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The main purpose of this paper is to consider knowledge production as a social self‐organization process, to clarify ethical implications of such an approach, and to relate it to the thinking of Heinz von Foerster.

The method employed is the one of dialectical constructions, i.e. existing contradicting approaches on knowledge research are identified and classified and a constructive synthesis of these approaches is made.

Since Heinz von Foerster's pioneering work, information‐generating systems are considered to be self‐organizing systems. We see knowledge as only a particular kind of information: it is the manifestation of information in the social realm. Thus, the creation of social information is due to the self‐organization of social systems. Heinz von Foerster has given us some indications of how knowledge and self‐organization could be applied to society. In this paper, we try to sketch a position of our own while taking into consideration Heinz von Foerster's relevant ideas.

The research results in this paper imply that a knowledge‐based society can only survive if it is designed in a participatory and socially and ecologically sustainable way. Hence a practical implication is that participation and co‐operation need to be advanced in order to guarantee human development.

The innovative aspect of the paper is that it suggests that all social self‐organizing systems are knowledge‐producing systems and that considering knowledge as a co‐operative process implies responsibility for solving the global social problems. It combines knowledge research and systems thinking based on ideas on self‐organization by Heinz von Foerster in order to describe social systems.

In order to deepen the understanding of self-organization, the purpose of the paper is to raise and analyze the state of the art in the area of innovation networks, particularly the characteristics of self-organizing, relying on the theory of complex systems to overcome any shortcomings.

The databases selected for the search were Web of Science and Scopus; the keywords searched in the titles of articles were innovation networks, complex systems, self-organization and self-organizing; the timeline of the search covers the period between 2000 and 2014 due to the presence of important studies in the field of networks starting in the early 2000s; only studies published in English were used; the articles selected were examined by first reading the titles, then the abstracts, and finally the texts in full.

The way the main constructs from the analytical perspective of innovation networks intersect with complex systems explains how self-organization is presented and how it can be allowed to occur within a view of expected benefits for the purposes of these networks.

The originality of the research lies in the questioning of the classical form of organizational management in innovation networks, essentially based on the concentration of hierarchical power.

Illustrate supported by Beer’s Viable System Model and four vignettes the relevance of self-organisation, recursive structures, self-reference and reflexivity in policy processes. The paper aims to discuss these issues.

First, the concepts of self-organisation, recursive structures, self-reference and reflexivity are briefly discussed to ground policy processes in good cybernetics. Then, with the support of four vignettes, the idea of good cybernetics in policy processes is illustrated.

The cybernetics of policy processes is often ignored.

If the purpose of this paper were to influence policy makers it would be necessary to further the empirical base of the four vignettes and clarify desirable forums to ground the relevance of self-organisation, recursive structures, self-reference and reflexivity in policy processes.

Beer’s recursive structures, self-reference and reflexivity have much to contribute to the betterment of policy processes and the amelioration of the unbearable social and organisational costs of many current policies.

The application of concepts such as self-organisation, recursive structures, self-reference and reflexivity adds to the understanding of policy processes.

Autopoiesis is a concept originally used to define living systems. However, no measure for autopoiesis has been proposed so far. Moreover, how can we build systems with a higher autopoiesis value? The paper aims to discuss these issues.

Relating autopoiesis with Ashby’s law of requisite variety, self-organization is put forward as a way in which systems can be designed to match the variety of their environment.

Guided self-organization has been shown to produce systems which can adapt to the requisite variety of their environment, offering more efficient solutions for problems that change in time than those obtained with traditional techniques.

Being able to measure autopoiesis allows us to apply this measure to all systems. More “living” systems will be fitter to survive in their environments: biological, social, technological, or urban.

A recommendation system for an extended process of information retrieval in distributed information systems is proposed. This system is both a model of dynamic cognitive categorization processes and powerful real application useful for knowledge management. It utilizes an extension of fuzzy sets named evidence sets as the mathematical mechanisms to implement the categorization processes. It is a development of some aspects of Pask’s conversation theory. It is also an instance of the notion of linguistic‐based selected self‐organization here described, and as such it instantiates an open‐ended semiosis between distributed information systems and the communities of users they interact with. This means that the knowledge stored in distributed information resources adapts to the evolving semantic expectations of their users as these select the information they desire in conversation with the information resources. This way, this recommendation system establishes a mechanism for user‐driven knowledge self‐organization.

Considers a conceptual model that leads to the notions of a “distance function” g(t) and that of a “controlled‐disturbance function” δ(t)=h(g(t)). Using these notions we begin a mathematical theory of a system that is self‐organizing to achieve a given state of affairs in a given environment. Obtains, in terms of the functions δ(t) and g(t), a condition under which the system always progresses towards the goal. We also establish the form of expression for the distance function g(t). This comes as a major tool in the proofs of the so‐called goal‐state‐description theorems. These theorems have results that facilitate the determination of the “working functions” of the self‐organizing system (SOS). When they exist, the “working functions” specify a goal‐path for the SOS to learn to adopt.

In the stream of works studying complexity from an organizational viewpoint, literature is focused mainly on describing new organizational forms (holonic organization, circular organization, virtual corporation, …) and on conceptual works identifying new managerial principles to manage emergence (job enrichment, de‐regulation, …). But literature still lacks “actionable knowledge” on management of emergent processes. Therefore, the authors seek to propose an empirical analysis with the aim of finding the organizational design drivers that enable self‐organization. As creativity could be understood as an important precondition for both adaptation and innovation in an age of complexity, the authors focus on creativity emergence process.

The analysis is based on a framework of self‐organization principles. The authors derived it from the literature and used it to empirically analyse open innovation web‐based platforms for creativity. They are deemed particularly important because their open and collaborative innovation process is often self‐organized and their collaboration relationships seem to be loosely coupled. Therefore, they are a good ground to investigate the alternatives to managerial hierarchy and the knowledge‐based organizations associated with emergence and self‐organization.

The results highlight important organizational design choices to enable self‐organization process: organic structure composed by activity systems to enable reconfiguration; roles enlargement and enrichment and cognitive diversity to enable redundancy; collaboration and participatory decision‐making and social capital and networking to enable interconnection; values adaptation, motivations and rules to enable sharing principle.

The paper contributes to the empirical knowledge on emergence process, translating the principles of self‐organization in managerial actions.

This study aims to investigate the relationship between university and industry as two major infrastructures of national innovation system in all leading scientific and industrial settings.

Large complex organizations with high technology that follow non-linear dynamic rules need to define concepts and adopt new approaches to achieve organizational efficiency and effectiveness. Among various models, a dynamic model of innovation was developed based on a joint investment between industry and university. Hence, the concepts of systems thinking and system dynamics were used.

The results reveal three levels of industry and university communication from the lower levels to the higher levels.

The value of this paper lies in adding two axes of “type of relationship” and “form of relationship” to the axis of “strength of relationship”, and a static three-dimensional space as a spatial capacity of the relationship between the industry and the university has been organized. Further, this is the first study that investigates the dynamic relationship between industry and university based on the self-organization theory and system thinking.

The concept of self‐organization proposed by Von Foerster and developed recently by I. Prigogine and the “synergetics” of H. Haken state a framework that promises to be fruitful in the construction of theories synthesizing the microscale and the macroscale points of view in complex systems. This concept may be especially interesting in human sciences, like sociology, where the duality microsociology/ macrosociology remains. Tries to identify explicitly the main features defining the self‐organization of microelements that produce a macroscopic system, and applies the concept to phenomena of social evolution, suggesting a formulation of the micro/macro relationship in social sciences in terms of the probabilistic field theory.

The purpose of this paper is to provide a review of the innovation literature, with special focus on studies applying a complexity perspective. As a contribution in its own right to the innovation literature, the review clarifies the concept of complexity, explores possible points of relevance and the “added value” gained from complexity theory (CT) to the study of innovation, and identifies some of the applications of the theory.

A literature search was conducted which yielded 20 relevant articles. These articles were analyzed by focusing on the key concepts of complexity and studying their applications in the context of innovation research.

Based on the approach adopted, the literature was divided into three categories, namely research focusing on microdynamics, macrodynamics, and leadership and management. The key complexity concepts identified in the innovation literature were “edge of chaos”, “phase shift”, “emergence and self-organization”, “(co)evolution”, and “complexity regulation”. The articles reviewed differed in terms of their perspectives on complexity and, accordingly, their operationalization of the complexity concepts. Key areas of development suggested by the authors include forging a stronger link with existing innovation theory and giving greater weight to empirical evidence.

While a systematic review strategy was adopted to identify all relevant research on “open innovation” and complexity, a selective snowball strategy was deemed the only feasible approach to cover research conducted on “innovation” and complexity.

Practitioners can learn to put CT-based research in context and also learn to recognize the value of CT for innovation management. The authors distilled three important lessons for practice from the research done: embracing complexity, embracing ambidexterity, and embracing failure.

To the best of the authors’ knowledge no review has as yet been undertaken to encapsulate the current state of applications of CT to innovation research.