Newtonian Paradigm Research Articles

Dynamic models of evolving systems

AbstractThe basis of systems modeling has been the Newtonian paradigm that states that the behavior of a system can be understood and anticipated by identifying its components and the causal links between them. This assumption leads to a set of deterministic differential or difference equations that governs the behavior of the system. However, such description is achieved by classifying elements into categories and supposing that only the most probable events in fact occur. But real systems evolve, that is, they add and subtract mechanisms, components, and interactions over time; the deterministic model does not reflect this. Clearly, evolution must therefore result from what has been removed in the reduction process. If we are to understand evolving systems better, to anticipate structural changes, and to explore the real impacts of decisions, we must study the effects on system dynamics of nonaverage behavior. We can then gain new insights into the nature of evolutionary processes and build better models that include the adaptive responses from within the system.

When is a mechanism not a mechanism? The network thermodynamic approach to complex systems

Rosen (1985) has made a distinction between two classes of objects, “mechanisms” and “complex systems”. One central distinguishing feature is the applicability of the Newtonian paradigm. Mechanisms are describable with this formalism, while complex systems, such as living systems, are not. Rosen maintains that living systems can, at best, be given a very local description by the use of mechanisms. This categorization is potentially very useful and can provide insight into the dominant roles played by both empiricism and reductionism in modern biology (Mikulecky, 1987). Reductionism is methodologically motivated by the absence of a theory of complex systems in today's Physics and Chemistry. Systems must be reduced to simple mechanisms in order to be tractable in terms of the theory provided by those incomplete disciplines. Complex systems have been most thoroughly studied in engineering. Network thermodynamics uses theory originating in engineering and applies it in a general way to the complexity of topological connectedness in a dynamic system. Although it begins with elements which are obviously mechanisms in Rosen's language, it quickly illustrates that the whole is much more than the mere sum of its parts. This work uses multiport network elements which represent some of the most common energy conversion processes in living systems to rigorously establish that simple mechanisms begin to “emerge” in the sense that the composite system can behave in new ways, none of which are characteristics of its constituent elements. The long term goals of these efforts are two fold: 1) to establish a network or circuit theory for the dynamic events observed in living systems, and 2) to identify in specific examples the transition from mechanistic to complex system behavior.

‘‘Aristotelian’’ was given as the answer, but what was the question?

Students come to physics instruction, in Newtonian mechanics, with naive ideas about how and why things move; for example, many believe that force causes motion and, conversely, that in the absence of a force things are necessarily at rest. Moreover, such preconceived ideas interfere with the ability of students to learn, and function within, the Newtonian paradigm. These student ideas have been labeled ‘‘Aristotelian,’’ a catchy label that has rapidly become widely accepted. This paper shows that these student beliefs are not like those of Aristotle, questions the hypothesis that generated the label, and describes a danger, to the sound growth of knowledge, inherent in the use of theory-ladened nomenclature. The purpose of the paper is to suggest that the label be abandoned so that this important line of research, with its profound implications for teachers, may develop unhindered by the weight of incorrect, theory-driven nomenclature.