Collective Intelligence Approach Research Articles

Connecting the challenges of quality and equality in higher education using the collective intelligence approach: findings from an international expert panel

This article presents the results of phase one of the B-CAUSE project, an international project designed to connect equity and quality in higher education. Expert stakeholders worked together using collective intelligence methods to develop a shared understanding of (1) key features of equity-focused quality higher education, (2) barriers to equity-focused quality higher education and (3) options for overcoming these barriers. Results highlight the potential transformative dimension to equity-focused, quality higher education, including responsiveness to students, participatory design, pluralism and openness and the educational imperative to promote equity in practice. The barriers and options generated by experts focused on institutional resources and supports, excellence-equity tensions, systemic norms and pressures, reflective complexity, awareness and empathy and student supports. The collective intelligence of experts provides the basis for ongoing research, strategy and pedagogical or curricular innovation as part of the B-CAUSE project and other international efforts to foster equity-focused, quality higher education.

Impulsive Control Discrete Fractional Neural Networks in Product Form Design: Practical Mittag-Leffler Stability Criteria

The planning, regulation and effectiveness of the product design process depend on various characteristics. Recently, bio-inspired collective intelligence approaches have been applied in this process in order to create more appealing product forms and optimize the design process. In fact, the use of neural network models in product form design analysis is a complex process, in which the type of network has to be determined, as well as the structure of the network layers and the neurons in them; the connection coefficients, inputs and outputs have to be explored; and the data have to be collected. In this paper, an impulsive discrete fractional neural network modeling approach is introduced for product design analysis. The proposed model extends and complements several existing integer-order neural network models to the generalized impulsive discrete fractional-order setting, which is a more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate impulsive controllers are designed, and practical Mittag-Leffler stability criteria are proposed. The Lyapunov function strategy is applied in providing the stability criteria and their efficiency is demonstrated via examples and a discussion. The established examples also illustrate the role of impulsive controllers in stabilizing the behavior of the neuronal states. The proposed modeling approach and the stability results are applicable to numerous industrial design tasks in which multi-agent systems are implemented.

Constructing a Knowledge Graph for the Chinese Subject Based on Collective Intelligence

Knowledge graphs are a valuable tool for intelligent tutoring systems and are typically constructed with a focus on objectivity and accuracy. However, they may not effectively capture the subjectivity and complex relationships often present in the humanities. To address this issue, a dynamic visualization of subject matter knowledge graph was developed using a collective intelligence approach that integrates the individual intelligence of learners and considers cognitive diversity to construct and evolve the knowledge graph. The approach resulted in the construction of 722 knowledge associations and the evolution of 584 triples. A survey assessed the effectiveness and user-friendliness, revealing that this approach is effective, easy to use, and can improve subject matter knowledge ontology. In conclusion, combining individual and collective intelligence is a promising approach for building effective knowledge graphs in subject areas with subjectivity and complexity.

Geospatial Collective Intelligence Approach in the appreciation phase of military planning

Military planning is an essential process by which it is possible to determine the best idea of manoeuver and the most efficient way to use of military power, allowing the commander to make accurate and timely decisions; for this purpose, it is essential to have a deep knowledge of military science and operational factors such as geographical space, force, and time, which allow to have greater probabilities of success in the execution of military operations. This article intends to propose a new approach in the assessment phase of military planning, which allows providing support to the commander in complex geographic scenarios, through which current and prospective geospatial patterns can be established, mainly focused on the planning, organization and use of land resources.

‘Insight unlocked’: Applying a collective intelligence approach to engage employers in informing local skills improvement planning

This paper demonstrates how the innovative application of a Collective Intelligence approach enhanced Local Skills Improvement Planning information for employers, education and skills training organisations and regional economic policy organisations. This took place within a Knowledge Transfer Partnership between a Chamber of Commerce and a University. This aimed to develop and deploy regional business intelligence for enhanced policy and decision-making in enterprise and economic development. The project converged knowledge from several research centres including economics, entrepreneurship and innovation, data science, and Artificial Intelligence. The paper presents a project case study which provides two contributions to applied knowledge. Firstly, it demonstrates how a Collective Intelligence (CI) approach can be applied to achieve rapid results in resolving the real-world problem of local skills information availability. Useful real-time data was gathered from employers in three sectors on skills requirements, supply and training. This was analysed using Artificial Intelligence tools, then shared publicly via an automated Internet portal, providing a scalable model for wider use. Secondly, it explores and evaluates how the knowledge exchange (KE) process can function effectively and quickly in applying CI-based innovation in practical ways which create new value, within a Knowledge Transfer Partnership between a University and Chamber of Commerce.environment.

Compromising improves forecasting.

Predicting the future can bring enormous advantages. Across the ages, reliance on supernatural foreseeing was substituted by the opinion of expert forecasters, and now by collective intelligence approaches which draw on many non-expert forecasters. Yet all of these approaches continue to see individual forecasts as the key unit on which accuracy is determined. Here, we hypothesize that compromise forecasts, defined as the average prediction in a group, represent a better way to harness collective predictive intelligence. We test this by analysing 5 years of data from the Good Judgement Project and comparing the accuracy of individual versus compromise forecasts. Furthermore, given that an accurate forecast is only useful if timely, we analyze how the accuracy changes through time as the events approach. We found that compromise forecasts are more accurate, and that this advantage persists through time, though accuracy varies. Contrary to what was expected (i.e. a monotonous increase in forecasting accuracy as time passes), forecasting error for individuals and for team compromise starts its decline around two months prior to the event. Overall, we offer a method of aggregating forecasts to improve accuracy, which can be straightforwardly applied in noisy real-world settings.

Managing the Inner Workings of Collective Intelligence Approaches for Wicked Problems – An Assessment Model and Evaluation

Ill-defined and complex problems that affect multiple stakeholders with potentially conflicting perspectives are often referred to as wicked problems. The utilization of collective intelligence (CI) via web-based platforms is a promising approach for addressing such wicked problems. The management of these information systems would benefit from evidence-based decision support regarding facilitation and improvement efforts. However, to date, there is no suitable model to guide such efforts. Existing approaches address specific applications or cover certain assessment areas but do not provide a holistic perspective. Meanwhile, research offers substantial insights into best practices for addressing wicked problems and running CI applications. This paper develops an assessment model comprising five central success dimensions for information systems that address wicked problems. Their subdimensions and associated measurement metrics allow for evidence-driven facilitation and improvement of CI applications for wicked problems. Apart from the model’s capability to improve future runs and processes, it also offers the potential to provide immediate insights for facilitation efforts during runtime. The model was validated with a platform dealing with the assessment of risks presented by global climate change. This evaluation generated strong evidence for the model’s applicability and usefulness.

Genesis of an innovation-based entrepreneurial ecosystem: exploring the role of intellectual capital

PurposeThe aim of the study is to explore the genesis of entrepreneurial ecosystems (EE) and highlight the role played by intellectual capital (IC) in that process. Specifically, the paper adopts the collective intelligence approach, and the study shows how human capital (HC), structural capital (SC) and relational capital (RC) interact to create an entrepreneurial ecosystem.Design/methodology/approachThe paper adopts a single case study of an Italian EE. The data analysis is based upon the collection of different sources of data: semi-structured interviews with representatives of each actor of the ecosystem; email correspondence; meetings report; a 24-months period of direct observation. Given the novelty of the topic, the qualitative method seems well suited for studying innovation-based EE since the method offers rich data about a phenomenon in real-life context.FindingsThe case is a top-down, innovation-based EE in which all main components of the IC play a crucial role from the initial stage. Findings show how the constant interchange between IC components occurs at two different levels: the micro and the meso level. HC and RC play major roles at both levels, whilst SC only occurs at a meso level, representing the environment in which the whole ecosystem takes place. Additionally, the use case, a new intangible asset integrating all three components of IC, emerged as one of the main outcomes of this innovation-based EE.Originality/valueThe paper contributes to a rather unexplored topic in the existing literature on EE and IC, namely the formation process of EE and the role played by IC within that process. Additionally, through the application of the collective intelligence approach, the authors shed light on the need to manage IC at both micro and meso level in the creation of an EE.

Chronic pain self-management in middle-aged and older adults: A collective intelligence approach to identifying barriers and user needs in eHealth interventions.

ObjectiveseHealth refers to health services and health information delivered or enhanced through the internet and related technologies. The number of eHealth interventions for chronic pain self-management is increasing. However, little evidence has been found for the overall efficacy of these interventions for older adults. The aim of the current study was to use a Collective Intelligence approach to identify the barriers and specific user needs of middle-aged and older adults using eHealth for chronic pain self-management.MethodsA Collective Intelligence workshop was conducted with middle-aged and older adults to generate, clarify, select, and structure ideas in relation to barriers to eHealth use and specific design requirements for the purposes of chronic pain self-management. Prior to attending the workshop, participants received a trigger question requesting the identification of five barriers to eHealth use for chronic pain self-management. These barriers were categorised and presented to the group along with barrier-related scenarios and user need prompts, resulting in the generation of a set of ranked barriers and a set of user needs.ResultsA total of 78 barriers were identified, from which six categories emerged: Content, Support, Technological, Personal, Computer Literacy and Accessibility. Additional idea-writing and group reflection in response to these barriers revealed 97 user needs.ConclusionThis is the first study to use Collective Intelligence methods to investigate barriers to eHealth technology use and the specific user needs of middle-aged and older adults in the context of chronic pain self-management. The results of the current study provide a platform for the design and development of enhanced eHealth interventions for this population.

The complementarities of big data and intellectual capital on sustainable value creation; collective intelligence approach.

It is evident in the literature that both intellectual capital and big data analytics create value to the organizations independently, but how threats, opportunities, capabilities and value creation for intellectual capital change with big data adoption is largely unexplored. This paper aims to develop an analytical framework for identifying challenges, opportunities, capabilities and value creation in the face of complementarity between big data and components of intellectual capital. The paper uses a Collective Intelligence approach as a theoretical background. Based on Structured Literature Review, the current study has developed an analytical framework for organizations to be used as a decision-making tool while making investment in big data and managing intellectual capital. Findings suggest that the scope of human capital has changed largely as now employees are expected much more than in the past with strong analytical, dynamic, technical and IT capabilities. Structural capital calls for new practices, routines and procedures to be adopted and old methods to unlearn whereas relational capital stresses the importance of network building and social media to create sustainable value for the society.

Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)

Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables.

The Use of Collective Intelligence to Create Efficient Marketing Strategies

There is no doubt that almost all companies recognized the necessity and the significant role of marketing strategies in the development and the progress of any business. However, the challenge still is how to elaborate and create effective marketing strategies? The purpose of this study is to investigate and illustrate how we could create efficient marketing strategies by using the Collective Intelligence approach? Interviews were conducted with marketing department teams; added to participants from other department members and also customers and stakeholders. through three different steps (before, during, and after the use of this approach); in order to provide and discover the feedbacks and results of all participants through the implementation of the Collective Intelligence approach. Following the result; There is a consensus from the participants of this research study confirmed that the use of this new concept of collective intelligence will not only transform the design of the future trajectory of elaboration of marketing strategies but also the future of work.

Evaluation of Pattern Recognition Techniques in Response to Cardiac Resynchronization Therapy (CRT)

Cardiac resynchronization therapy (CRT) improves cardiac function in patients with heart failure (HF), and the result of this treatment is decrease in death rate and improving quality of life for patients. This research is aimed at predicting CRT response for the prognosis of patients with heart failure under CRT. According to international instructions, in the case of approval of QRS prolongation and decrease in ejection fraction (EF), the patient is recognized as a candidate of implanting recognition device. However, regarding many intervening and effective factors, decision making can be done based on more variables. Computer-based decision-making systems especially machine learning (ML) are considered as a promising method regarding their significant background in medical prediction. Collective intelligence approaches such as particles swarm optimization (PSO) algorithm are used for determining the priorities of medical decision-making variables. This investigation was done on 209 patients and the data was collected over 12 months. In HESHMAT CRT center, 17.7% of patients did not respond to treatment. Recognizing the dominant parameters through combining machine recognition and physician’s viewpoint, and introducing back-propagation of error neural network algorithm in order to decrease classification error are the most important achievements of this research. In this research, an analytical set of individual, clinical, and laboratory variables, echocardiography, and electrocardiography (ECG) are proposed with patients’ response to CRT. Prediction of the response after CRT becomes possible by the support of a set of tools, algorithms, and variables.

Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach

BackgroundAutomated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models for the classification of global sclerosis and glomerular cell proliferation, there are several other glomerular pathological findings required for diagnosis, and the comprehensive models for the classification of these major findings have not yet been reported. Whether the cooperation between these AI models and clinicians improves diagnostic performance also remains unknown. Here, we developed AI models to classify glomerular images for major findings required for pathological diagnosis and investigated whether those models could improve the diagnostic performance of nephrologists. MethodsWe used a dataset of 283 kidney biopsy cases comprising 15,888 glomerular images that were annotated by a total of 25 nephrologists. AI models to classify seven pathological findings: global sclerosis, segmental sclerosis, endocapillary proliferation, mesangial matrix accumulation, mesangial cell proliferation, crescent, and basement membrane structural changes, were constructed using deep learning by fine-tuning of InceptionV3 convolutional neural network. Subsequently, we compared the agreement to truth labels between majority decision among nephrologists with or without the AI model as a voter. ResultsOur model for global sclerosis showed high performance (area under the curve: periodic acid-Schiff, 0.986; periodic acid methenamine silver, 0.983); the models for the other findings also showed performance close to those of nephrologists. By adding the AI model output to majority decision among nephrologists, out of the 14 constructed models, the results of the majority decision showed improvement in sensitivity for 10 models (four of them were statistically significant) and specificity for eight models (five significant). ConclusionOur study showed a proof-of-concept for the classification of multiple glomerular findings in a comprehensive method of deep learning and suggested its potential effectiveness in improving diagnostic accuracy of clinicians.

Diabetic Retinopathy Detection Using Collective Intelligence

Much attention has been focused on describing the utility of artificial intelligence (AI) applied to diabetic retinopathy data. It has been determined that there are ample opportunities for AI algorithms within medicine and that AI is even superior to what we can determine with the professional human eye. However, fewer studies actually have looked at a combined model, or rather, a collective intelligence approach of both human and computer/machine efforts. We attempt to describe and demonstrate the power of collective intelligence in the future of medicine and to offer ways to consider a more complementary approach to both humans and computers.

Collective intelligence approaches in interactive evolutionary multi-objective optimization

AbstractEvolutionary multi-objective optimization algorithms (EMOAs) have been successfully applied in many real-life problems. EMOAs approximate the set of trade-offs between multiple conflicting objectives, known as the Pareto optimal set. Reference point approaches can alleviate the optimization process by highlighting relevant areas of the Pareto set and support the decision makers to take the more confident evaluation. One important drawback of this approaches is that they require an in-depth knowledge of the problem being solved in order to function correctly. Collective intelligence has been put forward as an alternative to deal with situations like these. This paper extends some well-known EMOAs to incorporate collective preferences and interactive techniques. Similarly, two new preference-based multi-objective optimization performance indicators are introduced in order to analyze the results produced by the proposed algorithms in the comparative experiments carried out.

Methods of Generating Collective Behavior in a Robotic Swarm: An Automatic Design Based on a Collective Intelligence Approach

Methods of Generating Collective Behavior in a Robotic Swarm: An Automatic Design Based on a Collective Intelligence Approach

A decision support framework for remanufacturing of highly variable products using a collective intelligence approach

Abstract We propose a new theoretical decision support system framework based on multiagent systems able to design new products from post-used components. This framework, based on the genericity of its input, is open and allows to design new products in real time from highly variable post-used components and a customer demand while negotiating with the user if the need cannot be fulfilled. Finally, the multiagent system approach implements a social analogy in order to allow agents to interact effectively with each other and to address the group formation problem.

Synthetic accessibility assessment using auxiliary responses

Despite the recent advances in computational approaches to discovering new chemical compounds, accessibility assessment of designed compounds has still been a difficult task to automate because it is a heavily knowledge intensive task. A promising solution to such “AI-hard” tasks is collective intelligence approaches that aggregate opinions of a group of human non-experts or semi-experts. However, the existing aggregation methods rely only on synthetic accessibility evaluation scores given by humans, and they do not exploit auxiliary information obtained as byproducts of human evaluations such as that related to chemical structures. In this paper, we propose to exploit such auxiliary responses to obtain better aggregations. We introduce a new two-stage aggregation method of semi-expert judgments consisting of synthetic accessibility evaluation scores along with auxiliary responses that select substructures of targets obstructive to their synthesis. The first stage divides both semi-experts and substructures into clusters using stochastic block models to identify similar skills or properties. The second stage aggregates judgments while considering groups of semi-experts and substructures, and predicts synthetic accessibility. Our experiments show that the use of auxiliary responses improves the prediction performance and gives insight into evaluators and the structure of evaluated compounds.

Digital entrepreneurship ecosystem: How digital technologies and collective intelligence are reshaping the entrepreneurial process

Digital technologies have nowadays a significant impact on how new business ventures are imagined and created. The arising technology paradigm is leveraging the potential of collaboration and collective intelligence to design and launch more robust and sustainable entrepreneurial initiatives. However, although the topic of digital entrepreneurship is relevant and timely, there is a limited literature discussion on the real impact of digital technologies and collaboration on the entrepreneurial process. Further research is needed to describe the nature and characteristics of the entrepreneurial ecosystem enabled by the new socio-technical paradigm. Based on extant literature, this article proposes a definition of digital entrepreneurship ecosystem by highlighting the integrated digital-output and digital-environment perspectives. A collective intelligence approach is then adopted to define a descriptive framework and identify the distinguishing genes of a digital entrepreneurship ecosystem. Four dimensions associated to digital actors (who), digital activities (what), digital motivations (why), and digital organization (how) are defined and discussed. The framework was also applied to describe 9 real cases of companies and initiatives, which are analyzed as digital entrepreneurship ecosystems along the four key dimensions presented. The article ends with a discussion about the results and a research agenda for future studies.