영향을 미치는지 파악했다. 이 연구는 빅데이터의 사용과 온라인 플랫폼 사용에 초점을 맞췄다. 조사 데이터를 바탕으로 각 기술이 집단 사고와 집단 지능에 미치는 영향을 파악하였다. 이 연구의 결과는 본 논문은 상기 연구를 통해 조직 지식창출 과정의 효율성을 높이고 조직 전략과 기술적 측면의 양질의 지식을 창출하는 방법을 제시했다. 전환 요인을 활용한 조직 전략 수립 가이드라인을 제시하고, 빅데이터 분석 기술의 활용과 온라인 플랫폼의 활용을 통해 사회기술적(socio-technology) 관점에서의 전략을 제시한다.Chapter 1. Introduction 1 1.1 Research background 1 1.2 Problem statement 3 1.3 Research objective 4 1.4 Research question 7 1.5 Research outline 9 Chapter 2. Literature review 12 2.1 Creation of organizational knowledge 12 2.2 Groupthink 15 2.2.1 Criticisms on empirical evidence 18 2.2.2 Criticisms on framework 19 2.3 Collective intelligence 22 2.4 Switching factors 27 2.4.1 Knowledge conflict 30 2.4.2 Reconsideration of alternatives 32 2.4.3 Organizational memory 33 2.5 Technology and organizational knowledge 35 2.5.1 Big data analytics 35 2.5.1 Online platforms 37 Chapter 3. Is groupthink really inevitable?: based on self-organization aspect 41 3.1 Introduction 41 3.2 Revisiting Janis groupthink model 47 3.2.1 Evidence of Janis groupthink model 47 3.2.2 Data 48 3.2.3 Measurement 52 3.2.4 Retesting Janis groupthink model 54 3.3 Groupthink simulation model 55 3.3.1 Overview 57 3.3.2 Design concept 72 3.3.3 Details 73 3.4 Simulation results 82 3.4.1 No interaction model 82 3.4.2 Interaction model (baseline model) 84 3.4.3 Groupthink models 87 3.5 Discussion 90 3.5.1 The effect of group cohesiveness 91 3.5.2 The effect of structural faults 93 3.5.3 Inevitability of groupthink 93 Chapter 4. Comparing the better knowledge creation strategy of organizations in groupthink situations 95 4.1 Introduction 95 4.2 Effect of switching factor 100 4.2.1 Overview 101 4.2.2 Details 116 4.3 Simulation result 120 4.3.1 Reference model 120 4.3.2 Knowledge optimization and knowledge bias 121 4.3.3 Quality of knowledge and average utility 125 4.4 Finding the optimal strategy 128 4.4.1 Meta-frontier analysis 128 4.4.2 Comparison of strategies using switching factors 132 4.5 Discussion 134 4.6 Conclusion and limitations 139 Chapter 5. Effect of emerging technologies on the organizational knowledge creation: the use of big data analytics and online platforms 140 5.1 Introduction 140 5.2 Technology and organizational knowledge creation 146 5.2.1 Organizational knowledge creation 147 5.2.2 Big data analytics 148 5.2.3 Online platform 150 5.2.4 Task complexity 154 5.3 The effect of technology usage 155 5.3.1 Data 155 5.3.2 Measurement 157 5.3.3 Regression model 163 5.3.4 Result: the effect of the use of technology 164 5.4 Discussion 171 Chapter 6. Conclusion and implications 175 6.1 Conclusions 175 6.1.1 Overall summary 175 6.1.2 Main findings 188 6.2 Implications 188 6.3 Utilization 193 6.3.1 Firm 193 6.3.2 Policy 195 References 196 Appendix 258 Abstract (Korean) 289Docto

Toward a collective intelligence recommender system for education

The development of Information and Communication Technology (ICT), have revolutionized the world and have moved us into the information age, however the access and handling of this large amount of information is causing valuable time losses. Teachers in Higher Education especially use the Internet as a tool to consult materials and content for the development of the subjects. The internet has very broad services, and sometimes it is difficult for users to find the contents in an easy and fast way. This problem is increasing at the time, causing that students spend a lot of time in search information rather than in synthesis, analysis and construction of new knowledge. In this context, several questions have emerged: Is it possible to design learning activities that allow us to value the information search and to encourage collective participation?. What are the conditions that an ICT tool that supports a process of information search has to have to optimize the student's time and learning? This article presents the use and application of a Recommender System (RS) designed on paradigms of Collective Intelligence (CI). The RS designed encourages the collective learning and the authentic participation of the students. The research combines the literature study with the analysis of the ICT tools that have emerged in the field of the CI and RS. Also, Design-Based Research (DBR) was used to compile and summarize collective intelligence approaches and filtering techniques reported in the literature in Higher Education as well as to incrementally improving the tool. Several are the benefits that have been evidenced as a result of the exploratory study carried out. Among them the following stand out: • It improves student motivation, as it helps you discover new content of interest in an easy way. • It saves time in the search and classification of teaching material of interest. • It fosters specialized reading, inspires competence as a means of learning. • It gives the teacher the ability to generate reports of trends and behaviors of their students, real-time assessment of the quality of learning material. The authors consider that the use of ICT tools that combine the paradigms of the CI and RS presented in this work, are a tool that improves the construction of student knowledge and motivates their collective development in cyberspace, in addition, the model of Filltering Contents used supports the design of models and strategies of collective intelligence in Higher Education.Postprint (author's final draft