Collective Intelligence Framework Research Articles

The collective intelligence of evolution and development

Collective intelligence and individual intelligence are usually considered to be fundamentally different. Individual intelligence is uncontroversial. It occurs in organisms with special neural machinery, evolved by natural selection to enable cognitive and learning functions that serve the fitness benefit of the organism, and then trained through lifetime experience to maximise individual rewards. Whilst the mechanisms of individual intelligence are not fully understood, good models exist for many aspects of individual cognition and learning. Collective intelligence, in contrast, is a much more ambiguous idea. What exactly constitutes collective intelligence is often vague, and the mechanisms that might enable it are frequently domain-specific. These cannot be mechanisms selected specifically for the purpose of collective intelligence because collectives are not (except in special circumstances) evolutionary units, and it is not clear that collectives can learn the way individual intelligences do since they are not a singular locus of rewards and benefits. Here, we use examples from evolution and developmental morphogenesis to argue that these apparent distinctions are not as categorical as they appear. Breaking down such distinctions enables us to borrow from and expand existing models of individual cognition and learning as a framework for collective intelligence, in particular connectionist models familiar in the context of neural networks. We discuss how specific features of these models inform the necessary and sufficient conditions for collective intelligence, and identify current knowledge gaps as opportunities for future research.

The Generic Collective Intelligence Framework: A Qualitative View

The Generic Collective Intelligence Framework: A Qualitative View

Social Networks as Platforms for Enhancing Collective Intelligence

Many research results have documented the powerful role of collective intelligence solving a wide range of problems. Such a role has been often called collective intelligence, which is commonly considered as a group of members acting as single unit can outperform any single individual (even an expert). Arguably, the main objective of collective intelligence is to maximize the ability of a collective of members that is not shown by members acting individually. However, not all collectives are intelligent, an intelligent collective is characterized by diversity, independence, decentralization, and aggregation. In this paper, we present recent research results on the use of collective intelligence in social networks. Then, a framework for collective intelligence in social networks with these characterizations is also introduced. Finally, research challenges on enhancing collective intelligence in social networks have also discussed.

Entrepreneurial universities and strategy: the case of the University of Bari

Purpose The purpose of this paper is to investigate why entrepreneurial universities choose a particular business strategy focussing on diversification and multi-nationalisation, and the role of intellectual capital (IC) in supporting such strategies. Design/methodology/approach The research question is answered through an exploratory case study of the University of Bari, Italy. Data were collected from strategic plans, annual reports, national evaluation reports and semi-structured interviews with the university’s board members and analysed using Secundo et al.’s (2016) collective intelligence framework. Findings The authors show how contingency factors, such as economic and historical reasons, justify both the diversification and internationalisation strategies, and how they both rely on IC. Practical implications The results of this study can be used by managers to support the development of entrepreneurial university strategies. Originality/value The paper is novel because it provides theoretical justification to strategy development in a university setting. Additionally, the findings contribute to the fourth stage of IC research by showing how IC can be used to support diversification and internationalisation in a university and support third mission goals. Finally, the paper provides an empirical application of the Secundo et al.’s (2016) model for understanding IC in universities.

Understanding intellectual capital disclosure in online media Big Data

PurposeIntellectual capital disclosure (ICD) in universities is gaining increasing attention, especially through the adoption of innovative technologies. Online media, as a relevant source of Big Data, is shifting ICD. The purpose of this paper is to explore how Big Data generated through online media, such as websites and platforms like Facebook, can be used as rich sources of data and viable disclosure channels for ICD in a university.Design/methodology/approachThis is an exploratory case study, following the methodology in Yin (2014), that examines how online media data contributes to closing the ICD gap. The IC disclosed through different online media channels by a private university in Albania is analysed using Secundo et al.’s (2016) collective intelligence framework. The online data sources include the university’s website, Facebook page, periodic reports and statements outlining future goals.FindingsWhat the authors discover in this research is that IC is an important part of how universities operate, and IC is communicated through social media, although unintentionally. However, this only serves to highlight the importance of IC, and if researchers want to discover IC and understand how it works in an organisation, they need to include social media and a prime resource for developing that understanding.Research limitations/implicationsMost importantly, the findings add to a growing consensus that ICD researchers, and researchers in other management and accounting disciplines, who traditionally rely on annual corporate social responsibility and other periodic reports, they need to change their medium of analysis because these reports no longer can be relied on to understand IC and its impact on an organisation.Originality/valueOnline media tools and the advent of Big Data have created new opportunities for universities to disclose their IC information to stakeholders in a timely manner and to gain relevant insights into their impact on the society. The originality of the paper resides in the contribution of Big Data to the ICD research stream.

A Declaração Universal sobre os Arquivos (DUA): uma reflexão de sua representação social

Este estudio tiene como objetivo elucidar la representatividad social de la Declaración Universal de los Archivos (DUA) en el ámbito de la inteligencia colectiva y de las habilidades requeridas en el proceso de aprendizaje y comprensión de los usuarios y del público en general. La Declaración establece un nuevo enfoque para la preservación del patrimonio documental y la protección de la memoria individual y colectiva, socializando la conciencia de la responsabilidad de todos en la salvaguarda del proceso de construcción del conocimiento. Este es un estudio descriptivo que utiliza un método de investigación bibliográfica y documental, y realiza un análisis de la UDA, su historia e impacto en el proceso de construcción el compromiso de todos en la percepción de la importancia de la preservación de los archivos, las bibliotecas y los museos. La UDA colabora en el desarrollo de la concientización de los ciudadanos para la necesidad de formar profesionales en habilidades de información y convertirse en agentes de memoria y facilitadores del acceso a la información archivada. El estudio concluye con recomendaciones para inclusión de los temas de la UDA en los currículos de los cursos de Biblioteconomía y Archivística, asegurando así que sus principios sean comprendidos y experimentados en la práctica profesional, mientras que sirve para construir la identidad social y el conocimiento.

Intellectual capital management in the fourth stage of IC research

PurposeThe purpose of this paper is to present a case study of a university that uses a collective intelligence approach for managing its intellectual capital (IC). Specifically, the authors investigate how one of Europe’s oldest business schools, Ca’ Foscari University of Venice (Italy), manages IC through stakeholder engagement to achieve academia’s third mission so contributing to social and economic development.Design/methodology/approachData are collected through semi-structured interviews and Ca’ Foscari University’s strategic plan. Secundo et al.’s (2016) collective intelligence framework is used to analyse the data. Alvesson and Deetz’s (2000, pp. 19-20) critical management tasks – insight, critique and transformative redefinition – are adopted to frame and discuss the results.FindingsOn the assumption that a university is a collective intelligence system, the findings demonstrate that IC management needs to change to incorporate an ecosystem perspective, reflecting the fourth stage of IC research. The IC management at the university incorporates its core goal (what), the collective involvement of internal and external stakeholders to achieve the goal (who), the motivations behind the achievement of the goal (why) and, finally, the processes activated inside the university (how) and indicators to assess value creation.Research limitations/implicationsA new perspective for managing IC in universities that adopts a collective intelligence approach is further developed. Contributions to the fourth stage of IC research – IC in an ecosystem – are highlighted that expand the concept of IC value creation beyond universities into wider society.Practical implicationsTwo key consequences of this case study are that more stakeholders have become involved in IC management and that IC management requires critical rethinking, given the universities’ evolving role.Originality/valueThis paper brings together issues that are usually dealt with in separate domains of the literature: IC management and collective intelligence in the university setting.

Modes of Thought in Entrepreneurial Intention Studies: A Collective Intelligence Perspective

The concept of collective intelligence supports the notion that the coexistence of different perspectives is a necessary condition for a complex system to adapt to the environment and to keep its vitality. Transposing this assumption to a scientific community, we investigate the field of entrepreneurial intention studies and examine its internal dynamics by identifying the relevant practices to conduct research. Specifically, we conduct a hybrid bibliometric analysis to discover 68 articles constituting the field’s knowledge base and 83 articles representing its research fronts. This analysis is completed by an analytical coding of each paper according to the collective intelligence framework proposed by Woolley and Fuchs (2011). We find both a convergent and a divergent perspective; the former oriented to clarifying and bounding concepts, theories, and contexts; the latter interested in opening new paths. However, a reflective perspective that calls into question the field’s assumptions to reformulate directions and general purposes is still lacking. Our results suggest a set of priorities to be taken into account in the interest of driving future research.

Livestock infectious disease and climate change: a review of selected literature

It is indisputable that climate is an important factor in many livestock diseases. Nevertheless, our knowledge of the impact of climate change on livestock infectious diseases is much less certain.Therefore, the aim of the article is to conduct a systematic review of the literature on the topic utilizing available retrospective data and information. Across a corpus of 175 formal publications,limited empirical evidence was offered to underpin many of the main arguments. The literature reviewed was highly polarized and often inconsistent regarding what the future may hold. Historical explorations were rare. However, identifying past drivers to livestock disease may not fully capture the extent that new and unknown drivers will influence future change. As such, our current predictive capacity is low. We offer a number of recommendations to strengthen this capacity in the coming years. We conclude that our current approach to research on the topic is limiting and unlikely to yield sufficient, actionable evidence to inform future praxis. Therefore, we argue for the creation of a reflexive, knowledge-based system, underpinned by a collective intelligence framework to support the drawing of inferences across the literature.

Collective Intelligence of Market-Categories in Entrepreneurial Ecosystems: Evidence of Population-Level Learning in Mobile Applications

In this paper, we examine how groups of organizations engage in population-level learning in entrepreneurial ecosystems. We specifically analyze how within- and across-group characteristics of organizational groups shape learning, and thereby extend the literature on population-level learning that has focused mostly on within-population leaning. We argue that a group of new organizations may develop collective intelligence, in which population-level learning stemming from both the audience of consumers and fellow producers enhances innovation in groups. These persistent differences are facilitated by active feedback from within the audience (within-group learning sources) and member organizations’ diversification into other groups (across-group sources), but the effect of latter decreases after a certain point, indicating a curvilinear relationship. Our longitudinal analysis of Apple’s mobile application ecosystem supports these claims. Different market-categories indicated a significant, persistent heterogeneity in the aggregated performance across several different criteria, and this heterogeneity was facilitated by active audience feedback. Diversification of member organizations also showed a positive influence on group-level heterogeneity, yet its effect decreased after a threshold. This paper contributes to the literature on population-level learning by showing the importance of examining across-group learning sources, and by introducing a new collective intelligence framework for analyzing organizational groups. In addition, we provide a framework through which persistent heterogeneity in entrepreneurial ecosystem innovation can be understood.

Constraint handling in probability collectives using a modified feasibility-based rule

Almost all existing heuristic techniques are unconstrained optimisation methods and treat the system in centralised way. A distributed and decentralised optimisation technique in the framework of collective intelligence referred to as probability collectives PCs decomposes the entire system into subsystems and treats them as a multi-agent system. Similar to other contemporary heuristic techniques, its performance is significantly affected when constraints are involved. In order to handle constraints, a modified feasibility-based rule is incorporated into the PC algorithm. The approach is validated by solving a variety of constrained test problems. A tension/compression spring design problem, welded beam design problem and pressure vessel design problem are also solved. The approach is shown to be sufficiently robust and other strengths and weaknesses are also discussed. The solution to these problems proves that the constrained PC approach can be applied to a variety of practical/real world problems.

A semantic approach for enhancing assistive services in ubiquitous robotics

The Ambient Intelligence (AmI) technologies have the potential to create intelligent environments with new generation of assistive services, enhanced with ubiquitous robots. These environments have the ability to be anticipatory, responsive and intelligent providers of assistive services anytime and anywhere. These services can assist frail persons effectively in their daily tasks. One of the main challenging research problems in assistive robotics is to endow ubiquitous robots with ability to pro-actively taking on some tasks to help humans in performing complex activities, by participating with them just as other humans do, in normal societies or organizations.In this paper, we propose a collective intelligence framework based on narrative reasoning and natural language processing. In the proposed approach, we propose a hybrid model that bridges together the Narrative Knowledge Representation Language (NKRL), from natural language processing field, and the HARMS (Humans, software Agents, Robots, Machines and Sensors) model, from multi-agent systems engineering field. This model is able to (i) drive the dialogues between humans, robots and smart devices, (ii) understand a complex situation, and (iii) trigger reactive actions, in the ubiquitous environment, according to given contexts.Two scenarios dedicated to the assistance of a frail person in a smart home equipped with a companion robot and smart objects are implemented and discussed for validation purposes of the proposed framework.

Crowdteaching: Supporting teaching as designing in collective intelligence communities

<p>The widespread availability of high-quality Web-based content offers new potential for supporting teachers as designers of curricula and classroom activities. When coupled with a participatory Web culture and infrastructure, teachers can share their creations as well as leverage from the best that their peers have to offer to support a collective intelligence or crowdsourcing community, which we dub <em>crowdteaching</em>. We applied a collective intelligence framework to characterize crowdteaching in the context of a Web-based tool for teachers called the Instructional Architect (IA). The IA enables teachers to find, create, and share instructional activities (called IA projects) for their students using online learning resources. These IA projects can further be viewed, copied, or adapted by other IA users. This study examines the usage activities of two samples of teachers, and also analyzes the characteristics of a subset of their IA projects. Analyses of teacher activities suggest that they are engaging in crowdteaching processes. Teachers, on average, chose to share over half of their IA projects, and copied some directly from other IA projects. Thus, these teachers can be seen as both contributors to and consumers of crowdteaching processes. In addition, IA users preferred to view IA projects rather than to completely copy them. Finally, correlational results based on an analysis of the characteristics of IA projects suggest that several easily computed metrics (number of views, number of copies, and number of words in IA projects) can act as an indirect proxy of instructionally relevant indicators of the content of IA projects.</p>

Introducing Open Innovation to Universities How to Increase Universitiess Innovativeness

In order to react to increasing competition for new students, European universities need to become more attractive and listen to the demands of their students. Open Innovation has proven to be an effective way for the integration of new stakeholders into innovation processes and increase innovativeness. This paper describes the application of a framework for the design of Collective Intelligence task to an Open Innovation context. We will show how the framework supports the conceptual design of an idea generation process for a virtual ideas community platform for a German university. The process will account for the specific environment of public universities and the demands of their students. The paper shows that the framework for Collective Intelligence is also applicable to crowdsourced Open Innovation activities like idea generation.

Livestock infectious disease and climate change: a review of selected literature.

Abstract It is indisputable that climate is an important factor in many livestock diseases. Nevertheless, our knowledge of the impact of climate change on livestock infectious diseases is much less certain. Therefore, the aim of the article is to conduct a systematic review of the literature on the topic utilizing available retrospective data and information. Across a corpus of 175 formal publications, limited empirical evidence was offered to underpin many of the main arguments. The literature reviewed was highly polarized and often inconsistent regarding what the future may hold. Historical explorations were rare. However, identifying past drivers to livestock disease may not fully capture the extent that new and unknown drivers will influence future change. As such, our current predictive capacity is low. We offer a number of recommendations to strengthen this capacity in the coming years. We conclude that our current approach to research on the topic is limiting and unlikely to yield sufficient, actionable evidence to inform future praxis. Therefore, we argue for the creation of a reflexive, knowledge-based system, underpinned by a collective intelligence framework to support the drawing of inferences across the literature.

A framework for collective intelligence from internet Q&A documents

One source of collective intelligence on the internet is the Question and Answer (Q&A) data available from many Q&A sites. Although such data has been helpful to many people, its helpfulness is limited. The data is dispersed across different sites. Even on the same site, the data is often not properly organised or ranked. This paper proposes a framework for constructing and extending a Q&A thesaurus for various applications, including online commerce. We have implemented the framework, and validated the accuracy of Q&A data classification.

An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games

In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not stationary and the Markov property is not valid. This requires agents to be adaptive. RL is a natural approach to model the learning of individual agents. These Learning algorithms are however known to be sensitive to the correct choice of parameter settings for single agent systems. This issue is more prevalent in the MAS case due to the changing interactions amongst the agents. It is largely an open question for a developer of MAS of how to design the individual agents such that, through learning, the agents as a collective arrive at good solutions. We will show that modeling RL in MAS, by taking an evolutionary game theoretic point of view, is a new and potentially successful way to guide learning agents to the most suitable solution for their task at hand. We show how evolutionary dynamics (ED) from Evolutionary Game Theory can help the developer of a MAS in good choices of parameter settings of the used RL algorithms. The ED essentially predict the equilibriums outcomes of the MAS where the agents use individual RL algorithms. More specifically, we show how the ED predict the learning trajectories of Q-Learners for iterated games. Moreover, we apply our results to (an extension of) the COllective INtelligence framework (COIN). COIN is a proved engineering approach for learning of cooperative tasks in MASs. The utilities of the agents are re-engineered to contribute to the global utility. We show how the improved results for MAS RL in COIN, and a developed extension, are predicted by the ED.