Online research in philosophy

Main principles of the complex nonlinear thinking which are based on the notions of the modern theory of evolution and self-organization of complex systems called also synergetics are under discussion in this article. The principles are transdisciplinary, holistic, and oriented to a human being. The notions of system complexity, nonlinearity of evolution, creative chaos, space-time definiteness of structure-attractors of evolution, resonant influences, nonlinear and soft management are here of great importance. In this connection, a prominent contribution made to system analysis and to a necessary reform of education and thinking by Edgar Morin is considered.

The grounded theory research method embodies a crucial element of postmodernist thinking due to its aversion to theory verification and its ability to imbue analysts with the power to discover theory. These processes closely mirror systems thinking because they allow for holistic examination. Postmodern systems thinking combines the worldview of postmodernism with systems thinking, creating a mechanism that is both respectful to the variations of human interaction and the need for "de-compartmentalizing" complex systems. The postmodern systems thinking framework united with grounded action research could be a potent approach for the dissemination of localized, culture-specific ideas in countries susceptible to Western hegemony.

As science, knowledge, and ideas evolve and are increased and refined, the branches of philosophy in charge of describing them should also be increased and refined. In this work we try to expand some ideas as a response to the recent approach from several sciences to complex systems. Because of their novelty, some of these ideas might require further refinement and may seem unfinished, but we need to start with something. Only with their propagation and feedback from critics they might be improved. We make a brief introduction to complex systems, for then defining abstraction levels. Abstraction levels represent simplicities and regularities in nature. We make an ontological distinction of absolute being and relative being, and then discuss issues on causality, metaphysics, and determinism.

A short review of complexity research from the perspective of history and philosophy of biology is presented. Complexity and its emergence has scientific and metaphysical meanings. From its beginning, biology was a science of complex systems, but with the advent of electronic computing and the possibility of simulating mathematical models of complicated systems, new intuitions of complexity emerged, together with attempts to devise quantitative measures of complexity. But can we quantify the complex?

Biology deals, notoriously, with complex systems. In discussing biological methodology, all three papers in this symposium honor the complexity of biological subject matter by preferring models and theories built to reflect the details of complex systems to models based on broad general principles or laws. Rheinberger's paper, the most programmatic of the three, provides a framework for the epistemology of discovery in complex systems. A fundamental problem is raised for Rheinberger's epistemology, namely, how to understand the referential continuity of the theoretical terms and concepts employed in typical case studies involving complex systems.

Here, for the first time, development studies encounters the set of ideas popularly known as 'Chaos Theory'. Samir Rihani applies to the processes of economic development, ideas from complex adaptive systems like uncertainty, complexity, and unpredictability. Rihani examines various aspects of the development process - including the World Bank, debt, and the struggle against poverty - and demonstrates the limitations of fundamentally linear thinking in an essentially non-linear world.

Complex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this thesis I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by actively interacting among themselves. The main premise of the methodology claims that reducing the “friction” of interactions between elements of a system will result in a higher “satisfaction” of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while practical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for improving communication within self-organizing bureaucracies are advanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are discussed. Philosophical implications of the conceptual framework are also put forward.

All complex systems are complex, but some are more complex than others are. Biological systems are generally more complex than physical systems. How do biologists tackle complex systems? In this talk, we will consider two biological systems, the genome and the brain. Scientists know much about them, but much more remains unknown. Ignorance breeds philosophical speculation. Reductionism makes a strong showing here, as it does in other frontier sciences where large gaps remain in our understanding. I will show that reductionism and its claims have no bases in actual scientific research and results. The Human Genome Project will serve as a case in point..

This paper explores the meaning and possibility of a theory of knowledge vis-a-vis the non linear complex systems. The thesis hereafter defended is that knowledge of complex systems has to do more with possibilities than with factual reality. Therefore, knowledge is characterized by incompletness, incomputability and randomness, and so computer acquires a relevant role in the study of complex systems. Towards the end, the place and the very complexity of human beings as a complex problem is considered.

Introduction to complexity and complex systems -- Introduction to large linear systems -- Introduction to biochemical oscillators and nonlinear biochemical systems -- Modularity, redundancy, degeneracy, pleiotropy and robustness in complex biological systems -- The evolution of biological complexity; invertebrate immune systems -- Irreducible and specified complexity in living systems -- The complex adaptive and innate human immune systems -- Complexity in quasispecies : microRNAs -- Introduction to complexity in economic systems -- Complexity in quasispecies : micrornas -- Dealing with complexity.