Science as systems learning. Some reflections on the cognitive and communicational aspects of science

This paper undertakes a theoretical investigation of the 'learning' aspect of science as opposed to the 'knowledge' aspect. The practical background of the paper is in agricultural systems research – an area of science that can be characterised as 'systemic' because it is involved in the development of its own subject area, agriculture. And the practical purpose of the theoretical investigation is to contribute to a more adequate understanding of science in such areas, which can form a basis for developing and evaluating systemic research methods, and for determining appropriate criteria of scientific quality. Two main perspectives on science as a learning process are explored: research as the learning process of a cognitive system, and science as a social, communicational system. A simple model of a cognitive system is suggested, which integrates both semiotic and cybernetic aspects, as well as a model of selfreflective learning in research, which entails moving from an inside 'actor' stance to an outside 'observer' stance, and back. This leads to a view of scientific knowledge as inherently contextual and to the suggestion of reflexive objectivity and relevance as two related key criteria of good science

Toward Multi-Level, Multi-Theoretical Model Portfolios for Scientific Enterprise Workforce Dynamics

Development of theoretically sound methods and strategies for informed science and innovation policy analysis is critically important to each nation's ability to benefit from R&D investments. Gaining deeper insight into complex social processes that influence the growth and formation of scientific fields and development over time of a diverse workforce requires a systemic and holistic view. A research agenda for the development of rigorous complex adaptive systems models is examined to facilitate the study of incentives, strategies, mobility, and stability of the science-based innovation ecosystem, while examining implications for the sustainability of a diverse science enterprise.Agent-Based Model, Complexity, Innovation, Science Studies, Diversity

Historical roots and the evolving science of forest management under a systemic perspective

In recent history, both a growing awareness of how scientific and societal uncertainty impacts management decisions and of the intrinsic value of nature have suggested new approaches to forest management, with a growing debate in forest science over the need for a paradigmatic shift from the classic conventional world view, based on determinism, predictability, and output-oriented management, towards a world view that has roots in complex adaptive systems theory and is consistent with a nature-based ethic. A conceptual framework under this context is provided by systemic silviculture. In this discussion, we analyze how this approach can be linked to three fundamental moments of the history of forestry and forest science: the Dauerwald theory, Gurnaud's control method, and the origins of environmental ethics. Relationships with the recent history of forest management science and current research perspectives are also highlighted.4n

The Systems View of Life: A Unifying Conception of Mind, Matter, and Life

Over the last thirty years, a new systemic understanding of life has emerged at the forefront of science. It integrates four dimensions of life: the biological, the cognitive, the social, and the ecological dimension. At the core of this new understanding we find a fundamental change of metaphors: from seeing the world as a machine to understanding it as a network. One of the most radical philosophical implications of the systems view of life is a new conception of mind and consciousness which, for the first time, overcomes the Cartesian division between mind and matter

Science of problem solving in a historical context

The generality of the ‘systemic or structural view’ of parts of the world is introduced. This leads to the generality of the ‘innate’ problem solving activity of problematic issues in the living sphere associated with survival or homeostasis, achievements of ambitions by chance or purpose and maintenance of isothermal operations. The anomaly of using predominantly qualitative, quantitative properties over the history of human intellectual endeavour for the development of empirical theories in preference of ‘systemic properties’, had they been available, is asserted. One- and two- place, declarative sentences of processed natural language acting as such properties, are suggested. Consideration of the notions of achievement and ambition leads to a ‘problem solving structure’ as the integral part of the ‘New science of systems’ called the ‘Science of problem solving’ or the ‘Science of the 21st century’. Accordingly, the problematic issues can be resolved and the anomaly to disappear. Aristotle’s four causes from the historical background of thought are compared with the problem-solving structure and the problem-solving function and place of conventional science of physics at the object level is discussed leading to a ‘scientific enterprise’. Integration of ancient and modern views has emerged. An example of application of ‘linguistic modelling’ to the problem-solving structure, is given

From Concept to Policy: Building Regional Innovation Systems in Follower Regions

In the spirit of “The Lisbon strategy”, public policies are redirecting support from investment-driven policies to knowledge building as the main driver for competitiveness and innovation. This re-orientation poses different challenges to regions and RIS concept may be the central element, simultaneously goal and toolbox, for devising innovation promotion policies. The RIS framework stresses the need to combine a systemic and inclusive view of innovation along with territorially embedded specificities. In this paper we explore how to operationalize the concept of RIS in terms of innovation policy, arguing against a “one size fits all” approach. Concentrating our analysis on follower regions, we bridge the concept of RIS with the structural deficiencies and challenges posing to this kind of regions, for which innovation policy should seek an adequate combination between science push and demand pull perspectives. We also address the importance of taking advantage of the catching-up status, building upon R&D cost-advantages and clustering around external initiatives as well as the correction of important constraints to the construction of a RIS.Innovation, Regional Innovation Systems, Innovation Policy, Follower Regions

New socio-political environments and the dynamics of European public research systems

The performance of science and technology is being challenged by new socio-political environments. The changes in science policy are influenced by a more systemic view of the understanding on how science and technology evolve. The concept of risk society is mediating the links between science and society. Comparative analyses cast doubts about the possibilities of European institutions to cope with the challenges of the new environment.This paper is based on the work and previous experience of the author and develops some results from the project 'European Comparison of Public Research Systems (EUPSR)', funded by the European Commission TSER programme (contract SOE1-CT96-1036), co-ordinated by J. Senker (SPRU). The author is solely responsible for the work presented in this paper. The support of the EC is gratefully acknowledged as well as that of the Spanish National R&D Plan (SEC97-1382). A preliminary version was presented in the Lisbon Workshop (5-6 June 2000) of the EUROPOLIS project funded by the STRATA Programme

The herd moves? Emergence and self-organization in collective actors?

The puzzle about collective actors is in the focus of this contribution. The first section enters into the question of the adequateness and inadequateness of reductionist explanations for the description of entities. The considerations in this part do not draw on systems and hence not on principles of self-organisation, because this concept necessitates a systemic view. In other words, the first section discusses reductionism and holism on a very general level. The scope of these arguments goes far beyond self-organising systems. Pragmatically, these arguments will be discussed within the domain of corporative actors. Emergence is a concept embedded in system theory. Therefore, in the second part the previous general considerations about holism are integrated with respect to the concept “emergence”. In order to close the argument by exactly characterising self-organising systems and giving the conceptual link between self-organisation and emergence – which is done in the section four – the third section generally conceptualises systems. This conceptualisation is independent of whether these systems are self-organising or not. Feedback loops are specified as an essential component of systems. They establish the essential precondition of system-theoretic models where causes may also be effects and vice versa. System-theory is essential for dynamic models like ecological models and network thinking. In the fourth part mathematical chaos-theory bridges the gap between the presentation of systems in general and the constricted consideration of self-organising systems. The capability to behave or react chaotically is a necessary precondition of self-organisation. Nevertheless, there are striking differences in the answers given from theories of self-organisation in biology, economics or sociology on the question “What makes the whole more than the sum of its parts?” The fracture seems particularly salient at the borderline between formal-mathematical sciences like natural sciences including economy and other social sciences like sociology, for instance in the understanding and conceptualisation of “chaos” or “complexity”. Sometimes it creates the impression that originally well defined concepts from mathematics and natural science are metaphorically used in social sciences. This is a further reason why this paper concentrates on conceptualisations of self-organisation from natural sciences. The fifth part integrates the arguments from a system-theoretic point of view given in the three previous sections with respect to collective and corporative actors. Due to his prominence all five sections sometimes deal with the sociological system theory by Niklas Luhmann, especially in those parts with rigorous and important differences between his conception and the view given in this text. Despite Luhmann’s undoubted prominence in sociology, the present text strives for a more analytical and formal understanding of social systems and tries to find a base for another methodological approach.

Level of Agreement in the Mental Models of Human Factors Practitioners and Systems Engineers Working in Collaborative Teams

Emerging research in complexity science recognizes traditional techniques for engineering systems do not always work for complex systems. Designing complex systems requires individuals to have knowledge of engineering as well as human performance. To this end, design efforts rely often on multi-disciplinary teams. While any two members of a design team may view the system design problem in vastly different manners, this study sought to identify a possible systemic effect on approach by the differing education and experience obtained by social practitioners, represented by human factors, and technical practitioners, represented by systems engineers. It further examined the impact of the complexity of the designed system designed on this systemic effect; in this case, two systems associated with unmanned aircraft systems (UAS). This study relied on measurement of individual mental models, using a graphical brainstorming tool to capture functional decompositions, argued as representing the problem domain component of an individual mental model. This study compared individual functional decomposition models against an average model composed from the same educational specialty, and from an average model composed from the opposite educational specialty. Participants developed models for a simple/closed problem and an open/complex problem. The researcher conducted a repeated measures multivariate analysis of variance on the effects of domain, problem type and the interaction between the two, as well as with interactions with educational specialty. The results indicated higher agreement among mental models when individuals were compared to the average model from their same specialty, that more agreement in mental models occurred in relation to the simple/closed problem than in relation to the open/complex problem, and that open/complex problems can exacerbate the level of mental model dis-agreement among team members with different educational backgrounds