Application Of Collective Intelligence Research Articles

Developing a Utopian Model of Human-Technology Interaction: Collective Intelligence Applications in Support of Future Well-Being

ABSTRACT Human-technology interactions are omnipresent in daily life, a reality that must be faced to enact positive change without uprooting the technological systems that have come to define us. The present study develops a collective intelligence model for human-technology interaction (HTI) design that aims to promote peace, prosperity, and happiness through design intentionality informed by utopian targets of radical improvement in society. Participants generated ideas, clarified and consolidated them, and then developed an interpretive structure model of the most important affordances identified during the idea generation phase. Coupled with a two-pass thematic analysis of the original idea set, what emerged is a model that is informed by stakeholder users of the technologies of tomorrow. Affordance themes centered on the importance of promoting world peace through technological means, identifying and rectifying material and financial inequalities, addressing negative well-being effects in current technologies, and increasing global collaboration and eventual expansion into the cosmos.

Solving pictorial jigsaw puzzles via Internet-based collective intelligence

Pictorial jigsaw (PJ) puzzles are a well-known leisure game for humans. Usually, a PJ puzzle game is played by one or several players face-to-face in physical space. In this paper, we focus on how to solve PJ puzzles in cyberspace by a group of physically-distributed players. We propose an approach to solve PJ puzzles using stigmergy-inspired Internet-based human collective intelligence. The core of the approach is a continuously-executing loop, namely, the EIFloop, which consists of three activities: exploration, integration, and feedback. During the explorationactivity, each player tries to solve the PJ puzzle alone, without direct interaction with the other players. At any time, the result of a players exploration represents a partial solution to the PJ puzzle and a set of rejected neighboring relationships between the puzzle pieces. The results of all players explorations are integrated in real time through the integrationactivity, forming a continuously-updated collective opinion graphCOG). Through the feedbackactivity, each player is provided with personalized feedback information based on the current COG and the players current exploration results, in order to accelerate his/her puzzle-solving process. Exploratory experiments show that:(1) Supported by this approach, the time to solve PJ puzzles is nearly linear with respect to the reciprocal of the number of players. Furthermore, compared with the best single players in the experiments, the puzzle-solving time decreases by 31.36%–64.57% on average for groups composed of 2 to 10 players.linebreak(2) Supported by this approach, the feedback information received by the best player in a group has an average precision of 86.34%, and, as the group size increases, the feedback information in the puzzle-solving result of the best player increases gradually from 20% to 45%.(3) This approach exhibits a better scalability with puzzle size than that of a face-to-face collaboration among ten players. Additionally, it always leads to 100%-accurate puzzle-solving results, whereas the results of the automated PJ-puzzle solver have an average accuracy of only 52%.It is envisaged that these results will provide useful information or opinions to facilitate a broader application of human collective intelligence in the Internet environment.

A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers

Peer-to-peer ridesharing enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic). Despite these very promising potential advantages, the percentage of users who currently adopt ridesharing solutions is very low, well below the adoption rate required to achieve said benefits. One of the reasons of this insufficient engagement by the public is the lack of effective incentive policies by regulatory authorities, who are not able to estimate the costs and the benefits of a given ridesharing adoption policy. Here we address these issues by (i) developing a novel algorithm that makes large-scale, real-time peer-to-peer ridesharing technologically feasible; and (ii) exhaustively quantifying the impact of different ridesharing scenarios in terms of environmental benefits (i.e., reduction of CO2 emissions, noise pollution, and traffic congestion) and quality of service for the users. Our analysis on a real-world dataset shows that major societal benefits are expected from deploying peer-to-peer ridesharing depending on the trade-off between environmental benefits and quality of service. Results on a real-world dataset show that our approach can produce reductions up to a 70.78% in CO2 emissions and up to 80.08% in traffic congestion.

Collaborative positive psychology: solidarity, meaning, resilience, wellbeing, and virtue in a time of crisis

This paper introduces readers to collaborative positive psychology, a natural complement to transdisciplinary systems science. Consistent with second wave positive psychology models (PP2.0), collaborative positive psychology highlights how negative emotions (e.g. sadness, guilt, shame, anger, anxiety) can act as key drivers of positive transformation in collaborative groups, providing a catalyst for critical systems thinking and collective responses to shared problems. Collaborative positive psychology emphasises pathways from the recognition of collective problems to problem-focused and method-driven responding supporting sustainable wellbeing. Key principles of collaborative PP include solidarity, empowerment, and teamwork, with a specific focus on the application of collective intelligence (CI) methods to address shared complex problems. An overview of the work of the Collective Intelligence Network Support Unit (CINSU) is used to highlight the power and potential of collaborative groups. Collaborative positive psychology highlights the need for the development of collaborative infrastructures that reinforce solidarity, collective empowerment, and collective intelligence.

Literature review on collective intelligence: a crowd science perspective

PurposeCollective intelligence has drawn many scientists’ attention in many centuries. This paper shows the collective intelligence study process in a perspective of crowd science.Design/methodology/approachAfter summarizing the time-order process of related researches, different points of views on collective intelligence’s measurement and their modeling methods were outlined.FindingsThe authors show the recent research focusing on collective intelligence optimization. The studies on application of collective intelligence and its future potential are also discussed.Originality/valueThis paper will help researchers in crowd science have a better picture of this highly related frontier interdiscipline.

Using collective intelligence to identify barriers to teaching 12–19 year olds about the ocean in Europe

Since the degradation of the marine environment is strongly linked to human activities, having citizens who appreciate the ocean's influence on them and their influence on the ocean is important. Research has shown that citizens have a limited understanding of the ocean and it is this lack of ocean literacy that needs to change. This study maps the European landscape of barriers to teaching 12–19 year olds about the ocean, through the application of Collective Intelligence, a facilitation and problem solving methodology. The paper presents a meta-analysis of the 657 barriers to teaching about the ocean, highlighting how these barriers are interconnected and influence one another in a European Influence Map. The influence map shows 8 themes: Awareness and Perceived knowledge; Policies and Strategies; Engagement, formal education sector; the Ocean itself; Collaboration; Connections between humans and the ocean and the Blue Economy, having the greatest influence and impact on marine education. “Awareness and Perceived knowledge” in Stage 1, exerts the highest level of overall influence in teaching 12–19 year olds about the ocean. This map and study serves as a roadmap for policy makers to implement mobilisation actions that could mitigate the barriers to teaching about the ocean. Examples of such actions include free marine education learning resources such as e-books, virtual laboratories or hands-on experiments. Thus, supporting educators in taking on the challenge of helping our youth realise that the ocean supports life on Earth is essential for education, the marine and human well-being.

Collective intelligence in medical diagnosis systems: A case study

Diagnosing a patient's condition is one of the most important and challenging tasks in medicine. We present a study of the application of collective intelligence in medical diagnosis by applying consensus methods. We compared the accuracy obtained with this method against the diagnostics accuracy reached through the knowledge of a single expert. We used the ontological structures of ten diseases. Two knowledge bases were created by placing five diseases into each knowledge base. We conducted two experiments, one with an empty knowledge base and the other with a populated knowledge base. For both experiments, five experts added and/or eliminated signs/symptoms and diagnostic tests for each disease. After this process, the individual knowledge bases were built based on the output of the consensus methods. In order to perform the evaluation, we compared the number of items for each disease in the agreed knowledge bases against the number of items in the GS (Gold Standard). We identified that, while the number of items in each knowledge base is higher, the consensus level is lower. In all cases, the lowest level of agreement (20%) exceeded the number of signs that are in the GS. In addition, when all experts agreed, the number of items decreased. The use of collective intelligence can be used to increase the consensus of physicians. This is because, by using consensus, physicians can gather more information and knowledge than when obtaining information and knowledge from knowledge bases fed or populated from the knowledge found in the literature, and, at the same time, they can keep updated and collaborate dynamically.

A study for the mechanism of expression of individual creativity throughout the social learning platform in cluster computing environment (focus on scenario and data collection design)

As social network service (SNS) is set as life friendly service, the knowledge of ordinary individuals is being used in educational arena. That is, collective intelligence is built between students or between students and SNS, not between teachers and student in hierarchical educational situation, and the application plan for collective intelligence has been suggested as a new topic in cluster computing environment. This study suggests social learning platform in a specific way to realize the collective intelligence with the interaction between students, teachers and external helper (using API) in educational filed. In this study social learning platform means the aggregation of specific system that collective intelligence helps individual's creativity realize. This study is expected to help for application, expansion and creative linkage of collective intelligence which is able to use at social education in the future, also help the new relation between students and teachers in cluster computing environment.

장르유사도와 선호장르를 이용한 협업필터링 설계

전자상거래와 소셜미디어 서비스의 활성화에 따라, 집단지성을 개인 맞춤 서비스에 활용하는 추천시스템에 관한 연구가 활발히 진행되고 있다. 또한, 스마트폰의 발달과 모바일 환경의 발달에 따라 단말의 제약성에도 불구하고 개인화 서비스에 대한 연구가 가속화되고 있다. 대표적인 예로 위치기반 서비스와의 결합이다. 이에 본 연구에서는 영화의 장르유사도와 선호장르를 이용한 추천시스템을 제안한다. 영화 장르 유사도 프로파일을 생성하여 이를 모바일실험 환경에서 서비스 될 수 있도록 설계하고 프로토 타이핑 한 후에 MovieLens 데이터를 적용하여 평가한다. As e-commerce and social media service evolves, studies on recommender systems advance, especially concerning the application of collective intelligence to personalized custom service. With the development of smartphones and mobile environment, studies on customized service are accelerated despite physical limitations of mobile devices. A typical example is combined with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A profile of movie genre similarity is generated and designed to provide related service in mobile experimental environment before prototyping and testing with data from MovieLens.

Recommender system design using movie genre similarity and preferred genres in SmartPhone

As e-commerce (e.g. www.amazon.com) and social media (e.g. www.facebook.com) services evolve, studies of recommender systems advance, especially concerning the application of collective intelligence to personalized service. With the development of smartphones and the new mobile environment, studies of customized services increase despite the physical limitations of mobile devices. A typical example combines customized services with location-based services. In this study, we propose a recommender system using movie genre similarity and preferred genres. A movie genre similarity profile is designed and generated to provide related services in a mobile experimental environment before prototyping and testing with data from MovieLens. In order to accomplish this, genre similarity correlations are determined with a Pearson correlation coefficient, and similar clusters are derived. The correlations within clusters are used to define genre similarity. Genre similarity is then used to recommend new genres to targeted customers.

Designing for collective intelligence

A collective intelligence application is one that harnesses the knowledge and work of its users to provide the data for the application and to improve its usefulness. The most hyped examples of collective intelligence applications have been labeled as "Web 2.0" applications. Web 2.0 is an amorphous term used to define a computing paradigm that uses the Web as the application platform and facilitates collaboration and information sharing between users. Classic examples of Web 2.0 applications include: wikis, blogs (or Weblogs), social network services, and social bookmarking. Collective intelligence is not a new concept. As long ago as 1968, computer visionaries foresaw the ability of computers to be applied to cooperation in creative endeavors by allowing people capable of solving specific problems to share their ideas. However, collective intelligence has been gaining momentum as new tools supporting collaboration have become available. The concept of collective intelligence is now being explored by businesses interested in using it for collaborative innovation and by researchers interested in addressing systemic problems like climate change. Collective intelligence is a fundamentally different way of viewing how applications can support human interaction and decision making. Most traditional applications have focused in improving the productivity or decision making of the individual user. The emphasis has been on providing the tools and data necessary to fulfill a specific job function. Under the collective intelligence paradigm, the focus is on harnessing the intelligence of groups of people to enable greater productivity and better decisions than are possible by individuals working in isolation. The shift to a collective intelligence paradigm requires software developers to have different ways of thinking about how their how software might be used and what features would enable better visualization and use of information among groups of people. The new breed of collective intelligence applications needs to center around user defined data that can be reused to support decision making, team building, or to improve understanding of the world around us. The users of these systems should play a central role in defining what data is important and how the data is used. The essential features of collective intelligence applications are similar to the design patterns for Web 2.0 applications except that collective intelligence applications can be custom applications designed for small highly specialized domains instead of the larger Web audience served by most Web 2.0 applications. The seven principle collective intelligence application requirements are (adapted from O'Reilly): 1. Task specific representations: Domain specific collective intelligence applications should support views of the task that are tailored to the particular domain. 2. Data is the key: Collective intelligence applications are data centric and should be designed to collect and share data among users. 3. Users add value: Users of collective intelligence applications know the most about the value of the information it contains. The application should provide mechanisms for them to add to, modify, or otherwise enhance the data to improve its usefulness. 4. Facilitate data aggregation: The ability to aggregate data adds value. Collective intelligence applications should be designed such that data aggregation occurs naturally through regular use. 5. Facilitate data access: The data in collective intelligence applications can have use beyond the boundaries of the application. Collective intelligence applications should offer Web services interfaces and other mechanisms to facilitate the re-use of data. 6. Facilitate access for all devices: The PC is no longer the only access device for internet applications. Collective intelligence applications need to be designed to integrate services across handheld devices, PCs, and internet servers. 7. The perpetual beta: Collective intelligence applications are ongoing services provided to its users thus new features should be added on a regular basis based on the changing needs of the user community. The processes involved in designing and implementing specialized collective intelligence applications are discussed below in the context of DDtrac, a Web-based application that allows for the easy collection and summary of special education data.

Developing a collective intelligence application for special education

This research uses an action research methodology to develop a web based collective intelligence application, DDtrac. DDtrac allows special education practitioners to collect data and share insights related to student performance during educational tasks and social interactions and can be used to assess special education student progress and improve decision making. A survey of 40 special education professionals and a four year case study using a single subject both indicate that educators, clinicians, families, parents, or other professionals that work with individuals with developmental disabilities achieve tangible benefit from the real time data tracking and decision support provided by the DDtrac application. The development of the DDtrac application and subsequent end-user evaluation is used to develop a set of six requirements for collective intelligence applications. These requirements can be used to guide future developers seeking to create web based applications that harness the collective intelligence of groups.

Collective Intelligence and Knowledge Ecosystems: The Future of the U.S. Intelligence Community

Concerned that the U.S. lacks a modern technology strategy for national security, this research paper discusses bottom-up approaches to inter-organizational collaborations that national security efforts must develop to successfully share knowledge within and across organizational assets. Without technology-aided collaborations and knowledge sharing activities that span agencies, U.S. national security will be limited in its ability to analyze intra- and international social transactions for aberrant patterns. For a fuller, more holistic approach to intelligence gathering, the U.S. government needs to cultivate more vibrant knowledge ecosystems. Research regarding the application of collective intelligence and knowledge ecosystems to national security explores strategies that promote collaboration between government agencies - and encourage partnerships with members of the private and academic sectors - to share relevant knowledge for successful inter-agency collaborations. Such knowledge ecosystems can assist with the identification of potential geopolitical concerns and support appropriate response if necessary (e.g., law enforcement for terrorists or drug cartels, U.N. aid for refugees, or public health for infectious diseases). Through application of these findings, improved inter-organizational information collaborations, to include augmented group cognition and emerging virtual institutions, follow from this empirical research. The CIA's Global Trends 2015 report predicts governments will have less control over flows of information, technology, diseases, migrants, arms, and financial transactions across their borders. The ability and agility to form partnerships to exploit increased information flows increasingly depends on effective governance. I believe all of this can be performed while preserving the freedom of privacy that is a core tenet of a free democracy.