Search

Cognitive differences can catalyze social learning through the process of one-to-one social influence. Yet the learning benefits of exposure to the ideas of cognitively dissimilar others often fail to materialize. Why do cognitive differences produce learning from interpersonal influence in some contexts but not in others? To answer this question, we distinguish between cognition that is expressed-one's public stance on an issue and the way in which supporting arguments are framed-and cognition that is latent-the semantic associations that underpin these expressions. We theorize that, when latent cognition is obscured, one is more likely to be influenced to change one's mind on an issue when exposed to the opposing ideas of cognitively dissimilar, rather than similar, others. When latent cognition is instead observable, a subtle similarity-attraction response tends to counteract the potency of cognitive differences-even when social identity cues and other categorical distinctions are inaccessible. To evaluate these ideas, we introduce a novel experimental paradigm in which participants (a) respond to a polarizing scenario; (b) view an opposing argument by another whose latent cognition is either similar to or different from their own and is either observable or obscured; and (c) have an opportunity to respond again to the scenario. A preregistered study (n = 1,000) finds support for our theory. A supplemental study (n = 200) suggests that the social influence of latent cognitive differences operates through the mechanism of argument novelty. We discuss implications of these findings for research on social influence, collective intelligence, and cognitive diversity in groups.

The dramatic expansion of internet communication tools has led to the increased use of temporary online groups to solve problems, provide services, or produce new knowledge. However, many of these groups need help to collaborate effectively. The rapid development of new tools and collaboration forms requires ongoing experimentation to develop and test new ways to support this novel form of teamwork. Building on research demonstrating the use of nudges to shape behavior, we report the results of an experiment to nudge teamwork in 168 temporary online groups randomly assigned to one of four different nudge treatments. Each nudge was designed to spur one of three targeted collaborative processes (collaborator skill use, effective task strategy, and the level of collective effort) demonstrated to enhance collective intelligence in extant research. Our results support the basic notion that digitally nudging collaborative processes can improve collective intelligence. However, to our surprise, a couple of nudges had unintended negative effects and ultimately decreased collective intelligence. We discuss our results using structured speculation to systematically consider the conditions under which we would or would not expect the same patterns to materialize in order to clearly articulate directions for future research. [ABSTRACT FROM AUTHOR]

The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM. [ABSTRACT FROM AUTHOR]

Urban Digital Twins (UDTs) have become the new buzzword for researchers, planners, policymakers, and industry experts when it comes to designing, planning, and managing sustainable and efficient cities. It encapsulates the last iteration of the technocratic and ultra-efficient, post-modernist vision of smart cities. However, while more applications branded as UDTs appear around the world, its conceptualization remains ambiguous. Beyond being technically prescriptive about what UDTs are, this article focuses on their aspects of interaction and operationalization in connection to people in cities, and how enhanced by metaverse ideas they can deepen societal divides by offering divergent urban experiences based on different stakeholder preferences. Therefore, firstly this article repositions the term UDTs by comparing existing concrete and located applications that have a focus on interaction and participation, including some that may be closer to the concept of UDT than is commonly assumed. Based on the components found separately in the different studied cases, it is possible to hypothesize about possible future, more advanced realizations of UDTs. This enables us to contrast their positive and negative societal impacts. While the development of new immersive interactive digital worlds can improve planning using collective knowledge for more inclusive and diverse cities, they pose significant risks not only the common ones regarding privacy, transparency, or fairness, but also social fragmentation based on urban digital multiplicities. The potential benefits and challenges of integrating this multiplicity of UDTs into participatory urban governance emphasize the need for human-centric approaches to promote socio-technical frameworks able to mitigate risks as social division. [ABSTRACT FROM AUTHOR]

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task—filled with conflicts—is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we show stable traffic simulations with up to 5000 agents, and experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a 250 m wide circular area. [ABSTRACT FROM AUTHOR]

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales. [ABSTRACT FROM AUTHOR]

The emergence of collective order in swarms from local, myopic interactions of their individual members is of interest to biology, sociology, psychology, computer science, robotics, physics and economics. Cooperative swarms, whose members unknowingly work towards a common goal, are particularly perplexing: members sometimes take individual actions that maximize collective utility, at the expense of their own. This seems to contradict expectations of individual rationality. Moreover, members choose these actions without knowing their effect on the collective utility. I examine this puzzle through game theory, machine learning and robots. I show that in some settings, the collective utility can be transformed into individual rewards that can be measured locally: when interacting, members individually choose actions that receive a reward based on how quickly the interaction was resolved, how much individual work time is gained and the approximate effect on others. This internally measurable reward is individually and independently maximized by learning. This results in a equilibrium, where the learned response of each individual maximizes both its individual reward and the collective utility, i.e. both the swarm and the individuals are rational. This article is part of the theme issue 'The road forward with swarm systems'. [ABSTRACT FROM AUTHOR]

Biological ensembles use collective intelligence to tackle challenges together, but suboptimal coordination can undermine the effectiveness of group cognition. Testing whether collective cognition exceeds that of the individual is often impractical since different organizational scales tend to face disjoint problems. One exception is the problem of navigating large loads through complex environments and toward a given target. People and ants stand out in their ability to efficiently perform this task not just individually but also as a group. This provides a rare opportunity to empirically compare problem-solving skills and cognitive traits across species and group sizes. Here, we challenge people and ants with the same "piano-movers" load maneuvering puzzle and show that while ants perform more efficiently in larger groups, the opposite is true for humans. We find that although individual ants cannot grasp the global nature of the puzzle, their collective motion translates into emergent cognitive skills. They encode short-term memory in their internally ordered state and this allows for enhanced group performance. People comprehend the puzzle in a way that allows them to explore a reduced search space and, on average, outperform ants. However, when communication is restricted, groups of people resort to the most obvious maneuvers to facilitate consensus. This is reminiscent of ant behavior, and negatively impacts their performance. Our results exemplify how simple minds can easily enjoy scalability while complex brains require extensive communication to cooperate efficiently. [ABSTRACT FROM AUTHOR]

This paper presents social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human moral values, and to provide ethics datasets and knowledge bases to help promote AI models "follow good advice as naturally as a stream follows its course". By creating a comprehensive and representative platform that accurately mirrors the moral judgments of diverse groups including humans and AIs, we hope to effectively portray cultural and group variations, and capture the dynamic evolution of moral judgments over time, which in turn will facilitate the Establishment, Evaluation, Embedding, Embodiment, Ensemble, and Evolvement (6Es) of the moral capabilities of AI models. Currently, STREAM has already furnished a comprehensive collection of ethical scenarios, and amassed substantial moral judgment data annotated by volunteers and various popular Large Language Models, collectively portraying the moral preferences and performances of both humans and AIs across a range of moral contexts. This paper will outline the current structure and construction of STREAM, explore its potential applications, and discuss its future prospects. [ABSTRACT FROM AUTHOR]

Embracing local knowledge is vital to conserve and manage biodiversity, yet frameworks to do so are lacking. We need to understand which, and how many knowledge holders are needed to ensure that management recommendations arising from local knowledge are not skewed towards the most vocal individuals. Here, we apply a Wisdom of Crowds framework to a data-poor recreational catch-and-release fishery, where individuals interact with natural resources in different ways. We aimed to test whether estimates of fishing quality from diverse groups (multiple ages and years of experience), were better than estimates provided by homogenous groups and whether thresholds exist for the number of individuals needed to capture estimates. We found that diversity matters; by using random subsampling combined with saturation principles, we determine that targeting 31% of the survey sample size captured 75% of unique responses. Estimates from small diverse subsets of this size outperformed most estimates from homogenous groups; sufficiently diverse small crowds are just as effective as large crowds in estimating ecological state. We advocate for more diverse knowledge holders in local knowledge research and application. [ABSTRACT FROM AUTHOR]

Background: Engaging people in advance care planning is a challenging systemic problem that requires a social innovation approach and a conceptual framework to guide behavioural and social change efforts. Aim: To identify stakeholders' perspectives on barriers to advance care planning engagement, options for overcoming these barriers, and user needs. The findings will inform the design of a health behaviour change intervention for engaging older adults (50+) in advance care planning. Design: To advance co‐production and intervention design goals, the study used collective intelligence and scenario‐based design methods. Methods: Following a systematic stakeholder analysis, 22 participants were recruited to three online collective intelligence sessions. The socioecological perspective informed framing of integrated findings and specifying factors at the individual, interpersonal, service, and system levels. Results: Identified barriers (n = 109) were grouped into seven categories: (i) Psychological, (ii) Advance Care Planning Literacy, (iii) Interpersonal and Interprofessional, (iv) Service‐Related, (v) Resources and Supports, (vi) Advance Care Planning Process and Methods, (vii) Cultural and Societal. Stakeholders generated 222 options for overcoming these barriers and specified 230 service user needs. The need to change perceptions of advance care planning, increase psychological readiness, and target advance care planning literacy was highlighted (individual‐level). Timely, focused, and meaningful interaction between the key ACP actors must be facilitated using creative strategies (interpersonal‐level). Need‐ and value‐based services, including high quality resources, support systems, and infrastructure, should be co‐designed (service‐level). Cultural and societal transformation is required (system‐level). Conclusion: Findings integration offered insight into the complexity of the design context and problem situation and identified directions for context‐specific advance care planning intervention development. The use of design thinking methodologies is recommended for the next phase of complex intervention development. Implications: The study presents a roadmap of actions required from policy‐makers, practitioners, and researchers to ensure the design of adequate advance care planning interventions. Reporting Method: Quality of reporting was assured by adherence to Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines (International Journal for Quality in Health Care, 19, 2007, 349). Patient or Public Contribution: Patient and public representatives participated in the collective intelligence sessions. Members of the All Ireland Institute of Hospice and Palliative Care Voices4Care facilitated that process. Findings from the first CI session (involving patients and caregivers) informed the content, format, and methods used in subsequent CI sessions. [ABSTRACT FROM AUTHOR]

Copyright of RED - Revista de Educación a Distancia is the property of Universidad de Murcia 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.)

We model an adaptive agent-based environment using selfish algorithm agents (SA-agents) that make decisions along three choice dimensions as they play the multi-round prisoner’s dilemma game. The dynamics that emerge from mutual interactions among the SA-agents exhibit two collective-level properties that mirror living systems, thus making these models suitable for societal/biological simulation. The properties are: emergent intelligence and collective agency. The former means there is observable intelligent behavior as a unitary collective entity. The latter means the collective exhibits observable adaptability that enables it to reorganize its network structure to meet its objectives in response to a changing environment. In this study, we generate these capabilities in a single, simple case. We do this first by letting a temporal complex network among SA-agents emerge and second by changing conditions in the ecosystem to test adaptability. This latter phase is done by introducing an artificial virus that infects SA-agents during interactions and can remove (or ‘kill’) the SA-agents. We then study the dynamics of the contagion within the collective as the virus spreads through the population and impacts collective reward-seeking performance. Specifically, we compare two strategies to control the spread of the virus: exogenous top-down control and endogenous bottom-up self-isolation strategies. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), as evidenced by POSCO's case study from South Korea. This study examines the effects of AI-enabled Video Interviews (AIV) in the early hiring stages compared to traditional CV Screening (CVS) on recruiting and subsequent work performance. The results indicate that AIV can complement the traditional method of CVS by identifying diverse candidates with superior collaborative and problem-solving abilities. This suggests that integrating AI could significantly improve talent acquisition by fostering Collaborative Intelligence between AI tools and HR professionals. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment analysis (ALSA) is used to identify the sentiment polarities of the given aspects in a sentence. Various approaches have been proposed to improve the performance of ALSA, most recently graph convolutional networks (GCNs). Although GCN-based ALSA methods have obtained the promised results, how to effectively and simultaneously harness the semantic, syntactic structure information from the dependency tree and the contextual affective knowledge regarding the specific aspect remains a challenging research question. This research proposes a novel sentiment analysis method applied at the aspect level, called multigraph convolutional network (MulGCN), by integrating three GCNs. Unlike previous GCNs, the MulGCN model can simultaneously capture features related to three knowledge: syntax, semantics, and context by combining the dependency parser tree, affective information in SenticNet, and inter-aspect-aware technique. The research starts with a comprehensive survey of articles related to ALSA methods based on GCN to evaluate and unify the approach, thereby identifying the point where GCN has not been adequately used in ALSA methods to have a basis for proposing appropriate improvements to improve performance. Next, three knowledge-based GCNs are built to represent and extract high-level features related to syntax, semantics, and context. Then, the fusion mechanism is used to integrate the extracted features. Finally, these features are fed into a classifier consisting of convolutional layers to determine the sentiment polarity of the aspects. The MulGCN model will be experimented on three benchmark datasets. The experimental results prove the effectiveness of MulGCN model for improving the performance of ALSA including the accuracy and the $F_{1}$ score.

Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models-including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.

Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert’s knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds.

Living in groups offers social animals the significant advantage of accessing collective wisdom and enhanced information processing, enabling more accurate decisions related to foraging, navigation and habitat selection. Preserving group membership is crucial for sustaining access to collective wisdom, incentivizing animals to prioritize group cohesion. However, when individuals encounter divergent information about the quality of various options, this can create a conflict between pursuing immediate rewards and the maintenance of group membership to improve access to future pay-offs. In this study, I show that rational agents who seek to maximize long-term rewards will be more inclined to follow the decisions of their peers than those with short-term horizons. In doing so, they necessarily make less-rewarding decisions in the short-term, which manifests in a lower individual accuracy when choosing the better of two options. Furthermore, I demonstrate that intuitions about collective wisdom can be misleading in groups of agents who prioritize long-term rewards, with disagreement being a better signal for the accuracy of collective choices than consensus. These results demonstrate that observed patterns of sociality should be interpreted in the context of the life history of an individual and its peers, rather than through the lens of an isolated decision. This article is part of the discussion meeting issue 'Understanding age and society using natural populations'. [ABSTRACT FROM AUTHOR]

Currently, there are increasing attempts to better involve citizens in political decision processes. A successful approach in that regard has been participatory budgeting (PB), which allows citizens to propose projects and then decide how to distribute a given budget over them. Meanwhile, the literature on collective intelligence (CI) has also shown the ability of groups to solve complex problems. Thus, by combining CI and PB, it should be possible for citizens to identify problems and create their own solutions. In this article, we study this possibility by using agent-based models. Specifically, we first show that a system combining CI and PB produces solutions that strongly penalize minorities if the solution quality depends on group size. Then, we introduce an approach that can overcome this issue. Indeed, by using a common knowledge base for the storage of partial solutions, the quality of solutions of minorities can benefit from the work of the majority, thereby promoting fairness. Interestingly, this approach also benefits majorities, as the quality of their solutions is further improved by the work of the minorities, thus reaching better solutions for everyone. This stresses the potential and importance of an open innovation approach, which is committed to information sharing. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

This article investigates into Orientalism in Disney’s Caro, Niki, dir. 2020. Mulan. Orlando, FL: The Walt Disney Company. and its resistant reading on Chinese social media platform Douban. It adopts a qualitative method to discipline data collected from Douban, then analyzing them in light of Stuart Hall’s articulation theory and Henry Jenkins’ ‘collective intelligence’. The analysis first reveals two ‘articulated’ Orientalist discourses in the film undergirded by a form of ‘new racism’, which serves to perpetuate the existing racial and gender hierarchies in the West–East power matrix. Then it shows how Douban users’ collective intelligence can potentially ‘disarticulate’ and ‘rearticulate’ Orientalism through a kind of microphysical, collective rewriting. The research argues that both disarticulation and rearticulation are a means by which Douban users indulge the civic imagination for alternative geopolitics in popular culture representation; compared with disarticulation, their rearticulatory practice is less effectual due to the inclusion of self-conflicting discursive elements, which ironically lay bare traces of their own discursive formation. This irony, this article suggests, calls into question the dominant-resistant binary paradigm that predominates current media reception studies. The article concludes by rethinking Orientalism and its Douban resistance in light of ‘Chineseness as identity work’, which deconstructs the oppositional paradigm and enlightens Orientalist film studies in global contexts. [ABSTRACT FROM AUTHOR]

ABSTRACT The dominant paradigm in biomedicine focuses on genetically‐specified components of cells and their biochemical dynamics, emphasizing bottom‐up emergence of complexity. Here, I explore the biomedical implications of a complementary emerging field: diverse intelligence. Using tools from behavioral science and multiscale neuroscience, we can study development, regenerative repair, and cancer suppression as behaviors of a collective intelligence of cells navigating the spaces of possible morphologies and transcriptional and physiological states. A focus on the competencies of living material—from molecular to organismal scales—reveals a new landscape for interventions. Such top‐down approaches take advantage of the memories and homeodynamic goal‐seeking behavior of cells and tissues, offering the same massive advantages in biomedicine and bioengineering that reprogrammable hardware has provided information technologies. The bioelectric networks that bind individual cells toward large‐scale anatomical goals are an especially tractable interface to organ‐level plasticity, and tools to modulate them already exist. This suggests a research program to understand and tame the software of life for therapeutic gain by understanding the many examples of basal cognition that operate throughout living bodies. [ABSTRACT FROM AUTHOR]

We study the emergence of agency from scratch by using Large Language Model (LLM)-based agents. In previous studies of LLM-based agents, each agent's characteristics, including personality and memory, have traditionally been predefined. We focused on how individuality, such as behavior, personality, and memory, can be differentiated from an undifferentiated state. The present LLM agents engage in cooperative communication within a group simulation, exchanging context-based messages in natural language. By analyzing this multi-agent simulation, we report valuable new insights into how social norms, cooperation, and personality traits can emerge spontaneously. This paper demonstrates that autonomously interacting LLM-powered agents generate hallucinations and hashtags to sustain communication, which, in turn, increases the diversity of words within their interactions. Each agent's emotions shift through communication, and as they form communities, the personalities of the agents emerge and evolve accordingly. This computational modeling approach and its findings will provide a new method for analyzing collective artificial intelligence. [ABSTRACT FROM AUTHOR]

The study of collective intelligence has focused in the last years on crowdsourcing and artificial swarm intelligence. Currently, large online communities have demonstrated their effectiveness but even if the contributions in this domain are significant, it remains essential to question the functioning of collective intelligence in small groups, especially since the gain in popularity of brainstorming strategies, focus groups and co-working practices. In this context, we conducted a qualitative systematic review using Prospero, PRISMA protocol and bias assessment to identify the factors currently recognised as impacting on the emergence of collective intelligence in small groups. These factors were then organised according to the different levels of abstraction observed in research about collective intelligence. From this work, collective intelligence appears as the crystallization of emerging properties that manifest themselves in interactions and whose possibility of existing is intrinsically linked to meta-cognition and meta-communication processes. [ABSTRACT FROM AUTHOR]

Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert's knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds. [ABSTRACT FROM AUTHOR]

Background: Hypertension remains one of the most important modifiable risk factors for stroke and heart disease. Anti-hypertensive medications are effective, but are often not used to maximum benefit. Sub-optimal dosing by prescribers and challenges with medication-taking for patients remain barriers to effective blood pressure control. Objectives: We aimed to systematically develop a theory-based complex intervention to support General Practitioners (GPs) and people with hypertension to maximise medication use to control blood pressure. Methods: We used the three-phase Behaviour Change Wheel (BCW) as the overarching intervention development framework. Collective Intelligence methodology was used to operationalise the stakeholder input to Phases 2 and 3 of the BCW. This took the form of a Collective Intelligence workshop with 19 stakeholders from diverse backgrounds including lived experience, general practice, nursing, pharmacy and health psychology. Techniques such as barrier identification, idea-writing and scenario-based design were used to generate possible intervention options. Intervention options were then selected and refined using the Acceptability, Practicability, Effectiveness, Affordability, Side-effects and Equity (APEASE) criteria and guidance from the MIAMI Public and Patient Involvement Panel. Results: The finalised MIAMI intervention consists of both GP and patient supports. GP supports include a 30-minute online training, information booklet and consultation guide (drop-down menu) embedded within the patient electronic health system. Patient supports include a pre-consultation plan, website, and a structured GP consultation with results from an Ambulatory Blood Pressure Monitor and urine chemical adherence test. The intervention components have been mapped to the intervention functions of the BCW and Behaviour Change Technique Ontology. Conclusion: Collective Intelligence offered a novel method to operationalise stakeholder input to Phases 2 and 3 of the BCW. The MIAMI intervention is now at pilot evaluation stage. [ABSTRACT FROM AUTHOR]

This article explores the intersection between cognition theories and urban planning, conceptualizing the city as a distributed socio-cognitive architecture. It traces the evolution of these theories through three waves—functionalism, social externalism, and radical enactivism —. Correspondingly, the article suggests implications for reorienting urban planning approaches, highlighting participatory design, collaborative placemaking, and the nurturing of place-based affordances. Drawing examples from existing planning literature, it demonstrates resonances with Extended Mind-informed orientations. The conclusion synthesizes these insights, proposing a potentially transformative framework by rethinking planning as more participatory, pluralistic, and cognitively integrative, challenging internalist and technocratic assumptions. [ABSTRACT FROM AUTHOR]

AbstractAdolescents are vulnerable to the exponential increase in fake news spread. Collective judgment on the quality of news has been an effective intervention. Collective Learning platform makes it possible to apply collective intelligence through simultaneous interaction in a large group, similar to social networks. The objective of this article is to analyze whether this platform effectively improves how adolescents confront fake news. 360 Spanish students aged 15–16 years old participated in the experiment confronting a fictitious case of fake news. The quantitative analysis using ANOVA demonstrated that there were significant positive changes for addressing fake news, both overall as well as in each of the participants. We discuss how these results confirm that collective intelligence improves digital literacy in the Internet challenges. [ABSTRACT FROM AUTHOR]

Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions; nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task. [ABSTRACT FROM AUTHOR]

The wisdom of the crowd is the finding that aggregating the judgments of many people can lead to surprisingly accurate group judgments. Usually statistical methods are used to aggregate people's judgments, but there are advantages to using cognitive models instead. Crowd judgments based on cognitive modeling can (a) identify experts and amplify their judgments, (b) provide a representational structure for aggregating complicated multidimensional judgments, (c) debias judgments that are affected by heuristic cognitive processes or competitive social situations, and (d) diversify the crowd by incorporating predictions about judgments that have not been observed. Demonstrations of these advantages are provided in case studies involving ranking, probability estimation, and categorization problems. [ABSTRACT FROM AUTHOR]

The fitness of an organism can be strongly affected by the decisions that it makes throughout its lifetime. These decisions may be spatial (deciding where to go), temporal (deciding when to perform an action), or a mixture thereof. How organisms make spatial or temporal decisions should involve different mechanisms because of fundamental differences between the two. For example, time is irreversible, while animals can traverse space more freely. Making decisions together as a group can improve the accuracy of decisions (a form of collective intelligence). However, to date, almost all existing research has been on collective spatial decisions and, as a result, it is through this spatial lens that our intuition of collective decisions has developed. Understanding how individuals in groups make timing decisions is particularly relevant in a changing climate, where both the optimal time to perform actions and the cues used to time the action are changing. Studying collective intelligence in the context of timing decisions will reveal novel mechanisms that social animals across taxa (including humans) use, allowing us to predict the future of species in a changing world and to design new bio-inspired strategies. The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed–accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups. [ABSTRACT FROM AUTHOR]

This research examines the creation of knowledge on social networks about blood donation, the role of virtual communities and their potential to attract donors. We applied a two-step methodology, starting by interviews with blood donation management institutions in Spain (N=21) and concluding with the analysis of comments from 61 donor testimonials on TikTok (N=1,606). This social network was chosen because of the scarce institutional presence and the predominance of Generation Z, which is necessary for generational change. The results show that blood donation in Spain is seen from a positive perspective, where complaints are rare or relativised and the community encourages the behaviour imitation thanks to the feeling of pride and gratitude to donors. Knowledge is generated both from the contributions of creators and audiences and is focused on informing about the process, conditions and consequences, supporting its voluntary and altruistic nature. Overall, social, group and collaborative learning takes place, with greater success in those contents shared by health profiles but supported by all users. We conclude that TikTok plays a relevant role in the literacy and awareness of blood donation based on the virtual community collective intelligence, which will affect the donor relay thanks to the behavioural models that obtain social recognition. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power is the property of Electric Power Editorial Office 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.)

Previous efforts to support creative problem-solving have included (a) techniques such as brainstorming and design thinking to stimulate creative ideas, and (b) software tools to record and share these ideas. Now, generative AI technologies can suggest new ideas that might never have occurred to the users, and users can then select from these ideas or use them to stimulate even more ideas. To explore these possibilities, we developed a system called Supermind Ideator that uses a large language model (LLM) and adds prompts, fine tuning, and a specialized user interface in order to help users reformulate their problem statements and generate possible solutions. This provides scaffolding to guide users through a set of creative problem-solving techniques, including some techniques specifically intended to help generate innovative ideas about designing groups of people and/or computers ("superminds"). In an experimental study, we found that people using Supermind Ideator generated significantly more innovative ideas than those generated by people using ChatGPT or people working alone. Thus our results suggest that the benefits of using LLMs for creative problem-solving can be substantially enhanced by scaffolding designed specifically for this purpose. [ABSTRACT FROM AUTHOR]

BACKGROUND: This paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to support understanding and primary use of complex national data but also to increase the dissemination and secondary impact of research based on these data. The case study uses the Northern Ireland Longitudinal Study (NILS), a member of the UK family of census/administrative data longitudinal studies (UKLS). METHODS: A stakeholder-engaged CI approach was applied to inform the transformation of the NILS Research Support Unit (RSU) infrastructure to support researchers in their use of government data, including collaborative decision-making and better dissemination of research outputs. RESULTS: We provide an overview of NILS RSU infrastructure design changes that have been implemented to date, focusing on a website redesign to meet user information requirements and the formation of better working partnerships between data users and providers within the Northern Ireland data landscape. We also discuss the key challenges faced by the design team during this project of transformation. CONCLUSION: Our primary objective to improve government data infrastructure and to increase dissemination and the impact of research based on data was a complex and multifaceted challenge due to the number of stakeholders involved and their often conflicting perspectives. Results from this CI approach have been pivotal in highlighting how NILS RSU can work collaboratively with users to maximize the potential of this data, in terms of forming multidisciplinary networks to ensure the research is utilized in policy and in the literature and providing academic support and resources to attract new researchers.

Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity and agent behaviour as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a groups ability to solve a difficult exploration task. We explore how size, connectivity and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, that population size has a positive effect on innovation, but also find that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation in the task; and that the highest performing agents tend to occupy more central positions in the network. Moreover, we show that every network factor which improves innovation leads to a proportional increase in inequality of performance in the network, creating genius effects among otherwise dumb agents in both idealized and real-world networks.

Collective intelligence challenges are often entangled with collective action problems. For example, voting, rating, and social innovation are collective intelligence tasks that require costly individual contributions. As a result, members of a group often free ride on the information contributed by intrinsically motivated people. Are intrinsically motivated agents the best participants in collective decisions? We embedded a collective intelligence task in a large-scale, virtual world public good game and found that participants who joined the information system but were reluctant to contribute to the public good (free riders) provided more accurate evaluations, whereas participants who rated frequently underperformed. Testing the underlying mechanism revealed that a negative rating bias in free riders is associated with higher accuracy. Importantly, incentivizing evaluations amplifies the relative influence of participants who tend to free ride without altering the (higher) quality of their evaluations, thereby improving collective intelligence. These results suggest that many of the currently available information systems, which strongly select for intrinsically motivated participants, underperform and that collective intelligence can benefit from incentivizing free riding members to engage. More generally, enhancing the diversity of contributor motivations can improve collective intelligence in settings that are entangled with collective action problems.

Data for Policy (dataforpolicy.org), a global community, focuses on policy–data interactions by exploring how data can be used for policy in an ethical, responsible, and efficient manner. Within its journal, six focus areas, including Data for Policy Area 1: Digital & Data-driven Transformations in Governance, were established to delineate the evolving research landscape from the Data for Policy Conference series. This review addresses the absence of a formal conceptualization of digital and data-driven transformations in governance within this focus area. The paper achieves this by providing a working definition, mapping current research trends, and proposing a future research agenda centered on three core transformations: (1) public participation and collective intelligence; (2) relationships and organizations; and (3) open data and government. The paper outlines research questions and connects these transformations to related areas such as artificial intelligence (AI), sustainable smart cities, digital divide, data governance, co-production, and service quality. This contribution forms the foundational development of a research agenda for academics and practitioners engaged in or impacted by digital and data-driven transformations in policy and governance.

Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information-sharing networks among clinicians to improve clinicians diagnostic accuracy and treatment decisions. We use a pool of 2,941 practicing clinicians recruited from around the United States to conduct 84 independent group-level trials, ranging across seven different clinical vignettes for topics known to exhibit high rates of diagnostic or treatment error (e.g., acute cardiac events, geriatric care, low back pain, and diabetes-related cardiovascular illness prevention). We compare collective performance in structured information-sharing networks to collective performance in independent control groups, and find that networks significantly reduce clinical errors, and improve treatment recommendations, as compared to control groups of independent clinicians engaged in isolated reflection. Our results show that these improvements are not a result of simple regression to the group mean. Instead, we find that within structured information-sharing networks, the worst clinicians improved significantly while the best clinicians did not decrease in quality. These findings offer implications for the use of social network technologies to reduce errors among clinicians.

The rise of local and global challenges (such as COVID-19, wars, natural disasters, etc.) requires advanced communication and information technologies to support economic development. The study aims to form a theoretical basis and practical tools for creating and functioning of wiki communities. Wiki communities are a new form of social association based on Internet communications of socio-economic subjects (individuals and organizations), in which each participant has equal rights to receive information, exchange opinions, and generate solutions. The theoretical basis involves substantiating the key principles on which wiki communities are formed, e.g., decentralization, openness, peering, sharing, and mass nature of activity. Wiki communities are represented by a set of specific types, such as professional, academic and research, custom, creative, public and non-commercial communities. The specific managing activities of wiki communities are described by several classification levels, such as operational activities, ensuring security, quality assurance, and motivation. The wiki community management toolkit includes a goal-setting algorithm, decision-making procedures, communications, rules of operation, typical tasks, areas of application, the operation and development cycle, and functional capabilities. It allows effective transfer of information, communication in real-time, and mutually enriching each other in forming knowledge and innovation. AcknowledgmentThe publication was prepared in the framework of the research project “Restructuring of the national economy in the direction of digital transformations for sustainable development” (№0122U001232) from National Research Foundation.

Human-computer interaction as a coordinating element between human and machine is used in many different ways. Due to their digital processes, countless industries are dependent on an effective intermeshing of humans and machines. This often involves preparatory work or sub-processes being carried out by machines, which humans initiate, take up, continue, finalise or check. Tasks are broken down into sub-steps and completed by humans or machines. Aggregated cooperation conceals the numerous challenges of hybrid cooperation in which communication and coordination must be mastered in favour of joint decision-making. However, research into human-computer interaction can also be thought of differently than a mere aggregation of humans and machines. We want to propose a nature-inspired possibility that has been successfully practising the complex challenges of joint decision-making as proof of successful communication and coordination for millions of years. Collective intelligence and the processes of self-organisation offer biomimetic concepts that can be used to rethink socio-technical systems as a symbiosis in the form of a human-computer organism. For example, the effects of self-organisation such as emergence could be used to exceed the result of an aggregation of humans and machines as a future social anthropology 4.0 many times over.

Industry 5.0 and Healthcare 5.0 converge towards a human centered society, having technological advancement as a lever. In Society 5.0, decentralized autonomous cities and a convergence of physical and cyberspace are the foundations of the new chapter of society’s development. The idea of creating digital replicas and legitimate representatives of human beings in cyberspace has become a pillar of digitalization. Society 5.0 introduces Human Digital Twins as a central element of Cyber Physical Systems that include human factors or are designed to interact with humans in a personalized fashion. Overall, the HDT and neighboring concepts are applied to depict how humans can be represented in a cyberspace. However, there are clear challenges in determining which human characteristics should take precedence, how much autonomy should be granted to HDTs to optimize their functionality and how to conceptualize the digital environment in which HDTs interact with various entities, including other digital agents and stakeholders with agency and decisional power. To harness similarities and differences of current approaches, we propose a classification of HDTs based on meta-characteristics and ethical implications. We discuss ethical implication by focusing on emerging risks and paradigm shifts and anchor the previous discussion in the vision for Society 5.0, questioning whether societal development relying on disruptive technologies, instead of leading to more human-centered societies might be driving humanized societies away from humanity. [ABSTRACT FROM AUTHOR]