Topics
Each part of the Guide to the SE Body of Knowledge (SEBoK) is divided into KAs, which are groupings of information with a related theme.
Systems Science brings together research into all aspects of systems with the goal of identifing, exploring, and understanding patterns of complexity which cross disciplinary fields and areas of application. It seeks to develop interdisciplinary foundations which can form the basis of theories applicable to all types of systems, independent of element type or application; additionally, it could form the foundations of a meta-discipline unifying traditional scientific specialisms.
An article on the History of Systems Science article describes some of the important multidisciplinary fields of research of which systems science is composed.
A second article presents and contrasts the underlying theories behind some of the system approaches taken in applying systems science to real problems.
People who think and act in a systems way are essential to the success of both research and practice. Successful systems research will not only apply systems thinking to the topic being researched but should also consider a systems thinking approach to the way the research is planned and conducted. It would also be of benefit to have people involved in research who have, at a minimum, an awareness of system practice and ideally are involved in practical applications of the theories they develop.
History of Systems Science
This article is part of the Systems Science Knowledge Area. It describes some of the important multidisciplinary fields of research comprising systems science in historical context.
Systems science, is an integrative discipline which brings together ideas from a wide range of sources which share a common systems theme. Some fundamental concepts now used in systems science have been present in other disciplines for many centuries, while equally fundamental concepts have independently emerged as recently as 40 years ago (Flood and Carson 1993).
The “Systems Problem”
Questions about the nature of systems, organization, and complexity are not specific to the modern age. As International Council on Systems Enginneering (INCOSE) pioneer and former International Society for System Sciences (ISSS) President John Warfield put it, “Virtually every important concept that backs up the key ideas emergent in systems literature is found in ancient literature and in the centuries that follow.” (Warfield 2006) It was not until around the middle of the 20th Century, however, that there was a growing sense of a need for, and possibility of a scientific approach to problems of organization and complexity in a “science of systems” per se.
The explosion of knowledge in the natural and physical sciences during the 18th and 19th centuries had made the creation of specialist disciplines inevitable: in order for science to advance, there was a need for scientists to become expert in a narrow field of study. The creation of educational structures to pass on this knowledge to the next generation of specialists perpetuated the fragmentation of knowledge (M’Pherson 1973). This increasing specialization of knowledge and education proved to be a strength rather than a weakness for problems which were suited to the prevailing scientific methods of experimental isolation and analytic reduction. However there were areas of both basic and applied science that were not adequately served by those methods alone.
The systems movement has its roots in two such areas of science: the biological-social sciences, and a mathematical-managerial base stemming first from cybernetics and operations research, and later from organizational theory.
Biologist Ludwig von Bertalanffy was one of the first to argue for and develop a broadly applicable scientific research approach based on Open System Theory (Bertalanffy 1950). He explained the scientific need for systems research in terms of the limitations of analytical procedures in science.
These limitations, often expressed as emergent evolution or "the whole is more than a sum of its parts”, are based on the idea that an entity can be resolved into and reconstituted from its parts, either material or conceptual:
This is the basic principle of "classical" science, which can be circumscribed in different ways: resolution into isolable causal trains or seeking for "atomic" units in the various fields of science, etc.
He stated that while the progress of "classical" science has shown that these principles, first enunciated by Galileo and Descartes, are highly successful in a wide realm of phenomena, but two conditions are required for these principles to apply:
The first is that interactions between "parts" be non-existent or weak enough to be neglected for certain research purposes. Only under this condition, can the parts be "worked out," actually, logically, and mathematically, and then be "put together." The second condition is that the relations describing the behavior of parts be linear; only then is the condition of summativity given, i.e., an equation describing the behavior of the total is of the same form as the equations describing the behavior of the parts These conditions are not fulfilled in the entities called systems, i.e. consisting of parts "in interaction" and description by nonlinear mathematics. These system entities describe many real world situations:populations, eco systems, organizations and complex man made technologies.
The methodological problem of systems theory is to provide for problems beyond the analytical-summative ones of classical science (Bertalanffy 1968, pp. 18-19).
Bertalanffy also cited a similar argument by mathematician and co-founder of information theory Warren Weaver in a 1948 American Scientist article on “Science and Complexity”. Weaver had served as Chief of the Applied Mathematics Panel at the U.S. Office of Scientific Research and Development during WWII. Based on those experiences, he proposed an agenda for what he termed a new “science of problems of organized complexity”. Weaver explained how the mathematical methods which had led to great successes of science to date were limited to problems where appropriate simplifying assumptions could be made.
What he termed “problems of simplicity” could be adequately addressed by the mathematics of mechanics, while “problems of disorganized complexity” could be successfully addressed by the mathematics of statistical mechanics. But with other problems, making the simplifying assumptions in order to use the methods would not lead to helpful solutions. Weaver placed in this category problems such as, how the genetic constitution of an organism expresses itself in the characteristics of the adult, and to what extent it is safe to rely on the free interplay of market forces if one wants to avoid wide swings from prosperity to depression. He noted that these were complex problems which involved “analyzing systems which are organic wholes, with their parts in close interrelation.”
These problems-and a wide range of similar problems in the biological, medical, psychological, economic, and political sciences-are just too complicated to yield to the old nineteenth century techniques which were so dramatically successful on two-, three-, or four-variable problems of simplicity. These new problems, moreover, cannot be handled with the statistical techniques so effective in describing average behavior in problems of disorganized complexity [problems with elements exhibiting random or unpredictable behaviour].
These new critical global problems require science to make a third great advance,
An advance that must be even greater than the nineteenth-century conquest of problems of simplicity or the twentieth-century victory over problems of disorganized complexity. Science must, over the next 50 years, learn to deal with these problems of organized complexity [problems for which complexity “emerges” from the coordinated interaction between its parts]. (Weaver, 1948)
Weaver identified two grounds for optimism in taking on this great challenge: 1) developments in mathematical modeling and digital simulation, and 2) the success during WWII of the “mixed team” approach of operations analysis, where individuals from across disciplines brought their skills and insights together to solve critical, complex problems.
The importance of modeling and simulation and the importance of working across disciplinary boundaries have been the key recurring themes in development of this “third way” science for systems problems of organized complexity.