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Summary: Background: The recent COVID-19 pandemic highlighted the challenges for traditional forecasting. Prediction markets are a promising way to generate collective forecasts and could potentially be enhanced if high-quality crowdsourced inputs were identified and preferentially weighted for likely accuracy in real-time with machine learning. Methods: We aim to leverage human prediction markets with real-time machine weighting of likely higher accuracy trades to improve performance. The crowd sourced Almanis prediction market longitudinal platform (n = 1822) and Next Generation Social Science (NGS2) platform (n = 103) were utilised. Findings: A 43-feature model predicted accurate forecasters, those with top quintile relative Brier accuracy, with subsequent replication in two out-of-sample datasets (pboth

Collective Intelligence (CI) is important for groups that seek to address shared problems. CI in human groups can be mediated by educational technologies. The current paper presents a framework to support design thinking in relation to CI educational technologies. Our framework is grounded in an organismic-contextualist developmental perspective that orients enquiry to the design of increasingly complex and integrated CI systems that support coordinated group problem solving behaviour. We focus on pedagogies and infrastructure and we argue that project-based learning provides a sound basis for CI education, allowing for different forms of CI behaviour to be integrated, including swarm behaviour, stigmergy, and collaborative behaviour. We highlight CI technologies already being used in educational environments while also pointing to opportunities and needs for further creative designs to support the development of CI capabilities across the lifespan. We argue that CI education grounded in dialogue and the application of CI methods across a range of project-based learning challenges can provide a common bridge for diverse transitions into public and private sector jobs and a shared learning experience that supports cooperative public-private partnerships, which can further reinforce advanced human capabilities in system design. [ABSTRACT FROM AUTHOR]

The First Nations and Métis Organ Donation and Transplantation Network (the Network) facilitates Indigenous-driven, culturally-informed, and safe research, policies, education, and advocacy regarding organ donation and transplantation through the building of collective intelligence among Indigenous peoples in Canada. The Network's think tank comprises Indigenous Elders, thought leaders, and persons with lived experiences of organ donation—living donors and organ recipients—as well as healthcare professionals, outreach workers, and university-based researchers. The Network responds to the failure of governmental institutions to reduce health disparities facing Indigenous peoples, and the dispersal of Indigenous collective intelligence caused by changing federal or provincial and territorial leadership and priorities. The collective intelligence of Indigenous peoples regarding end-stage organ failure and organ donation and transplantation is central to improving patient experiences, increasing the number of Indigenous organ donors and recipients, and finding pathways for advancing healthcare reforms that prevent and treat end-stage organ failure. [ABSTRACT FROM AUTHOR]

Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development. [ABSTRACT FROM AUTHOR]

Copyright of Vie et Sciences de l'Entreprise is the property of ANDESE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

World of Honor, a danmei-adapted Chinese TV drama, was one of the most-watched TV series in China in 2021. Conceptualizing translation as a rewriting practice, based on a theoretical framework drawn from paratext studies and media studies, this article investigates how audience-created paratexts reframe queer pop in the translation of World of Honor in cyberspace. The research shows that comments and fanvids, as audience-created paratexts on YouTube, serve to render the implicit male-male romance between the two main characters in the TV drama more explicit in the streaming media. The comments help to interpret the homoerotic subtexts in the translation of subtitles and bridge cultural gaps for international audiences, while fanvids direct the storyline towards a danmei theme with the interplay of music and televisual footage. These media paratexts provide a communal space for audiences to interpret, communicate and participate, and enhance our understanding of danmei subculture within online communications. [ABSTRACT FROM AUTHOR]

Many models of learning in teams assume that team members can share solutions or learn concurrently. However, these assumptions break down in multidisciplinary teams where team members often complete distinct, interrelated pieces of larger tasks. Such contexts make it difficult for individuals to separate the performance effects of their own actions from the actions of interacting neighbors. In this work, we show that individuals can overcome this challenge by learning from network neighbors through mediating artifacts (like collective performance assessments). When neighbors' actions influence collective outcomes, teams with different networks perform relatively similarly to one another. However, varying a team's network can affect performance on tasks that weight individuals' contributions by network properties. Consequently, when individuals innovate (through "exploring" searches), dense networks hurt performance slightly by increasing uncertainty. In contrast, dense networks moderately help performance when individuals refine their work (through "exploiting" searches) by efficiently finding local optima. We also find that decentralization improves team performance across a battery of 34 tasks. Our results offer design principles for multidisciplinary teams within which other forms of learning prove more difficult. [ABSTRACT FROM AUTHOR]

Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here, we present a fully automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently sized groups, we find that our method substantially increases diagnostic accuracy: While single diagnosticians achieved 46% accuracy, pooling the decisions of ten diagnosticians increased this to 76%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety. [ABSTRACT FROM AUTHOR]

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge. [ABSTRACT FROM AUTHOR]

In this study, we added voting behavior in which voting proportionately reflects the value of a view (option, opinion, and so on) to the BRT agent. BRT agent is a consensus-building model of the decision-making process among a group of human, and is a framework that allows the expression of the collective behavior while maintaining dispersiveness, although it has been noted that it is unable to reach consensus by making use of experience. To resolve this issue, we propose the incorporation of a mechanism of voting at frequencies proportional to the value estimated using reinforcement learning. We conducted a series of computer-based experiments using the box-pushing problem and verified that the proposed method reached a consensus to arrive at solutions based on experience. [ABSTRACT FROM AUTHOR]

Type I contextuality or inconsistent connectedness is a fundamental feature of both the classical as well as the quantum realms. Type II contextuality (true contextuality or CHSH-type contextuality) is frequently asserted to be specific to the quantum realm. Nevertheless, evidence for Type II contextuality in classical settings is slowly emerging (at least in the psychological realm). Sign intransitivity can be observed in preference relations in the setting of decision making and so intransitivity in decision making may also yield examples of Type II contextuality. Previously, it was suggested that a fruitful setting in which to search for such contextuality is that of decision making by collective intelligence systems. An experiment was conducted by using a detailed simulation of nest emigration by workers of the ant Temnothorax albipennis. In spite of the intransitivity, these simulated colonies came close to but failed to violate Dzhafarov's inequality for a 4-cyclic system. Further research using more sophisticated simulations and experimental paradigms is required. [ABSTRACT FROM AUTHOR]

Effective line loss management necessitates a model-driven evaluation method to assess its efficiency level thoroughly. This paper introduces a "model-driven + data-driven" approach based on collective intelligence theory to address the limitations of individual evaluation methods in conventional line loss assessments. Initially, eight different evaluation methods are used to form collective intelligence to evaluate the line loss management of power grid enterprises and generate a comprehensive dataset. Then, the data set is trained and evaluated using the random forest algorithm, with Spearman rank correlation coefficient as the test metric, to assess the power grid enterprise's line loss management level. Combining model-driven and data-driven methods, this integrated approach efficiently leverages the informational value of indicator data while thoroughly considering the causal and associative attributes within the dataset. Based on data from 61 municipal grid enterprises, both the comparison of multiple AI methods and correlation tests of results verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Socio‐cognitive theory conceptualizes individual contributors as both enactors of cognitive processes and targets of a social context's determinative influences. The present research investigates how contributors’ metacognition or self‐beliefs, combine with others’ views of themselves to inform collective team states related to learning about other agents (i.e., transactive memory systems) and forming social attachments with other agents (i.e., collective team identification), both important teamwork states that have implications for team collective intelligence. We test the predictions in a longitudinal study with 78 teams. Additionally, we provide interview data from industry experts in human–artificial intelligence teams. Our findings contribute to an emerging socio‐cognitive architecture for COllective HUman‐MAchine INtelligence (i.e., COHUMAIN) by articulating its underpinnings in individual and collective cognition and metacognition. Our resulting model has implications for the critical inputs necessary to design and enable a higher level of integration of human and machine teammates. [ABSTRACT FROM AUTHOR]

We share the first reflections and activities of the Project From theory to practice: Application of the model of ethical competence in the context of collective intelligence in a community of information professionals, under development at the School of Communications and Arts of the University of São Paulo, in the Postgraduate modality. Doctorate degree. It is about developing cooperative work for sharing information that fosters technological innovation and scientific communication in the field of Information Science. The research intertwines, in the loom of Information Science, the conceptual threads of González de Gómez's information regime, Lévy's collective intelligence and Varela's ethical competence, acting from the action-research methodology. Our proposal represents a possibility for the emergence of collective intelligence projects in a way of life for the community of knowledge and information producers. A perspective that can contribute to a practice that brings us closer -- as much as possible -- to people and groups in which information manifests itself as a possibility of knowledge. [ABSTRACT FROM AUTHOR]

Until now, various methods have been presented for storing and retrieving information of semi-structured documents, most of them are placed in two groups with batch and incremental approach. In the batch or cluster approach, it is assumed that all the documents can be accessed and clustered, and the documents can be processed several times, which increases the execution time of such algorithms. In the incremental approach, all the documents do not exist in one place, but over time, they are provided to the classification method, and from this point of view, the execution time of such algorithms is less compared to the batch method, and as a result, their execution speed is faster. In this research, our proposed method was compared with XCLS and XCLS+ methods in three evaluation criteria: Entropy, Purity and Fscore. The results showed that the proposed method is preferable to the XCLS and XCLS+ methods in terms of Entropy, Purity and Fscore, and it is slightly less efficient than the XCLS+ method only in the Fscore criterion. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ciencia y Poder Aéreo is the property of Escuela de Postgrados de la Fuerza Aerea Colombiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As homo socius, the man lives in a community. Because the law deals with relationships between people, it is necessary to know the fundamental characteristics of these relationships, which are established in a community between people. Because all major social changes involve crowds, legislation and regulation must know how to address collectives, how they are influenced, how collective emotions are formed, and how they can effectively deal with external behavior in and between groups. This Article presents basic elements of crowds that should be included in legal decisions, especially in general ones. The Article shows potential applications of crowd elements in the law presented as a systemic arrangement of complex adaptive systems that can be reflected in the determination of public opinion through crowds. When a legal system in the right meaning of the word “system” determines public opinion and implements actions through crowds, it could be more effective and efficient and thus also more legitimate.

The term transmedia storytelling refers to diverse processes of creating and distributing stories through available and current media technology to reach a wider or clearly defined audience. In contemporary culture, transmedia storytelling plays an increasingly functional role in the expansively oriented mediation of artistic practices. Nearly every art institution engages with its audience through social networks, and internet presentation and communication have become essential for artists' public presence. In the context of convergence culture, transmedia music is presented in the public space by creating accompanying digital texts enabling diverse interactions and experiences with the audience. Transmedia storytelling is highly relevant to the sphere of music, as this innovative concept can practically be implemented to highlight the uniqueness of a musical piece, product, or practice using 'storytelling' techniques across various media channels, aiming for enhanced promotion in the media space and cultural market. This paper will provide a thorough presentation of the concept of transmedia storytelling, its features and mechanisms, and will also illustrate the potential of this practice in the context of digitally mediated music using concrete examples.

Research in vehicular analytics has explored two different approaches to infer properties about a vehicle's surroundings: (a) using sensors on smartphone devices to infer properties about surroundings, or (b) to use in-vehicle sensors. The latter approach was less studied. In this article, we take a first step beyond research to understand, using a pilot study, how to design vehicular analytics at scale. Our pilot prototype NORA (Network Oriented Road Applications) contains novel algorithms to detect roadside phenomena (such as potholes, rough road, and slippery surfaces). These leverage multiple in-vehicle sensors to disambiguate these phenomena from other conflating factors. NORA reliably detects, on a cloud service, roadside events from multiple individual in-vehicle detections. To do this, it uses careful clustering techniques to assess the spatial scale of the event, and belief decay techniques to match event duration. It also employs aggressive fleetwide suppression of detections to minimize communication cost. Through a 50-vehicle deployment of NORA pilot over 9 months, it is shown that the results obtained from NORA in-vehicle detection methods match very well with ground truth measurements, and NORA cloud is effective at aggregating road events in an accurate and efficient fashion.

The digital revolution has brought ethical crossroads of technology and behavior, especially in the realm of sustainable cities. The need for a comprehensive and constructive ethical framework is emerging as digital platforms encounter trouble to articulate the transformations required to accomplish the sustainable development goal (SDG) 11 (on sustainable cities), and the remainder of the related SDGs. The unequal structure of the global system leads to dynamic and systemic problems, which have a more significant impact on those that are most vulnerable. Ethical frameworks based only on the individual level are no longer sufficient as they lack the necessary articulation to provide solutions to the new systemic challenges. A new ethical vision of digitalization must comprise the understanding of the scales and complex interconnections among SDGs and the ongoing socioeconomic and industrial revolutions. Many of the current social systems are internally fragile and very sensitive to external factors and threats, which lead to unethical situations. Furthermore, the multilayered net-like social tissue generates clusters of influence and leadership that prevent communities from a proper development. Digital technology has also had an impact at the individual level, posing several risks including a more homogeneous and predictable humankind. To preserve the core of humanity, we propose an ethical framework to empower individuals centered on the cities and interconnected with the socioeconomic ecosystem and the environment through the complex relationships of the SDGs. Only by combining human-centered and collectiveness-oriented digital development will it be possible to construct new social models and interactions that are ethical. Thus, it is necessary to combine ethical principles with the digital innovation undergoing in all the dimensions of sustainability.

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries.This paper advocates for establishing an interdisciplinary research domain—Collective Human‐Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human–AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance‐based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine intelligence. [ABSTRACT FROM AUTHOR]

Primate and human social groups exhibit a fractal structure that has a very limited range of preferred layer sizes, with groups of 5, 15, 50 and (in humans) 150 and 500 predominating. In non-human primates, this same fractal distribution is also observed in the distribution of species mean group sizes and in the internal network structure of their groups. Here we demonstrate that this preferential numbering arises because of the critical nature of dynamic self-organization within complex social networks. We calculate the size dependence of the scaling properties of complex social network models and argue that this aggregate behaviour exhibits a form of collective intelligence. Direct calculation establishes that the complexity of social networks as measured by their scaling behaviour is non-monotonic, peaking globally around 150 with a secondary peak at 500 and tertiary peaks at 5, 15 and 50. This provides a theory-based rationale for the fractal layering of primate and human social groups. [ABSTRACT FROM AUTHOR]

Background: In many dental settings, diagnosis and treatment planning is the responsibility of a single clinician, and this process is inevitably influenced by the clinician's own heuristics and biases. Our aim was to test whether collective intelligence increases the accuracy of individual diagnoses and treatment plans, and whether such systems have potential to improve patient outcomes in a dental setting. Methods: This pilot project was carried out to assess the feasibility of the protocol and appropriateness of the study design. We used a questionnaire survey and pre-post study design in which dental practitioners were involved in the diagnosis and treatment planning of two simulated cases. Participants were provided the opportunity to amend their original diagnosis/treatment decisions after viewing a consensus report made to simulate a collaborative setting. Results: Around half (55%, n = 17) of the respondents worked in group private practices, however most practitioners (74%, n = 23) did not collaborate when planning treatment. Overall, the average practitioners' self-confidence score in managing different dental disciplines was 7.22 (s.d. 2.20) on a 1–10 scale. Practitioners tended to change their mind after viewing the consensus response, particularly for the complex case compared to the simple case (61.5% vs 38.5%, respectively). Practitioners' confidence ratings were also significantly higher (p < 0.05) after viewing the consensus for complex case. Conclusion: Our pilot study shows that collective intelligence in the form of peers' opinion can lead to modifications in diagnosis and treatment planning by dentists. Our results lay the foundations for larger scale investigations on whether peer collaboration can improve diagnostic accuracy, treatment planning and, ultimately, oral health outcomes. [ABSTRACT FROM AUTHOR]

In recent years, we have experienced rapid development of advanced technology, machine learning, and artificial intelligence (AI), intended to interact with and augment the abilities of humans in practically every area of life. With the rapid growth of new capabilities, such as those enabled by generative AI (e.g., ChatGPT), AI is increasingly at the center of human communication and collaboration, resulting in a growing recognition of the need to understand how humans and AI can integrate their inputs in collaborative teams. However, there are many unanswered questions regarding how human–AI collective intelligence will emerge and what the barriers might be. Truly integrated collaboration between humans and intelligent agents may result in a different way of working that looks nothing like what we know now, and it is important to keep the essential goal of human societal well‐being and prosperity a priority. In this special issue, we begin to scope out the underpinnings of a socio‐cognitive architecture for Collective HUman‐MAchine INtelligence (COHUMAIN), which is the study of the capability of an integrated human and machine (i.e., intelligent technology) system to achieve goals in a wide range of environments. This topic consists of nine papers including a description of the conceptual foundation for a socio‐cognitive architecture for COHUMAIN, empirical tests of some aspects of this architecture, research on proposed representations of intelligent agents that can jointly interact with humans, empirical tests of human–human and human–machine interactions, and philosophical and ethical issues to consider as we develop these systems. [ABSTRACT FROM AUTHOR]

Error affects most human judgments and communications. Here we consider two types of error: unbiased noise and directional biases and consider their effects in the context of collective problem-solving. We studied an agent-based model of networked agents collectively searching for solutions to simple and complex problems on an NK landscape. We implemented superiority bias as a reluctance to adopt solutions used by others unless they were substantially better than one’s own solution. We implemented communication errors by injecting noise into solutions learned from others. These factors both reduce the short-term efficiency of social learning, as individuals are less likely to faithfully copy superior solutions. We find that when a team faces complex problems, both communication noise and superiority bias have a positive effect on the overall quality of the team’s collective solution, at the cost of increased time and resource usage. We find that when a team faces simple problems, a moderate level of communication noise leads to a decrease in the required time and resources for a team. We discuss these results in terms of tradeoffs between the quality of a collective solution and the time and resources needed to reach that solution. [ABSTRACT FROM AUTHOR]

This article develops functionalities for a digital collaborative platform, called the Digital Silver Hub, which aims to serve as an ecosystem for the quadruple helix actors (private sector, public sector, academic institutions, and senior citizens) to participate in knowledge exchange, collaboration, and co-creation of innovative technological solutions to facilitate the elderly population. In service of that, we have conducted 30 interviews from the partner heads of an EU-funded project as well as quadruple helix actors from each region in the Baltic Sea Region (Estonia, Latvia, Finland, Lithuania, Denmark, and St. Petersburg) to deeply understand the functionalities that are needed to be offered by the platform. A deductive thematic analysis has been conducted to analyse these functionalities in terms of collective intelligence (CI) components (namely staffing, processes, goals, and motivation) based on a most recent generic CI model. The functionalities were further evaluated by experts working in the field of science and technology in the silver economy. Overall, this article offers insights into the functionalities that are required for a digital collaborative platform to support the elderly population and facilitate co-creation among quadruple helix actors, as well as provides a foundation for future work on designing and implementing such a platform. [ABSTRACT FROM AUTHOR]

Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a "BOL" approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size. [ABSTRACT FROM AUTHOR]