Using the Collective Intelligence for inventive problem solving: A contribution for Open Computer Aided Innovation

Flores, René Lopez; Belaud, Jean‐Pierre; Lann, Jean-Marc Le; Negny, Stéphane

doi:10.1016/j.eswa.2015.08.024

Cited by 30 publications

(10 citation statements)

References 47 publications

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“…The same principles are found in the organization of the human brain considered as a critically organized collective of neurons (Beggs, 2019;Cocchi et al, 2017;Pospelov et al, 2019;Trastoy and Schuller, 2018); as development and functioning of the latter is conditioned by social interaction (Buckholtz and Marois, 2012;Conte, 2002;Humphrey, 1976;Märtsin, 2008;Vygotsky, 1978), so that intelligence appears as a fundamentally collective phenomenon even when it functions in relatively standalone mode. This observation is expressed in century-old concepts of the noosphere and the collective unconscious (Jung, 2014;Levit, 2000) that are subject to selforganization regularities studied in recent decades.…”

Section: Collective Intelligencementioning

confidence: 97%

“…4.2.2 Artificial systems: design of intelligent organizations. Contemporary tasks of sensing the environment (Guo et al, 2015;Wang et al, 2019), prognosis of the future (LaComb et al, 2007), innovation (Bothos et al, 2012), decision-making and problem-solving (Brush et al, 2018;Lopez Flores et al, 2015) of ever-increasing complexity and number motivate for development of collective thinking practices which would outperform individual experts (Elia and Margherita, 2018;Kocher and Sutter, 2005;Michaelsen et al, 1989;Schoder et al, 2014). Operation of distributed information processing systems known as a swarm or collective intelligence (Alag, 2009;Bonabeau et al, 1999) crucially depends on interaction patterns between individuals which suppress or facilitate genuine communication, deliberate change of beliefs, unexpected decisions, diversity and reconciliation of viewpoints (Kerr and Tindale, 2004;Krause et al, 2010;Michelucci, 2013;Sunstein, 2004;Surowiecki, 2004).…”

Section: Collective Intelligencementioning

confidence: 99%

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Regimes of collective logic

Surov

Ignateva

Bazhenov

2020

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Purpose The study aims to reveal regularities of collective decision in groups of varying cooperativity and to investigate the influence of individualism–collectivism cultural dimension on the emergence of collective behavioral entities. Design/methodology/approach An agent-based model of group behavior is used to simulate the logic of collective binary decisions defined by reaching a consensus threshold. The key parameter of the model is the cooperativity of interaction between individuals which locates the group in individualism–collectivism cultural dimension. The probabilities of collective decisions are measured as a function of individual preferences and the strength of cooperation between agents. Findings Strong, weak and middle point cooperativities of inter-individual interaction define three distinct regimes of collective decision logic, namely, individualism, conformism and criticality. The critical organization allows the group to generate coherent non-predetermined collective behavior in statistical agreement with individual preferences. Research limitations/implications Emergent collective behavior with coherent and nondeterministic decision-making can be modeled in an agent-based approach with local interaction between individuals tuned to a critical point. Variation of a single cooperativity parameter accounts for a continuous transition between organizations of the group inherent to inert matter and living systems. Social implications A new organization of social systems distinguished by internally relaxed relations and large-scale freedom is found beyond the traditional individualism–collectivism contraposition. This state is reached by tuning behavioral logic of individuals to a critical balance of individual and collective values. Originality/value Individualist, conformist and critical regimes of collective organization distinct in terms of integrity, complexity and determinism are recognized as universal organizational classes of nature spanning both inert and living systems. In particular, individualist and conformist regimes produce simple deterministic behavior typical for gases and solid bodies, whereas the critical organization generates complex nondeterministic behavior inherent to living organisms.

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Section: Collective Intelligencementioning

confidence: 97%

Section: Collective Intelligencementioning

confidence: 99%

Regimes of collective logic

Surov

Ignateva

Bazhenov

2020

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“…Using the collective intelligence for inventive problem solving: A computer Aided Innovation [22] An agent proposes and shares a project through a platform with the other registered users. The system provides a list of possible collaborators.…”

Section: Characteristics Of the Actorsmentioning

confidence: 99%

Design of the organizational structure and coordination of an observatory based on collective intelligence

2020

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Introduction: This article is the product of research developed within the framework of the Emple-ap Project, throughout 2019 at the School of Industrial Engineering, Universidad Distrital Francisco Jose de Caldas. Problem: Limitations in the use of traditional hierarchical structures for the management of organizations based on control hierarchies, due to their difficulty in operating efficiently in human social systems (HSS). Objective: To generate a general proposal to understand the structure and organizational management for the observatory of employability of the Pacific Alliance (PA) supported by collective intelligence (CI) and high reliability organization (HRO). Methodology: The methodology for the development of the proposal is supported on grounded theory, in which the design emerges from the statements made in previous research. Results: Based on the results of the review, it presents a proposal for organizational structure for the observatory, emphasizing the alternatives to divide and coordinate the work and the important characteristics of the structure to facilitate the exploitation of available resources. Conclusion: The CI and reliable organizations, are a different and decentralized way of management in organizations that allow you to create more flexible and robust systems. This study creates a general outline of the characteristics and main preconditions that must have a system based on CI. Originality: The main contribution of this research lies in the design of an organizational structure, different from the traditional hierarchical structure, supported by CI for the management of an entire organization. Limitations: Currently, there are no great advances with respect to organizational structures or alternatives to the traditional hierarchies of control, despite limitations in information processing and the impact that has on performance.

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“…They are classified, but not limited to, as techniques to gather data for intelligence in web applications according to (Alag, 2008). The use of collective intelligence for inventive problem solving in the context of an open CAI is detailed in (Lopez Flores et al, 2015b).…”

Section: Collective Intelligencementioning

confidence: 99%

Management of «Systematic Innovation»: A kind of quest for the Holy Grail!

Negny

Lann

Flores

et al. 2017

Computers & Chemical Engineering

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In this paper, authors propose a contribution for improving the open innovation processes. It shows the necessity to get an efficient methodology for open innovation in order to build a computer aided tool for inventive design in Process Systems Engineering (PSE). The proposed methodology will be evocated to be fully used in the context of the "revolutionary" concepts around the so-called factory for the future, also called integrated digital factory, innovative factory.. . As a result the main contribution of this paper is to propose a software prototype for an Open Computer Aided Innovation 2.0. By definition this open innovation relies on collaboration. This collaboration should enable a community, with a very broad spectrum of skills, to share data, information, knowledge and ideas. As a consequence, a first sub objective is to create a methodological framework that takes advantages of collaboration and collective intelligence (with its capacity to join intelligence and knowledge). Furthermore, the raise of the digital company and more particularly the breakthroughs in information technologies is a powerful enabler to extend and improve the potential of collective intelligence. The second sub objective is to propose a problem resolution process to impel creativity of expert but also to develop, validate and select innovative solutions. After dealing with the importance of Process Innovation and Problem solving investigation in PSE, the proposed approach originally based on an extension of the TRIZ theory (Russian acronym for Theory of Inventive Problem Solving), has been improved by using approach such as case-based reasoning, in order to tackle and revisit problems encountered in the PSE. A case study on biomass is used to illustrate the capabilities of the methodology and the tool.

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Using the Collective Intelligence for inventive problem solving: A contribution for Open Computer Aided Innovation

Cited by 30 publications

References 47 publications

Regimes of collective logic

Regimes of collective logic

Design of the organizational structure and coordination of an observatory based on collective intelligence

Management of «Systematic Innovation»: A kind of quest for the Holy Grail!

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