Online research in philosophy

Using the concept of adjunction, for the comprehension of the structure of a complex system, developed in Part I, we introduce the notion of covering systems consisting of partially or locally defined adequately understood objects. This notion incorporates the necessary and sufficient conditions for a sheaf theoretical representation of the informational content included in the structure of a complex system in terms of localization systems. Furthermore, it accommodates a formulation of an invariance property of information communication concerning the analysis of a complex system.

The present issue of Mind and Matter on the concept of 'pragmatic information' has originated from a frutiful collaboration with Peter beim Graben,whose active involvement as a co-editor was decisive for its pro- duction and is greatly appreciated. The following extended editorial intro- duces the topic within a broader background. In particular,the concept of pragmatic information will be related to the study of complex systems and to concepts of complexity that are not in detail addressed in the in- dividual contributions to the issue.Finally,possible connections to an epistemically understood distinction of mental and material domains of discourse will be indicated.

Systems involving many interacting variables are at the heart of the natural and social sciences. Causal language is pervasive in the analysis of such systems, especially when insight into their behavior is translated into policy decisions. This is exemplified by economics, but to an increasing extent also by biology, due to the advent of sophisticated tools to identify the genetic basis of many diseases. It is argued here that a regularity notion of causality can only be meaningfully defined for systems with linear interactions among their variables. For the vastly more important class of nonlinear systems, no such notion is likely to exist. This thesis is developed with examples of dynamical systems taken mostly from mathematical biology. It is discussed with particular reference to the problem of causal inference in complex genetic systems, systems for which often only statistical characterizations exist.

The complex and dynamic nature of systems pose a particular challenge to researchers and require the use of a research methodology designed to deal with such systems. The properties of fit, relevance, understandability, generality, control, workability, generalizability, and modifiability make Glaserian grounded theory and grounded action particularly well suited for studying systems. These methods are innovative, systemic, and sophisticated enough to reveal the underlying complexities of systems and plan actions that address their complex, dynamic nature while remaining grounded in what is occurring within the systems as they change over time.

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.

Nature essentially consists of complex systems. The paper presents a conceptual framework to understand how complex systems interact with each other in nature. All natural systems are thermodynamically open and physically adaptive. The process of adaptation and continual self-organization cause these systems to interact continuously with the environment and compete against such similar systems for limited resources.

In this chapter we want to provide philosophical tools for understanding and reasoning about complex systems. Classical thinking, which is taught at most schools and universities, has several problems for coping with complexity. We review classical thinking and its drawbacks when dealing with complexity, for then presenting ways of thinking which allow the better understanding of complex systems. Examples illustrate the ideas presented. This chapter does not deal with specific tools and techniques for managing complex systems, but we try to bring forth ideas that facilitate the thinking and speaking about complex systems.

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..

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.

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.