Abstract
Systems science provides a somewhat unique mode of inquiry in revealing not just how one kind of system, say a biological system, works, but rather how all kinds of systems work. That is, it looks at what is common across all kinds of systems in terms of form and function. In this sense, it is a metascience, something that informs all other sciences that deal with particular kinds of systems. In this chapter, we describe the attributes that all systems share in common. We identify 12 non-exclusive principles that apply to all or most systems of significant interest. These principles provide the guidance for the rest of the book.
Surveying the evolution of modern science, we encounter a surprising phenomenon. Independently of each other, similar problems and conceptions have evolved in widely different fields.
Ludwig von Bertalanffy , 1969, 30
A new view of the world is taking shape in the minds of advanced scientific thinkers the world over, and it offers the best hope of understanding and controlling the processes that affect the lives of us all. Let us not delay, then, in doing our best to come to a clear understanding of it.
Ervin Laszlo , 1996, viii
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Notes
- 1.
For the classic introduction to chaos theory , see Gleick (1987).
- 2.
The term “self-organizing” is the most common reference to processes whereby components in a system tend to form stable linkages (see Chap. 10). We left this term usage here because that is what Prigogine and most other authors have used. The use of the word “self,” however, may carry a little too much emotive baggage and possibly convey a denotation of mental intention. For example, one could easily and innocently attach the notion of components “wanting” to interlink in such-and-such a manner, leading to a stable configuration. Throughout the rest of the book, we use an alternate term “auto-organization ” to mean what most researchers mean by self-organization . This term does not seem to carry any sense of a mental process and more correctly, in our view, labels the nature of the process of organization without outside manipulations taking place.
- 3.
- 4.
For much of that time we (Kalton and Mobus ) worked independently and only discovered our mutual understanding of principles a few years before agreeing to tackle this textbook project. The principles outlined here are the result of integrating our convergent ideas.
- 5.
Arthur Koestler (1905–1983) used the term “holarchy” to describe this fundamental structuring of systems. See Koestler (1967).
- 6.
One might think there is a potential problem with infinite regress in this definition. We address this in Principle 2 and later in the book. There are reasonable stopping conditions in both directions of analysis . Practically speaking, however, most systems of interest will not require approaching those conditions.
- 7.
Software, as in computer programs and accouterments such as relational data sets, are an offshoot of systems in the abstract , but with a more direct causal relation with systems in the world . For example, a robot can have causal influence over objects in physical reality. Or a program can influence how a user reacts to situations.
- 8.
The relationship between a network and a map should be really clear. The word “map” is used generically to refer to any graphic representation of relations between identified components . A map of a state or country is just one example of such a network representation, as the network of roads that connect cities, etc.
- 9.
For an extensive analysis of the dynamics of development and collapse in human and natural systems, see Gunderson and Holling (2002).
- 10.
The second law will show up many times throughout this book so it would be worthwhile for the reader to take some time to study its physical basis. The second law describes the way in which energy has a tendency to “diffuse” throughout a system or degrade to low temperature heat from which no additional work can be obtained. See http://en.wikipedia.org/wik/Laws_of_thermodynamics for a general overview of the laws of thermodynamics.
- 11.
- 12.
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Mobus, G.E., Kalton, M.C. (2015). A Helicopter View. In: Principles of Systems Science. Understanding Complex Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1920-8_1
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