Online research in philosophy

The challenges posed by chronic illness have pointed out to epidemiologists the multifactorial complex nature of disease causality. This notion has been referred to as a web of causality. This web extends theoretically beyond risk markers. It includes determinants of emergence/non-emergence of disease. This web of determinants is a form of complex system. Due to its complexity, the determinants within such system are not linked to each others in a linear, predictable manner only. Predictability is possible only on a short-term basis, and unpredictability sets in over the long run. Understanding such a system of determinants calls for articulation and testing of complex models which synthesize our knowledge of multiple determinants at many scales, both biological and otherwise. Given the complexity of this web and existing knowledge about the nonlinearity of such systems, the following question is posed: Can the challenge of studying causality be adequately addressed if emphasis continues to be placed on using tools and methods that are geared towards looking at such system from a linear paradigm? Or is it time to add to the epidemiologic research agenda the notion of nonlinearity and its relevant form of analytical approaches that are being tested in other disciplines? Furthermore, the question posed here applies as well to the study of determinants of health. Addressing determinants of heath adds further complexity to our task.

One of the most powerful tools in science is the analytic method, whereby we seek to understand complex systems by studying simpler sub-systems from which the complex is composed. If this method is to be successful, something about the sub-systems must remain invariant as we move from the relatively isolated conditions in which we study them, to the complex conditions in which we want to put our knowledge to use. This paper asks what this invariant could be. The paper shows that the kinds of thing that a Humean might point to – behaviour, laws, and dispositions – cannot play the role required of the invariant in question. Nor, indeed, can non-Humean causal powers of the kind advocated by contemporary metaphysicians such as Ellis and Lierse. The paper suggests that the analytic method presupposes a kind of entity that does not appear in standard ontologies – a metaphysically substantial notion of causal influence. This notion of causal influence is one that Cartwright has also seen the need for, though she does not seem to take the notion as seriously as she should.

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.

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.

In the context of technology development and systems engineering, knowledge is typically treated as a complex information structure. In this view, knowledge can be stored in highly sophisticated data systems and processed by explicitly intelligent, software-based technologies. This paper argues that the current emphasis upon knowledge as information (or even data) is based upon a form of rationalism which is inappropriate for any comprehensive treatment of knowledge in the context of human-centred systems thinking. A human-centred perspective requires us to treat knowledge in human terms. The paper sets out the particular importance of tacit knowledge in this regard. It sets out two case studies which reveal the critical importance of a careful treatment of tacit knowledge for success in two complex, technology-driven projects.

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.

Charles Perrow used the term normal accidents to characterize a type of catastrophic failure that resulted when complex, tightly coupled production systems encountered a certain kind of anomalous event. These were events in which systems failures interacted with one another in a way that could not be anticipated, and could not be easily understood and corrected. Systems of the production of expert knowledge are increasingly becoming tightly coupled. Unlike classical science, which operated with a long time horizon, many current forms of expert knowledge are directed at immediate solutions to complex problems. These are prone to breakdowns like the kind discussed by Perrow. The example of the Homestake mine experiment shows that even in modern physics complex systems can produce knowledge failures that last for decades. The concept of knowledge risk is introduced, and used to characterize the risk of failure in such systems of knowledge production.

The capacity to engage in systems thinking is often viewed as being a product of being able to understand complex systems due to one's facility in mastering systems theories, methods, and being able to adeptly reason. Relatively little attention is paid in the systems literature to the processes of learning from experience and creating knowledge as a direct consequence of individuals engaging systems thinking itself over time. In fact, the potential efficacy of systems thinking to improve performance normally seen as only contingent on a priori knowledge, rather than knowledge created via learning from experience. Such newly create knowledge often results from engaging in modeling efforts and systemic forms of inquiry. This article proposes a model for creating new knowledge by coupling systems modeling with a pragmatic approach to knowledge-creation. This approach is based on a foundation of the pragmatic concepts first proposed by the American philosopher/scientist Charles Sanders Peirce over a century ago. This model offers systems practitioners a framework to engage in knowledge-intensive systems thinking (KIST) for addressing complex problematic issues.

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..

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.