Search

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]

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]

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]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature 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.)

The intricate relationship between structure and function spans various disciplines, from biology to management, offering insights into predicting interesting features of complex systems. This interplay is evident in online forums, where the organization of the threads interacts with the message's meaning. Assessing readability in these discussions is vital for ensuring information comprehension among diverse audiences. This assessment is challenging due to the complexity of natural language compounded by the social and temporal dynamics within social networks. One practical approach involves aggregating multiple readability metrics as a consensus alignment. In this study, we explore whether the structural complexity of online discussions can predict consensus readability without delving into the semantics of the messages. We propose a consensus readability metric derived from well-known readability tests and a complexity metric applied to the tree structures of Reddit discussions. Our findings indicate that this proposed metric effectively predicts consensus readability based on the complexity of discourse structure. [ABSTRACT FROM AUTHOR]

The peer economy, such as crowdfunding, democratizes access to tasks available only to professionals. Although the peer economy has gained great popularity in practice, how crowds infer information from their peers, especially from experts, is still under minimal study in academia. Using data from a debt-based crowdfunding platform in China, this study investigates the impact of seasoned predecessors' bids on subsequent investors' decisions and how seasoned and unseasoned investors respond differently to herding signals. We discover that the cumulative lending amount from seasoned predecessors is positively associated with the lending amount of a successor, and such an association is greater if the successor is seasoned. In the repayment process, we find that the lending amount from seasoned investors is positively associated with loan performance, while the lending amount from unseasoned investors is not. Our results contribute to the literature on crowds of wisdom, implying that in a context that requires sophisticated knowledge, extracting hidden talents from experts rather than from crowds is more appropriate. [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]

Even with all the progress that humankind has achieved in the twenty-first century, the challenge of better knowledge available from science to address our most relevant problems remains. In this paper, we report on a few outstanding references built by the future vision of the great inspiring mind of Professor Sérgio Mascarenhas Oliveira. This special edition of the Brazilian Journal of Physics, “Sergio Mascarenhas−a Polymath in Physics,” reports most of his contributions. We focus on his invariant attitude of choosing to understand simple facts based on the best knowledge, going beyond the traditional approaches. For him, the subject did not matter. He systematically sought the knowledge frontier available, contacting researchers of different domains, sharing and exchanging knowledge to satisfy his curious and empathic mind. To illustrate his impact on society, one ought to consider his scientific heritage as over two thousand intellectual descendants that help us to understand his legacy. We conclude this report by describing the newborn Institute of Advanced and Strategic Studies, based on UFSCar, that aims to cultivate a world connecting and sharing the best knowledge available, which are values that had guided our Polymath until his last days. [ABSTRACT FROM AUTHOR]

Online discussion platforms are perceived as the next-generation method of citizen involvement. Such platforms can collect, integrate, and synthesize opinions to achieve social good. Crowd-scale platforms are being developed and deployed in social experiments that involve citizens and local governments. In such platforms, human facilitation is often used to preserve the quality of the discussions. Human facilitators often face difficulties when the discussions grow in size. In this paper, we present "D-agree," a crowd-scale discussion support system based on an automated facilitation agent. The agent extracts discussion structures from online discussions, analyzes them, and posts facilitation messages. We conducted small- and large-scale social experiments in Japan to assess the social impact of the platform. The results showcase the success of our automated facilitation agents in gathering valuable opinions from citizens. In addition, our experiments highlight the effect of an automated facilitation agent on online discussions. In particular, we find that combining the agent facilitator with human facilitators leads to higher user satisfaction. [ABSTRACT FROM AUTHOR]

Copyright of Gruppe. Interaktion. Organisation: Zeitschrift für Angewandte Organisationspsychologie (GIO) is the property of Springer Nature 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.)

In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design. [ABSTRACT FROM AUTHOR]

Copyright of Organisationsberatung, Supervision, Coaching is the property of Springer Nature 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.)

Collective intelligence (CI) is said to manifest in a group's domain general mental ability. It can be measured across a battery of group IQ tests and statistically reduced to a latent factor called the "c-factor." Advocates have found the c-factor predicts group performance better than individual IQ. We test this claim by meta-analyzing correlations between the c-factor and nine group performance criterion tasks generated by eight independent samples (N = 857 groups). Results indicated a moderate correlation, r, of.26 (95% CI.10,.40). All but four studies comprising five independent samples (N = 366 groups) failed to control for the intelligence of individual members using individual IQ scores or their statistically reduced equivalent (i.e., the g-factor). A meta-analysis of this subset of studies found the average IQ of the groups' members had little to no correlation with group performance (r =.06, 95% CI −.08,.20). Around 80% of studies did not have enough statistical power to reliably detect correlations between the primary predictor variables and the criterion tasks. Though some of our findings are consistent with claims that a general factor of group performance may exist and relate positively to group performance, limitations suggest alternative explanations cannot be dismissed. We caution against prematurely embracing notions of the c-factor unless it can be independently and robustly replicated and demonstrated to be incrementally valid beyond the g-factor in group performance contexts. [ABSTRACT FROM AUTHOR]

Within current debates about the future impact of Artificial Intelligence (AI) on human society, roughly three different perspectives can be recognised: (1) the technology-centric perspective, claiming that AI will soon outperform humankind in all areas, and that the primary threat for humankind is superintelligence; (2) the human-centric perspective, claiming that humans will always remain superior to AI when it comes to social and societal aspects, and that the main threat of AI is that humankind's social nature is overlooked in technological designs; and (3) the collective intelligence-centric perspective, claiming that true intelligence lies in the collective of intelligent agents, both human and artificial, and that the main threat for humankind is that technological designs create problems at the collective, systemic level that are hard to oversee and control. The current paper offers the following contributions: (a) a clear description for each of the three perspectives, along with their history and background; (b) an analysis and interpretation of current applications of AI in human society according to each of the three perspectives, thereby disentangling miscommunication in the debate concerning threats of AI; and (c) a new integrated and comprehensive research design framework that addresses all aspects of the above three perspectives, and includes principles that support developers to reflect and anticipate upon potential effects of AI in society. [ABSTRACT FROM AUTHOR]

We are living in the 'plasticene' era. Marine plastic pollution is a global problem, and increasing effort and funds are being invested towards identifying and implementing solutions. It is well accepted that the implemented solutions should take into account local specificities and culture, which requires the involvement and engagement of local decision-makers and key actors. This paper presents how the DeCyDe-4 method, which is based on a participatory decision-support concept, was implemented to develop community-based policy tools and action plans to combat marine litter. Through a presentation of the step-by-step implementation of the method at a 3-day workshop in the Paphos District of Cyprus, the DeCyDe-4 method is unfolded so as to showcase how a multi-disciplinary group of local stakeholders can co-create a Policy Tool, and identify and prioritize measures and solutions to marine litter. The case study shows that DeCyDe-4 can support a varied group of stakeholders to negotiate a complex environmental problem such as marine litter, and to make informed, unbiased decisions, through a process that encourages active participation and dialogue, so that opposite opinions are heard, consensus is reached and ownership of the solutions is achieved. [ABSTRACT FROM AUTHOR]

The current study aims to investigate whether backers' electronic word-of-mouth (eWOM) is a valuable input when it comes to generating collective intelligence for projects filtering in crowdfunding. Based on theories of collective intelligence and WOM, we developed a research model and conducted an empirical study using a cross-section dataset with 845 crowdfunding projects and a panel dataset of the funding dynamics of those projects. The results indicated that backers' negative WOM rather than positive WOM can effectively predict project implementation performance. Besides, for entrepreneurs with a high reputation, negative WOM has stronger predictive power for the complaints of product quality. We also found that projects with more accumulated negative WOM tend to attract fewer subsequent backers. Compared with positive WOM, negative WOM has a stronger effect on subsequent investments in a crowdfunding project. Finally, positive WOM can reduce the diffusion of responsibility effect in crowdfunding, that is, a backer is less likely to take responsibility for investing in a project when others have already backed it. Based on these findings, we conclude that backers' WOM, especially the negative WOM, contributes to projects filtering in crowdfunding. [ABSTRACT FROM AUTHOR]

The preference values in group decision-making (GDM) process can differ significantly between different experts, which may yield a low level of group consensus. Therefore, different consensus models have been developed for the modification of preference values to assist experts in improving their consensus degrees. However, most consensus models do not consider collective intelligence (CI) that may decrease as the consensus degree increases under certain circumstances. From the perspective of CI, the distrust relationship allows the group to better explore the decision space, rather than prematurely converge on an agreed suboptimal solution. Inspired by this idea, a theoretical framework of solving intuitionistic fuzzy GDM problems with low group consensus is proposed in this paper, which mainly includes two steps: (1) building the trust/distrust relationships and (2) establishing a consensus model. For two experts with a direct relationship, the trust/distrust relationships between them are constructed by fusing their knowledge levels and representativeness levels. For two experts with an indirect relationship, a new operator is designed to construct the trust/distrust relationships between them, which can describe the information attenuation of the decreasing trust along with the increasing distrust. Additionally, a consensus model based on the social network relationships density and trust/distrust relationships is proposed, which improves consensus degree and CI level conducively. Finally, a ranking of alternatives is constructed to select the optimal alternative. An illustrative example is used to demonstrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Even though both collective intelligence and groupthink promote concurrence seeking, studies focused on the relationship between the two are lacking. Therefore, this study aims to explore how to transform groupthink into collective intelligence that contributes to knowledge creation. Three "switching factors"— "knowledge conflict," "reconsideration," and "organizational memory"—are defined herein. The effects of each factor and combinations of factors were tested through an agent-based model (ABM) and efficiency analysis. The ABM simulation showed that "knowledge conflict" is good for organizational performance but reduces the scope for new knowledge. On the contrary, a model with "reconsideration" or "organizational memory" was able to hold the heterogeneity of knowledge. With respect to efficiency, eight possible strategies were tested by stochastic frontier analysis. Its results showed that the combination of "knowledge conflict" and "reconsideration" had the highest efficiency with respect to both "between-group" and "meta-frontier." Thus, this study suggests that the switching factors and strategies using their combinations can help in ensuring decentralization and diversity in organizations, and ultimately, contribute to new knowledge creation—specifically, the combination of "knowledge conflict" and "reconsideration" is the most efficient strategy. This study contributes to the possible existence of switching factors and, using them, builds an optimal strategy in the practical field of knowledge management. [ABSTRACT FROM AUTHOR]

When a fingerprint is located at a crime scene, a human examiner is counted upon to manually compare this print to those stored in a database. Several experiments have now shown that these professional analysts are highly accurate, but not infallible, much like other fields that involve high-stakes decision-making. One method to offset mistakes in these safety-critical domains is to distribute these important decisions to groups of raters who independently assess the same information. This redundancy in the system allows it to continue operating effectively even in the face of rare and random errors. Here, we extend this "wisdom of crowds" approach to fingerprint analysis by comparing the performance of individuals to crowds of professional analysts. We replicate the previous findings that individual experts greatly outperform individual novices, particularly in their false-positive rate, but they do make mistakes. When we pool the decisions of small groups of experts by selecting the decision of the majority, however, their false-positive rate decreases by up to 8% and their false-negative rate decreases by up to 12%. Pooling the decisions of novices results in a similar drop in false negatives, but increases their false-positive rate by up to 11%. Aggregating people's judgements by selecting the majority decision performs better than selecting the decision of the most confident or the most experienced rater. Our results show that combining independent judgements from small groups of fingerprint analysts can improve their performance and prevent these mistakes from entering courts. [ABSTRACT FROM AUTHOR]

This paper examines relationships between learning and technological change and argues that we urgently need new ways to approach what it means to learn in the context of a global Fourth Industrial Revolution. It briefly introduces the postdigital perspective, which considers the digital 'revolution' as something that has already happened and focuses to its reconfiguration. It claims that what we access, how we access it, what we do with it, and who then accesses what we have done, are important elements of a postdigital world worthy of closer examination. Focusing to recent debates about postdigital collective intelligence, we develop the concept of postdigital we-learn by showing that it might help us, amongst other things, to counter the idea of a lone human accessing education primarily for future individual, economic profit, as prescribed by the neoliberal learning economy. Building on new schools of thought emerging in response to the expansion of non-human (algorithmic) agency, we refine the concept of postdigital we-learn as a gathering between humans and machines. The consequences of this gathering are uncomfortable, as they imply unlearning elements of both capitalism and critical pedagogy. However, such unlearning is inherent to 'a critical pedagogy of becoming' and positions postdigital we-learn as a suitable framework for understanding and development of emancipatory, critical learning in our postdigital reality. [ABSTRACT FROM AUTHOR]

The method of supergrading is introduced for deriving a ranking of items from scores or grades awarded by several people. Individual inputs may come in different languages of grades. Diversity in grading standards is an advantage, enabling rankings derived by this method to separate more items from one another. A framework is introduced for studying grading on the basis of observations. Measures of accuracy, reliability and discrimination are developed within this framework. Ability in grading is characterized for individuals and groups as the capacity to grade reliably, accurately and at a high level of discrimination. It is shown that the collective ability of a supergrading group with diverse standards can be greater than that of a less diverse group whose members have greater ability. [ABSTRACT FROM AUTHOR]

Coordinating multiple unmanned aerial vehicles (multi-UAVs) is a challenging technique in highly dynamic and sophisticated environments. Based on digital pheromones as well as current mainstream unmanned system controlling algorithms, we propose a strategy for multi-UAVs to acquire targets with limited prior knowledge. In particular, we put forward a more reasonable and effective pheromone update mechanism, by improving digital pheromone fusion algorithms for different semantic pheromones and planning individuals' probabilistic behavioral decision-making schemes. Also, inspired by the flocking model in nature, considering the limitations of some individuals in perception and communication, we design a navigation algorithm model on top of Olfati-Saber's algorithm for flocking control, by further replacing the pheromone scalar to a vector. Simulation results show that the proposed algorithm can yield superior performance in terms of coverage, detection and revisit efficiency, and the capability of obstacle avoidance. [ABSTRACT FROM AUTHOR]

In conventional differential evolutionary (DE) algorithm, mutation operator has significant influence on generating new vectors by mixing existing target vectors randomly selected from the current population. Recently, many mutation operators, which usually employ the best individual or some high-quality individuals randomly chosen, have been proposed to improve searching capability. However, such designs may easily suffer from premature convergence trapped by local optima. To make a trade-off between exploration and exploitation capability, this paper proposes a novel collective intelligence (CI)-based mutation operator, which is named as "current-to-sa-ci-best." In the presented mutation operator, the evolutionary information of m best target vectors is linearly combined to generate new mutant vectors. Besides, m is designed as an exponential-distributed random number which could be self-adapted based on successful records of m values alongside evolution. Moreover, this mutation operator could be applied to any DE algorithm without destroying existing search capability by adding a greedy selection operator. To verify its effectiveness, the proposed CI-based mutation strategy, which is named as SaCI, was embedded into some state-of-the-art DE variants on 28 CEC2013 benchmark functions. Numerical results have confirmed that the SaCI operator may be beneficial to DEs to some extent. [ABSTRACT FROM AUTHOR]

In this paper, a survey about the algorithms based on swarm intelligence with parameter adaptation using some techniques to achieve the best results is presented. In this case, we analyzed the most popular algorithms such as ant colony optimization, particle swarm optimization, bee colony optimization, bat algorithm, firefly algorithm and cuckoo search. These algorithms are referenced in the paper because they have demonstrated to be superior with respect to the other optimization methods based on swarms with parameter adaptation using type-2 fuzzy logic in some applications, and also the algorithms are inspired on swarm intelligence. [ABSTRACT FROM AUTHOR]

Ants are known for their collective intelligence and efficient division of labor. Therefore, observations that seem to contradict these stereotypes are worth reporting. While observing a colony of Camponotus japonicus, we discovered that workers frequently dumped protein-rich food items at a particular spot, while other workers collected the dumped food items and brought them back to the nest. These two counteractive processes were performed simultaneously by the same colony, which indicates possible inefficiency of their collective labor. Discarding a specific type of food to calibrate the nutritional balance of a colony has been previously reported, but we found no reports on re-collecting discarded food. We suspect that this phenomenon might have been an outcome of communication inefficiency within the colony, or that the putative dump site might have served as a temporary deposit of food items before they were redistributed inside the nest. [ABSTRACT FROM AUTHOR]

Active learning promotes knowledge creation as a learning strategy for students working both individually and cooperatively. Learning 2.0 supported by the Web 2.0 model is based on the idea that students are creators of resources that can be used by students and teachers. This work begins in a context in which active learning is used and a model 2.0 through which students create, share and use resources through different channels of a technological ecosystem. From this context, a framework 3.0 was developed and tested, based on the Web 3.0 model, in which all the resources generated by students and teachers are organised and classified through an ontology which can be transferred to other subjects. Moreover, a semantic search system was developed that operates by drawing inferences between the elements of the ontologies. The framework was validated in two groups, respectively. One group was able to use the content they generated in real time, while the other group was only able to use content generated by students in previous courses. The result obtained was that both groups preserved the characteristics of learning in the 2.0 model, and the transition to the 3.0 model allowed better access to the knowledge created in the subject as well as an improvement in the searchability of resources. A relationship was also identified between model 3.0 and an improvement in students' grades. [ABSTRACT FROM AUTHOR]

This paper has the objective of assessing how ICTs are being used to provide accessibility in urban mobility, with special interest to collective intelligence based approaches. A systematic literature review (SLR) was performed, using several different criteria to filter down the 500 + academic papers that were originally obtained from a search for "accessible maps" to the 43 papers that finally remained in the corpus of the SLR. Among the findings, it was noticed that (i) few studies explored the motivations of users to actively contribute to improving accessible maps, by providing information to feed such maps; (ii) studies restricted themselves to exploring three techniques: gaming, monetary reward and ranking; (iii) social networks are rarely used as a source of data for building and updating maps; and (iv) there is no discussion of any initiative that aims to support both, the needs of physically and visually impaired citizens, at the same time. [ABSTRACT FROM AUTHOR]

The proliferation of the social Web due to increased user participation poses a challenge as well as presents an opportunity to examine the collective behavior of users for various business applications. In this work, we leverage the collective knowledge embedded in the social relationships of users on the network to predict user preferences and future behavior. We extract social dimensions in the form of overlapping communities that capture the behavioral heterogeneity in directed and signed social networks. We present an extension of signed modularity, namely Structural Balance Modularity (SBM). We first propose a metric Structural Balance Index (SBI) that determines users' degrees of affiliation towards various communities by harnessing the concept of the generalized theory of structural balance. We then incorporate SBI into the signed modularity to define SBM. It takes into account the density as well as the sign (positive or negative) of the links between users on the network. A genetic algorithm is developed that optimizes the SBM, thereby maximizing positive intra-community connections and negative inter-community connections. The discovered latent overlapping communities represent affiliations of users with similar preferences and mutual trust relationships captured by the signs of connections exerting differential effects on users' behaviors. Thereafter, we ascertain which communities are relevant for a targeted behavior by using discriminative learning. The computational experiments are performed on Epinions real-world dataset, and the results clearly demonstrate the effectiveness and efficacy of our proposed approach. [ABSTRACT FROM AUTHOR]

Group living confers ecological benefits, and the associated fitness gain may be positively related to the size of the group. Thus, the ability to discriminate numerical differences may confer important fitness advantages in social fish. There is evidence that this ability can be improved by behavioral interactions among individuals of the same species. Here, we looked for this effect in both conspecific and heterospecific dyads. In Chinese bream and grass carp, we measured the sociability and shoal preferences of singletons, conspecific dyads and heterospecific dyads presented with different numerical comparisons (0 vs 8, 2 vs 8, 4 vs 8, 6 vs 8 and 8 vs 8). Chinese bream generally showed higher sociability than did grass carp, but grass carp in heterospecific dyads showed improved sociability that was similar to that of Chinese bream. Among the comparisons, both grass carp and Chinese bream singletons could only discriminate the comparison of 2 vs 8, suggesting lower quantitative abilities in these fish species compared to other fish species. Grass carp dyads were more successful in discriminating between 6 and 8 than were singletons, although no such improvement was observed in their discrimination between 4 and 8. In contrast, numerical ability did not vary between singletons and conspecific dyads in Chinese bream. More interestingly, Chinese bream and grass carp in heterospecific groups could discriminate between 4 and 8, but neither species showed a preference when presented with 6 and 8. Our results suggested that interaction between conspecific grass carp might improve their joint numerical ability, and a similar process might occur in Chinese bream in heterospecific dyads. However, the mechanism underlying the differences in improvements in numerical ability requires further investigation. The improved cognitive ability of heterospecific dyads might yield important fitness advantages for predator avoidance and efficient foraging in the wild. [ABSTRACT FROM AUTHOR]

We present in this article, a new decentralized algorithmic approach for the automatic construction of three-dimensional structure based on simple behavioral rules. This model is an improvement of the model introduced by Eric Bonabeau and Guy Theraulaz to simulate the process of building wasps' nest. To speed up the construction process, and to prevent the builder swarm from moving far away from regions with stimuli to build, we constructed a dynamic envelope that limits agents' movements to remain close to the built structure. The envelope adjusts both as the structure develops and as agents assess whether individual sites should be built in, or not. The use of such an envelope results in faster construction and gives an explicit method for terminating a simulation when no buildable sites remain, thus indicating that a structure has converged to its final form. [ABSTRACT FROM AUTHOR]

Population-based metaheuristic algorithms have been extensively applied to solve discrete optimization problems. Generally speaking, they work with a set of candidate solutions in the population which evolve during generations using variant reproduction and selection operations to find the optimal solution(s). The population is similar to a small society having several individuals which seek a common goal/solution. This study is motivated from the election systems of societies which can be applied in the population-based algorithms. We propose utilizing the majority voting for discrete population-based optimization algorithms which uses the information of all candidate solutions in the current generation to create a new trial candidate solution, called a president candidate solution. During optimization process, after applying the evolutionary operations, all candidate solutions vote collectively to determine the values of the president's variables. In the proposed method, a majority voting is utilized to choose a value for each variable (gene) of the president candidate solution. This method keeps untouched all other steps of population-based algorithms; therefore, it can be used with any kind of population-based algorithm. As case studies, the discrete differential evolution (DDE) algorithm and the discrete particle swarm optimization (DPSO) are used as the parent algorithms to develop majority voting-based discrete DE (MVDDE) and majority voting-based discrete PSO (MVDPSO). These two algorithms are evaluated on the fifteen discrete benchmark functions with the dimensions of D = 10, 30, 50, 100, 200 and 500. Simulation results confirm that majority voting-based discrete optimization algorithms obtain a promising performance on the majority of the benchmark functions. In addition, we have conducted some tests on large-scale 0-1 knapsack problems with large scales as a real-world application. [ABSTRACT FROM AUTHOR]

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. [ABSTRACT FROM AUTHOR]

Researchers have indicated that the widespread adoption of digital technologies has changed the way we interact with each other and distinguishes digital natives from their older counterparts. Since young people are actively engaged in participatory cultures, this study aims to explore the linkages of aspects of literacy and principles of connected learning to their collective intelligence efficacy and civic engagement. With a sample of 304 Taiwanese college students, the findings suggest that there are three aspects of the expanded conception of literacy. While literacy as readers and information users, as well as literacy as authors and information creators are both linked to collective intelligence efficacy, the third aspect of critical literacy, which reflects an epistemological shift to cope with the participatory information environment, is linked to young people’s civic engagement, defined by political expression in various forms and by achieving social change in creative ways. Among the four principles of connected learning, however, the principle of fostering a sense of shared purpose mediated by new media and online communities is strongly associated with collected intelligence efficacy, defined by how young people perceive their capability for collaborating with others and making contributions. Overall, the findings provide empirical support for associations among notions/variables with broadened perspectives and re-conceptualizations that are insightful for understanding the Net generation’s new social practices. [ABSTRACT FROM AUTHOR]

Many current authors point toward the heightening of networked individualism and how this affects community creation and engagement. This trend poses strong challenges to the potential beneficial effects of collective intelligence. Education is one of the realms that can strongly suffer from this globalized individualism. Learning is deeply enhanced by social interactions and losing this social dimension will have long-lasting effects in future generations. Networked learning is also a by-product of our societal context, but not per se individual. Our paper presents a case—the HANDSON massive open online course (MOOC)—in which a purposely designed learning environment fosters the emergence of a kind of collective intelligence which, by the learners own accord, brings about a heightened sense of community. The MOOC’s design managed to enable individual learning paces without killing the social dimension. Thus, we argue that when learning together intentionally and informally in networked online environments, small and temporary communities (pop-up communities we call them) will form. This nascent sense of community is a first step that will ultimately contribute to the common good. [ABSTRACT FROM AUTHOR]

In this paper, we present a design tool, the positioning cards that we have developed, validated, and used in different projects. These cards are built to allow CI4CG and Participatory Design researchers to discuss the political alignment of design projects, in iterative processes of design involving people in the definition of the technological features to be implemented. The background of the cards is the conceptualization of contemporary participatory design as public design, engaging with societally relevant phenomena outside the traditional environment of the workplace. To engage with such an extended dimension of participatory design, we frame our contribution in the contemporary form of capitalism, stressing how contemporary capitalism dispossess the wealth created by social production. In this context, we argue, CI4CG designers need to engage deeply with the theoretical implications of their work. To support this effort, we built the cards combining a political perspective oriented toward nourishing the common—the ensemble of the material and symbolic elements tieing together human beings—with the “affect turn” in the social sciences—therefore including affective dimensions like joy, sadness, and desire in the design of CI4CG technologies. In the final part of the article we discuss how we have used the cards in four different projects. [ABSTRACT FROM AUTHOR]

Digital storytelling has become a popular method for curating community, organisational, and individual narratives. Since its beginnings over 20 years ago, projects have sprung up across the globe, where authentic voice is found in the narration of lived experiences. Contributing to a Collective Intelligence for the Common Good, the authors of this paper ask how shared stories can bring impetus to community groups to help identify what they seek to change, and how digital storytelling can be effectively implemented in community partnership projects to enable authentic voices to be carried to other stakeholders in society. The Community Digital Storytelling (CDST) method is introduced as a means for addressing community-of-place issues. There are five stages to this method: preparation, story telling, story digitisation, digital story sense-making, and digital story sharing. Additionally, a Storytelling Cycle of Trust framework is proposed. We identify four trust dimensions as being imperative foundations in implementing community digital media interventions for the common good: legitimacy, authenticity, synergy, and commons. This framework is concerned with increasing the impact that everyday stories can have on society; it is an engine driving prolonged storytelling. From this perspective, we consider the ability to scale up the scope and benefit of stories in civic contexts. To illustrate this framework, we use experiences from the CDST workshop in northern Britain and compare this with a social innovation project in the southern Netherlands. [ABSTRACT FROM AUTHOR]

Collective intelligence for the common good is considered here in terms of its contribution to social transformation at the micro level of community. A critical evaluation of the knowledge limitations of research programmes currently focussing on collective intelligence is presented before the case is made to widen collective intelligence research efforts and understanding. The application of a ‘common good’ focus to collective intelligence research and practice provides a contextualising space for community practice in the digital age to be considered through a philosophy of community technologies. Community media is presented as providing tools, spaces and processes for such critical considerations to be made. Community learning and community-based learning theories are discussed and drawn together to illustrate how community–university partnerships can be developed to facilitate and promote collective intelligence for the common good. The paper concludes with an introductory discussion of the Community Media 4 Kenya (CM4K) community–university partnership as an exemplar of collective intelligence for the common good. [ABSTRACT FROM AUTHOR]

In this research, an agent-based simulation seeks to discuss the tragedy of the commons, collective intelligence and institutions developed by social groups. The concept of the tragedy of the commons states that you can always expect environmental degradation when many individuals freely exploit a scarce resource of common use. Hardin (Science 162:1243–1248, 1968) proposes two alternatives to deal with it: state or privatized administration. However, it is possible another alternative of self-coordination when the social groups are small. That is, the tragedy of the commons could be faced when the synergy within the group—collective intelligence—develops. The focus of this paper is to analyze small groups who promote their coordination to achieve collective goals, aimed at the preservation and perpetuation of scarce natural resources of common use. The results of this research show that the extreme exhaustion of the environment and consequent dissolution of the group that depends on it, is easily observed in many circumstances. However, in small groups, the self-coordination through the development of strong institutions may avoid an eventual tragic outcome of total environment degradation. Additionally, it has been found that social identity influences the behavior of the individuals, determining the social structures. [ABSTRACT FROM AUTHOR]

Large-scale political or cultural events in urban environments disrupt and challenge city residents in making informed decisions to navigate through restricted zones. However, little research exists that examines how residents successfully overcome the mobility turmoils created by such events. Studies into collective intelligence found socio-technical efforts that can assist citizens in dealing with challenging situations in urban environments. Yet, studies into other forms of collective and network sociality and how those forms are interwoven with the social construction of urban space, are rare. In this paper, we present data from the 2014 G20 Summit held in Brisbane, Australia, to propose that collective-informed decisions made by urban residents in disrupted environments are influenced by particular communication patterns and information access, as well as the production and reproduction of urban imaginaries. An urban exploration as the G20 event unfolded, followed by audio-recorded focus groups, was conducted with 14 participants who mapped their walks through the city using a mobile app and captured images of different locations along their paths. The analysis of these data contributes towards a deeper understanding of collective intelligence strategies for citizens interacting with urban space and gives rise to potential strategies for enhancing socio-spatial engagement of urban residents. [ABSTRACT FROM AUTHOR]

This paper presents a critical reflection on insights into the ongoing endeavours for community engagement by Ayara and MAL; two urban grassroot organisations in Bogota, Colombia, where a long history of internal conflicts has resulted in diverse human right violations. The paper presents examples of the grassroots organisations’ unique methods of engagement that promotes building collective intelligence from the bottom–up through creative collaboration and design processes, leading to rebuilding social fabrics that support the common good for the people of Bogota. [ABSTRACT FROM AUTHOR]

This paper explores two reddit communities that supported Bernie Sanders and Donald Trump, respectively, in the run up to the 2016 US Presidential election campaign. Much of the paper is dedicated to explaining how reddit functions, describing the behaviour of the subreddit communities in question and then asking whether these demonstrated collective intelligence. Subreddit communities submit and vote on content, through their votes they make collective decisions about which content will be broadcast to their community. Large subreddit communities that formed rapidly to support a candidate in an election have not previously been observed on reddit—these offered an interesting context for the consideration of whether subreddit communities demonstrate collective intelligence. Voting is a key determinant of what happens on each subreddit and it is conducted anonymously, it is, therefore, not possible to understand the role(s) that every individual plays in the functioning of the subreddit. The behaviour of these subreddit communities can only be understood as a collective of submitting, commenting, voting and moderating participants. Whether these collectives behave intelligently is a matter of how one defines intelligence—but it is clear that they can be effective in pursuing certain ends. These collectives encounter and sometimes oppose each other on reddit. The community of Trump supporters in particular were in conflict with a number of other high-profile communities on the site, and also the platform’s administrators. [ABSTRACT FROM AUTHOR]

Climate change is one of the biggest challenges humanity faces today. Despite of high investments in technology, battling climate change is futile without the participation of the public, and changing their perception and habits. Collective intelligence tools can play an important role in translating this “distant” concept that is climate change into practical hints for everyday life. In this paper, we report a case study grounded on collective intelligence tools to collaboratively build knowledge around energy conservation. A preliminary study to raise energy awareness in an academic environment is summarised, setting the scene to a more ambitious initiative based on personal stories to transform energy awareness into behaviour change. The role of the collective intelligence tools and other technical artefacts involved are discussed, suggesting strategies and features that contributed (or not) to users’ engagement and collective awareness. Lessons learned from both studies are reported with a sociotechnical approach as implications for design pursuing behaviour change. [ABSTRACT FROM AUTHOR]

Nowadays, social media analysis systems are feeding on user contributed data, either for beneficial purposes, such as emergency management, or for user profiling and mass surveillance. Here, we carry out a discussion about the power and pitfalls of public accessibility to social media-based systems, with specific regards to the emergency management application EARS (Earthquake Alert and Report System). We investigate whether opening such systems to the population at large would further strengthen the link between communities of volunteer citizens, intelligent systems, and decision makers, thus going in the direction of developing more sustainable and resilient societies. Our analysis highlights fundamental challenges and provides interesting insights into a number of research directions with the aim of developing human-centered social media-based systems. [ABSTRACT FROM AUTHOR]

There has been growing interest in leveraging Web-based social and communication technologies for better crisis response. How might the Web platforms be used as an observatory to systematically understand the dynamics of the public's attention during disaster events? And how could we monitor such attention in a cost-effective way? In this work, we propose an 'attention shift network' framework to systematically observe, measure, and analyze the dynamics of collective attention in response to real-world exogenous shocks such as disasters. Through tracing hashtags that appeared in Twitter users' complete timeline around several violent terrorist attacks, we study the properties of network structures and reveal the temporal dynamics of the collective attention across multiple disasters. Further, to enable an efficient monitoring of the collective attention dynamics, we propose an effective stochastic sampling approach that accounts for the users' hashtag adoption frequency, connectedness and diversity, as well as data variability. We conduct extensive experiments to show that the proposed sampling approach significantly outperforms several alternative methods in both retaining the network structures and preserving the information with a small set of sampling targets, suggesting the utility of the proposed method in various realistic settings. [ABSTRACT FROM AUTHOR]

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains. [ABSTRACT FROM AUTHOR]

Critical thinking (CT) is a metacognitive process, consisting of a number of sub-skills and dispositions that, when used appropriately, increases the chances of producing a logical conclusion to an argument or solution to a problem. Though the CT literature argues that dispositions are as important to CT as is the ability to perform CT skills, the majority of research in the area has focused on CT skills. In addition, though most CT interventions are designed based on academic or expert definitions of CT, students are rarely, if ever, asked to guide their instruction by describing their perspectives on what constitutes CT. Thus, the current study used interactive management to examine similarities and differences in the way students and educators conceptualise CT dispositions. Interactive management (IM) is a computer-assisted process that allows a group to build a consensus-based structural model describing relations between elements in a system. Consistent with previous research, it is suggested that addressing how students conceptualise CT may provide a starting point to negotiate and innovative the CT curriculum and learning process. Furthermore, evaluating similarities and differences in the way students and educators understand CT dispositions may facilitate the building of complementary and integrative models of CT dispositions that reflect the full range of perspectives in University teaching environments. Results of the current study suggest that while students' conceptualisation of CT dispositions was largely consistent with both the educators' conceptualisation and dispositions highlighted in the extant literature, students' descriptions were broader, less abstract and more concrete accounts of CT dispositions; and were also primarily focused on utility or function rather than ideal principles of action. Results are discussed in light of research and theory on CT and best practice for CT instruction. [ABSTRACT FROM AUTHOR]

This paper presents a novel model of science funding that exploits the wisdom of the scientific crowd. Each researcher receives an equal, unconditional part of all available science funding on a yearly basis, but is required to individually donate to other scientists a given fraction of all they receive. Science funding thus moves from one scientist to the next in such a way that scientists who receive many donations must also redistribute the most. As the funding circulates through the scientific community it is mathematically expected to converge on a funding distribution favored by the entire scientific community. This is achieved without any proposal submissions or reviews. The model furthermore funds scientists instead of projects, reducing much of the overhead and bias of the present grant peer review system. Model validation using large-scale citation data and funding records over the past 20 years show that the proposed model could yield funding distributions that are similar to those of the NSF and NIH, and the model could potentially be more fair and more equitable. We discuss possible extensions of this approach as well as science policy implications. [ABSTRACT FROM AUTHOR]

Robots are developing in much the same way that personal computers did 40 years ago, and robot operating system is the critical basis. Current robot software is mainly designed for individual robots. We present in this paper the design of micROS, a morphable, intelligent and collective robot operating system for future collective and collaborative robots. We first present the architecture of micROS, including the distributed architecture for collective robot system as a whole and the layered architecture for every single node. We then present the design of autonomous behavior management based on the observe-orient-decide-act cognitive behavior model and the design of collective intelligence including collective perception, collective cognition, collective game and collective dynamics. We also give the design of morphable resource management, which first categorizes robot resources into physical, information, cognitive and social domains, and then achieve morphability based on self-adaptive software technology. We finally deploy micROS on NuBot football robots and achieve significant improvement in real-time performance. [ABSTRACT FROM AUTHOR]