Emergence Of Collective Intelligence Research Articles

Nonreciprocal interactions in crowd dynamics: Investigating the impact of moving threats on pedestrian speed preferences

Nonreciprocal interaction crowd systems, such as human–human, human–vehicle, and human–robot systems, often have serious impacts on pedestrian safety and social order. A more comprehensive understanding of these systems is needed to optimize system stability and efficiency. Despite the importance of these interactions, empirical research in this area remains limited. Thus, in our study we explore this underresearched area, focusing on scenarios where nonreciprocity plays a critical role, such as mass stabbings, which pose a substantial risk to public safety. We conducted the first experiments on this system and analysed high-accuracy data obtained from these experiments. Specifically, we conduct laboratory experiments on three scenarios: single exits, dual exits, and obscured chases. Then, we introduce an equation to describe the mechanism behind a direct threat zone, which significantly affects pedestrian behaviour. The extent of the direct threat zone is determined by the speed of the moving threat and the radius of danger occurrence. The equation can provide insights for different nonreciprocal interaction crowd systems, where the subjects of the nonreciprocal interactions can be a pedestrian, an autonomous robot or a vehicle. We further categorize potential threats into direct, adjacent, and rear-view zones, quantifying the level of threat for pedestrians. Our study revealed that a pedestrian’s desired velocity correlated positively with potential threat intensity, increasing until near the direct threat zone. An emerging steady state is observed when escape routes are blocked by moving threats. This deviation affects the density–velocity relationship, making it distinct from the general relationship. This deviation signifies unique pedestrian behaviour in the presence of moving threats. Additionally, the rate of change in the angle for pedestrian motion in various desired directions is synchronized. This indicates the emergence of collective intelligence in nonreciprocal interaction crowd systems. As a result, our study may constitute a pioneering step towards understanding nonreciprocal interactions in crowd systems through laboratory experiments. These findings may enhance pedestrian safety and inform not only government crowd management strategies but also individual self-protection measures.

Emergence of collective intelligence in industrial cyber-physical-social systems for collaborative task allocation and defect detection

The manufacturing industry is facing the challenge of meeting the growing demand for personalized products, which requires enhanced agility, flexibility, reconfigurability, and sustainability on the shop floor. To tackle these requirements, one possible solution is to foster collective intelligence (CI) by sharing data and knowledge among human operators, machines, and workpieces, thereby improving resource utilization and enabling informed decision-making. However, the implementation of CI in manufacturing systems poses several challenges, including interoperability issues and the complexity of communication and coordination between heterogeneous manufacturing resources. These challenges can be addressed by integrating cyber-physical-social systems (CPSS) and distributed artificial intelligence. Therefore, this paper presents a framework for enabling the emergence of CI in industrial CPSS to achieve collaborative task allocation and defect detection. The framework infuses intelligence into physical manufacturing resources through CPSS configuration and establishes real-time collaborative communication coupled with decentralized decision-making using a multi-agent system. Additionally, an online deep Q-network (DQN) is employed to train a smart scheduler agent for self-organized assignment of manufacturing tasks to machines. The proposed method is implemented in a 3D printing factory testbed. Experimental findings demonstrate the practicality and effectiveness of the proposed method, which has been deployed in the 3D printing factory for a few months and has reduced production costs, defects, and nonconformities in 3D printed parts.

The Group Provisory Conclusion, a powerful tool for science debut

This article will take you through the evolution of our approach in presenting and communicating science. For twenty years ‘1, 2, 3, sciences’ has run participatory live workshops for adults. A special tool, the Group Provisory Conclusion or GPC, involving each participant, contributes to the success. Our expectation was to rekindle the public’s interest through participatory methods, supported by the emergence of collective intelligence. It describes our actions to help people reduce their apprehension towards science.

Emergence of Collective Intelligence Through Behavioral Excitatory Transmission in Maze-Solving Termites

Emergence of Collective Intelligence Through Behavioral Excitatory Transmission in Maze-Solving Termites

Digitalization for Human Resource Management

Behaviours, sometimes different expectations as well as mastery and use of IT are new data to be integrated within the framework of an effective human resources policy. In the absence of real intergenerational management that promotes synergy and the emergence of collective intelligence, individual employees develop withdrawal strategies and anticipate their exclusion, which leads them to self-stigmatize. The objective of intergenerational management is to establish a working climate that promotes communication between generations and their well-being. It makes it possible to organize interactions between generations in work situations and define a common operating language so that profiles of the same age and different ages can lead to the implementation of a joint project. In other words, the usefulness of intergenerational management is to reduce tensions and conflicts beyond stereotypes. The article deals with the issue of intergenerational management to understand the sources of disputes between the different generations within Moroccan companies in the banking sector. The main objective of the research is twofold, firstly to determine the source of the intergenerational conflict by referring to a set of theoretical and empirical studies. The latter shows that the conflict between the different generations is closely linked to the systems of human resource management. On the other hand, bringing concrete leads to companies to understand the problem of intergenerational and to adapt their management methods according to the expectations of the different socio-professional categories. To respond to this problem in the Moroccan context, a qualitative empirical study was conducted with managers from three generations: baby boomers, generation X and generation Y and working in four banks. The empirical research has made it possible to understand the expectations of different ages and shows that beyond the intrinsic characteristics of individuals at work, expectations converge, and the organization of action is likely to be the primary source of intergenerational conflicts. Also, the digitization and use of the computer tool by different generations do not present an obstacle to intergenerational cooperation. The present study provided answers to the defined problem. It also made it possible to identify other variables capable of explaining the intergenerational conflict other than those identified in the literature review. It is more particularly the problem of time management and the lack of support systems. As a research perspective, the results of this study will be useful for testing these hypotheses and confirming them, considering the specificities of Moroccan business. Keywords collective intelligence, digitalization, expectation, human resource management, individual competence, intergenerational conflict.

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.

Emergence of collective intelligence in online communities

The main focus of this study is on the scientific identification of preconditions for collective intelligence (CI) to emerge and the prediction of possible development scenarios based on qualitative research results. The research subject is online communities that use innovative social technologies encouraging collective decision making, creativity, entrepreneurship and cooperation. The research treats such platforms of indirect communication as environments for the development of CI. The qualitative content analysis aims at exploring similarities, differences, and relations between interview segments and the theoretical framework for CI Potential Index. Qualitative research allows to deepen the understanding of CI and specifies a further direction for theoretical and empirical research. The research conclusions lead to the re-design and alteration of the proposed methodological framework for CI Potential Index.

Power Through Things: Following Traces of Collective Intelligence in Internet of Things

Purpose – it is becoming increasingly difficult to ignore the input Internet of Things (IoT) has to offer in development of public, business and other societal structures. Therefore, paper seeks to determine the current state of knowledge in the field of IoT terms of wisdom creation and emergence of collective intelligence. First, we discuss concept of collective intelligence, then we define phenomena of IoT and identify areas of its application. Later, the author reviews how intelligent outputs of IoT are defined in scientific literature. These findings of theoretical investigation may shed some light on research field that is promising but still very vague. Design/methodology/approach – this article provides a general overview of IoT concept and its growing relation to collective intelligence. Methods of document analysis and content analysis were applied. Theoretical analysis enabled recognition of IoT phenomena in relation to wisdom creation and emergence of collective intelligence. Findings – general overview of the field revealed that new understanding of collective intelligence surfaces. Often intelligent behavior and decisions emerge from ever increasing cooperation between ‘things’ and humans. The variety of new concepts and authors trying to describe relationship of ‘things’ with each other and humans when creating intelligent outcomes revealed that this field is still in its very infancy and still needs considerable amount of industry and scientific efforts to be understood and executed. Research limitations – although the paper has successfully demonstrated that IoT provides vast amounts of data for people to process and create knowledge this could be considered only as initial phase in studying the field. IoT and its intelligent outcomes need more investigations in terms of real life case studies and industry reviews in order to create valid definitions, models and future guidelines. Practical implications – this study provides an exciting opportunity to advance our knowledge of Internet of Things and could be used as theoretical background for further analysis of emergence of collective intelligence in interactive networks of people and things. Value – to date there has been little research summarizing scientific efforts in the field of IoT. This paper provides general overview of the knowledge on IoT and could be used as guidelines for further research. Keywords: Internet of Things, Collective Intelligence, Wisdom. Research type: General overview.

Topical community detection from mining user tagging behavior and interest

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.