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Systems science vs cybernetics
CHAPTER 1
Systems science vs cybernetics 1.1 General systems theory General systems theory (GST) is a science investigating general laws for arbitrarily complex arrangements—“systems”—which constitute functional integrities. The origin of the general systems theory is associated with the publication in 1928 of a seminal book (Von Bertalanffy, 1928, 1968) titled Kritische Theorie der Formbildung, authored by eminent Austrian biologist and philosopher Ludwig von Bertalanffy (1901–71). General systems theory (GST) is linked with cybernetics and information theory. Rigorous (axiomatic) mathematical formulation for GST was provided by Mesarovic and his collaborators (Mesarovic, 1964; Mesarovic and Macko, 1970; Mesarovic and Takahara, 1975, 1988), whereas important contributions to developments of systems theory were made by G.J. Klir in the field of computer methods of solving various systemic problems (Klir, 1969, 1972, 1981, 1985, 1992; Klir and Folger, 1987) and J.G. Miller in the field of living systems theory (Miller, 1978). The most typical ingredients (components) of the systems theory include: basic definitions, system thinking, system topologies, life cycles, system performance, conceptual design, current state evaluations, related sciences, solving methods, creative solutions, system synthesis, system analysis, optimization, solution assessment, virtual optimizing, system engineering, and evaluation of knowledge in economy and society. Contents of university courses usually involve: the significance of proper modeling and the role of identification and control aspects for systems of various structure. These issues are analyzed and the importance of computers in applications is evaluated.
1.2 Cybernetics and systems science in academics The basic concepts of cybernetics have proven to be very powerful in a variety of disciplines: computer science, management, sociology, biology, thermodynamics, etc. Cybernetics and systems science link the abstraction of philosophy and mathematics with the concreteness of (dealing with) the theory and modeling of real world systems. As exemplarily interdisciplinary sciences, cybernetics and systems science work between and among rudimentary disciplines, usually pairwise, but occasionally across more than two kinds of systems. Complexity and Complex Thermo-Economic Systems. https://doi.org/10.1016/B978-0-12-818594-0.00001-5 # 2020 Elsevier Inc. All rights reserved.
1
2
Chapter 1
Some recent approaches have their origins in ideas and concepts proposed by scientists many years ago: for example, neural networks, artificial intelligence, human-machine interfaces, organized therapies, etc. In these approaches, the majority of basic concepts and problems have already been formulated, mainly in the period of years 1940–80, by cyberneticians such as Von Bertalanffy (1928, 1968), Wiener (1948), Ashby (1952), Boulding (1978), Forrester (1961), Von Foerster (1979), Von Neumann (1958), McCulloch (1965), and Pattee (1973). Since their founding, cybernetics and systems science have struggled to achieve a level of respectability in the academic society. Cyberneticians especially have found it difficult to find their homes in academic institutions or to create their own. Presently, a number, perhaps still insufficient, of academic programs in cybernetics and systems science (CSS) exist, and those working in the new disciplines described seem not to always remember their cybernetic predecessors. What is the reason that the progress in the popularity of cybernetics is so slow? The difference between cyberneticians and researchers in the previously mentioned areas is that “the former stubbornly stick to their objective of building general, domain independent theories, whereas the latter focus on very specific applications, such as: expert systems, psychotherapy, thermodynamics, pattern recognition, etc. General integration of the former researchers is quite abstract, hence it is not sufficiently easy to be really appreciated” (Joslyn and Heylighen, 1992). As a common interdisciplinary field, cybernetics and systems science (CSS) offers common concepts used in multiple traditional disciplines and attempts to achieve a logically consistent unification by finding common terms for similar concepts in these multiple disciplines. Thus, CSS unifies individual concepts, theories, and terminologies in a specific discipline directed toward general, and perhaps idiosyncratic tools (usages). These new conceptual categories may not be recognizable to the traditional researchers, or they may find no reason in the use of the general concepts (Joslyn and Heylighen, 1992). Clearly, the problem of building a global theory is much more complex than any of the more down-to-earth goals of the fashionable approaches (Joslyn and Heylighen, 1992). But we may also say that the generality of the problem is dangerous in itself if it leads to being “stuck” in abstractions which are so far removed from everyday world that it is difficult to use them, interact with them, or test them on concrete problems; in other words, “to get a feel for how they behave and what their strengths and weaknesses are” (Joslyn and Heylighen, 1992). Although there are many exceptions, researchers in cybernetics and systems science tend to be trained in a traditional specialty (like biology, management, or psychology) and then come to apply themselves to problems in other areas, perhaps a single other area. Thus, their exposure to cybernetics and systems science concepts and theory tends to be somewhat ad hoc and specific to the two or three fields they apply themselves to (Joslyn and Heylighen, 1992). However, this
Systems science vs cybernetics 3
Capillaries in the lungs Pulmonary vein Pulmonary artery
Pulmonary circulation
Left atrium
Heart
Right atrium Right ventricle Left ventricle Aorta (main artery) Vena cava (main vein) Systemic circulation
Capillaries in the body Red: Oxygen-rich blood Blue: Oxygen-poor blood
Fig. 1.1 The human cardiovascular system.
doesn’t mean that the tremendous research record and great achievements of last years (especially those in biology after the discovery of DNA) are not appreciated enough. Rather, a much longer period is expected for inclusion of basic biological and biochemical systems into the general scheme of cybernetics and systems science (Voet and Voet, 1995; Menche, 2016; Schmidt et al., 2017). Fig. 1.1, which schematizes the human cardiovascular system, is an example of a very large number of complex biological subsystems found in living organisms. The blood circulatory system (cardiovascular system) consists of the heart and the blood vessels running through the entire body. The arteries carry blood away from the heart; the veins carry it back to the heart. There isn’t only one blood circulatory system in the human body, but two, which are connected: The systemic circulation provides organs, tissues, and cells with blood so that they get oxygen and other vital substances. The pulmonary circulation is where the fresh oxygen we breathe in enters the blood. At the same time, carbon dioxide is released from the blood (Menche, 2016; Schmidt et al., 2017).
1.3 Relation to other disciplines Ideas related to the domain of cybernetics and systems are firstly used in the emerging “sciences of complexity,” also called “complex adaptive systems,” studying selforganization and heterogeneous networks of interacting entities (e.g., the work of the Santa Fe Institute,
4
Chapter 1
Joslyn and Heylighen, 1992), and, secondly, in an associated research in the natural sciences such as far-from equilibrium thermodynamics, stability theory, catastrophe theory, chaos, and dynamical systems. An example is a bifurcation diagram of the stability theory, Fig. 1.2, which shows how the change of the control parameter C influences the change of the stability properties of the system. A third strand is constituted by different high-level computing applications such as artificial intelligence, neural networks, man-machine interaction, and computer modeling and simulation. Unfortunately, few practitioners in these recent disciplines seem to be aware that many of their concepts and methods have been proposed and used by cyberneticians for many years (Joslyn and Heylighen, 1992). Subjects like complexity, selforganization, connectionism, and X
(b1)
Stable
(a)
Stable
(a¢)
Unstable
Thermodynamic branch
Unique solution
Stable (b2) Multiple solutions
Fig. 1.2 Bifurcation diagram showing how the state variable X is affected when an arbitrary control parameter varies along the horizontal direction: the unique solution (a), the thermodynamic branch loses its stability at the point where the curvilinear boundary line touches the vertical straight line separating a unique solution and multiple solutions. At this value of control parameter, new branches of solutions (b1, b2), which are stable as implied by this example, are generated. Based on Nicolis, G., Prigogine, I., 1989. Exploring Complexity. W.H. Freeman. p. 72.
Systems science vs cybernetics 5 adaptive systems have already been extensively studied, in the 1940s and 1950s, by researchers like Wiener, Ashby, von Neumann, and von Foerster, and in discussion forums like the famous Josiah Macy meetings on cybernetics (Heims, 1991). Some popularizing books on “the sciences of complexity,” e.g., Waldroop (1993), seem to ignore the fact, creating the false impression that work on complex adaptive systems only started in earnest with the creation of the Santa Fe Institute in the 1980s (Joslyn and Heylighen, 1992).
1.4 What are cybernetics and systems science? Cybernetics and systems science (also “general system theory” or “system’s research”) constitutes a somewhat fuzzily defined academic domain which touches upon virtually all traditional disciplines, from mathematics, technology, and biology to philosophy and social sciences. It is more specifically related to the recently developing “sciences of complexity,” including artificial intelligence (AI), neural networks (NN), dynamical systems, chaos, and complex adaptive systems. The history of “sciences of complexity” dates back to the 1940s and 1950s when thinkers such as Wiener, von Bertalanffy, Ashby, and von Foerster founded the domain through a series of interdisciplinary meetings. Systems theory or system science argues that however complex or diverse the world that we experience is, we will always find different types of organizations in it, and such organizations can be described by concepts and principles which are independent from the specific domain at which we are looking. Hence, if we were to uncover these general laws, we would be able to analyze and solve problems in any domain, pertaining to any type of system. The system approach distinguishes itself from the more traditional analytical approach by emphasizing the interactions and connectedness of different components of a system. Although the system approach in principle considers all types of systems, in practice, it focuses on more complex adaptive, self-regulating systems which we might call cybernetic (Heylighen et al., 1999). Many concepts used by system scientists come from closely related approach of cybernetics: information, control, feedback, communication, etc. Cybernetics, derived from the Greek word for steersman (kybernetes), was first introduced by the mathematician Wiener, as the science of communication and control in the animal and the machine (to which we now might add: in social society and in individual human beings). It grew out of Shannon’s information theory, which was designed to optimize the transmission of information through communication channels and the feedback concept used in engineering control systems. See various schemes of a communication channels in Shannon and Weaver (1949) or other books on the information theory. In the present incarnation of “second order cybernetics,” the emphasis is on how observers construct models of the systems with which they interact (c.f. constructivism).
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Chapter 1
In fact, cybernetics and systems theory study essentially the same problem. The problem is organization independent of the substrate in which it is embodied. Insofar as it is meaningful to make a distinction, systems theory is focused more on the structure of systems and their models, whereas cybernetics is focused on how systems function, that is to say how they control their actions and how they communicate with other systems or with their own components. Since structure and function of a system cannot be understood in separation, it is clear that cybernetics and systems theory should be viewed as two facets of a single approach (Heylighen et al., 1999). This insight has resulted in the two domains almost merging in practice: many, if not most, of the central associations, journals, and conferences in the field include both terms, systems and cybernetics, in their title. In spite of the absence of strict subdivisions, though, the field is rather fragmented with many different approaches, similar in some respects, different in others, existing side-by-side (Heylighen et al., 1999). Many “schools,” such as autopoietic systems, anticipatory systems, living systems, viable systems, or soft systems, are associated with a particular theorist or thinker, respectively Maturana and Varela (1987), Rosen (1970), Miller (1978), Beer (1975), Turchin (1997). As a result, occasionally, the cybernetics and systems domain still lacks clear foundations. Yet, the authors of the Principia Cybernetica Project (Heylighen et al., 1999), believe that “the commonalities are much larger than the differences,” and, therefore, it is worthwhile attempting to integrate different approaches in a common conceptual network. While the present chapter cites many statements discussed in the text of Heylighen et al. (1999), some important issues contained therein were omitted for brevity. This pertains, in particular, to information about outside internet links. Some very good, readable introductory books on cybernetics and systems can be downloaded from the Principia Cybernetica Library (Heylighen et al., 1999). Together with a dictionary and bibliography of basic books and papers, this should be sufficient for at least an introductory course in the domain. Optimization of energy systems is covered in three editions of the book written by the present author with Jacek Jez˙owski (Sieniutycz and Jez˙owski, 2009, 2013, 2018); for a broader perspective, a comprehensive review of various thermodynamic approaches to practical systems should be helpful (Sieniutycz, 2016). Occasionally useful are sources and links to diverse articles, books, and websites, which provide further information and references. In particular, diverse sources are essential for understanding basic problems of selforganization. Summing up, in this introduction, typical ingredients of the systems theory are specified and its link with cybernetics is pointed out. General systems theory (GST) is defined as a science investigating general laws for topologically complicated arrangements—“systems”—which constitute the functional integrities. This introductory chapter provides the reader with both a historical outline and brief state-of-art of the GST a well as the list of the scientists who
Systems science vs cybernetics 7 contributed to the birth of the field and to the development of its mathematical backgrounds (in particular to the development of computer methods and indication of their applicative potential).
References Ashby, R., 1952. Design for a Brain. Wiley, New York. Beer, S., 1975. Platform for Change. Wiley, Chichester. Boulding, K., 1978. Ecodynamics. Sage, Beverly Hills. Forrester, J.W., 1961. Industrial Dynamics. Appendix E. MIT Press, Cambridge, MA. Heims, S., 1991. The Cybernetics Group. MIT Press, Cambridge, MA. Heylighen, F., Joslyn, C., Turchin, 1999. What are Cybernetics and Systems Science? http://pespmc1.vub.ac.be/ CYBSWHAT.html. Joslyn, C., Heylighen, F., 1992. Cybernetics and Systems Science in Academics. http://pespmc1.vub.ac.be/ CYBSACAD.html. Klir, G., 1969. An Approach to General Systems Theory. Van Nostrand, New York. Klir, G. (Ed.), 1972. Trends in General Systems Theory. Wiley, New York. Klir, G., 1981. Special issue on reconstructibility analysis. Int. J. Gen. Syst. 7 (1), 1–107. Klir, G., 1985. Architecture of Systems Problem Solving. Plenum, New York. Klir, G., 1992. Facets of Systems Science. Plenum, New York. Klir, G., Folger, T., 1987. Fuzzy Sets, Uncertainty, and Information. Prentice Hall, Englewood Cliffs. Maturana, H.R., Varela, F., 1987. The Tree of Knowledge—The Biological Roots of Human Understanding. Shambala Publications, Boston. McCulloch, W., 1965. Embodiments of Mind. MIT Press, Cambridge, MA. Menche, N., 2016. Biologie Anatomie Physiologie. Urban und Fischer, Munich (refer to Fig. 1.1). Mesarovic, M.D., 1964. Views of General Systems Theory. Wiley, New York. Mesarovic, M.D., Macko, D., 1970. Theory of Hierarchical Multi-Level Systems. Academic Press, New York. Mesarovic, M.D., Takahara, Y., 1975. General Systems Theory: Mathematical Foundations. Academic Press, New York. Mesarovic, M.D., Takahara, Y., 1988. Abstract Systems Theory. Springer-Verlag, Berlin. Miller, J.G., 1978. Living Systems. McGraw Hill, New York. Pattee, H. (Ed.), 1973. Hierarchy Theory. George Braziller, New York. Rosen, R., 1970. Dynamical Systems Theory in Biology. Wiley, New York. Schmidt, R., Lang, F., Heckmann, M., 2017. Physiologie des Menschen: Mit Pathophysiologie. Springer, Berlin (refer to Fig. 1.1). Shannon, C.E., Weaver, W., 1949. The Mathematical Theory of Communication. The University of Illinois Press, Urbana. Sieniutycz, S., 2016. Thermodynamic Approaches in Engineering Systems. Elsevier, Oxford. Sieniutycz, S., Jez˙owski, J., 2009. Energy Optimization in Process Systems, first ed. Elsevier, Oxford. Sieniutycz, S., Jez˙owski, J., 2013. Energy Optimization in Process Systems and Fuel cells, second ed. Elsevier, Oxford. Sieniutycz, S., Jez˙owski, J., 2018. Energy Optimization in Process Systems, third ed. Elsevier, Oxford. Turchin, V., 1997. Phenomenon of Science. Columbia University, New York. Voet, D., Voet, J.G., 1995. Biochemistry, second ed. Wiley, New York. Von Bertalanffy, L., 1928. Kritische Theorie der Formbildung. Gebr€ uder Borntraeger, Berlin. Von Bertalanffy, L., 1968. General Systems Theory. George Braziller, New York. Von Foerster, H., 1979. In: Kripendorf, K. (Ed.), Cybernetics of Cybernetics. Gordon and Breach, New York. Von Neumann, J., 1958. Computer and the Brain. Yale University, New Haven. Waldroop, M.M., 1993. Complexity: The Emerging Science at the Edge of Order and Chaos. (A Popularizing Book on “The Sciences of Complexity”). Viking Books, London. Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and Machine. Massachusetts Institute of Technology Press, Cambridge, MA.
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Further reading Mittleton-Kelly, E., 2003. Ten principles of complexity. In: Complex Systems and Evolutionary Perspectives on Organizations. Elsevier, Oxford, ISBN: 0-08-043957-8. Nicolis, G., Prigogine, I., 1989. Exploring Complexity. W.H. Freeman. p. 72. Pschyrembel, 2017. Klinisches W€orterbuch. De Gruyter, Berlin.
Systems science vs cybernetics 1.1 General systems theory General systems theory (GST) is a science investigating general laws for arbitrarily complex arrangements—“systems”—which constitute functional integrities. The origin of the general systems theory is associated with the publication in 1928 of a seminal book (Von Bertalanffy, 1928, 1968) titled Kritische Theorie der Formbildung, authored by eminent Austrian biologist and philosopher Ludwig von Bertalanffy (1901–71). General systems theory (GST) is linked with cybernetics and information theory. Rigorous (axiomatic) mathematical formulation for GST was provided by Mesarovic and his collaborators (Mesarovic, 1964; Mesarovic and Macko, 1970; Mesarovic and Takahara, 1975, 1988), whereas important contributions to developments of systems theory were made by G.J. Klir in the field of computer methods of solving various systemic problems (Klir, 1969, 1972, 1981, 1985, 1992; Klir and Folger, 1987) and J.G. Miller in the field of living systems theory (Miller, 1978). The most typical ingredients (components) of the systems theory include: basic definitions, system thinking, system topologies, life cycles, system performance, conceptual design, current state evaluations, related sciences, solving methods, creative solutions, system synthesis, system analysis, optimization, solution assessment, virtual optimizing, system engineering, and evaluation of knowledge in economy and society. Contents of university courses usually involve: the significance of proper modeling and the role of identification and control aspects for systems of various structure. These issues are analyzed and the importance of computers in applications is evaluated.
1.2 Cybernetics and systems science in academics The basic concepts of cybernetics have proven to be very powerful in a variety of disciplines: computer science, management, sociology, biology, thermodynamics, etc. Cybernetics and systems science link the abstraction of philosophy and mathematics with the concreteness of (dealing with) the theory and modeling of real world systems. As exemplarily interdisciplinary sciences, cybernetics and systems science work between and among rudimentary disciplines, usually pairwise, but occasionally across more than two kinds of systems. Complexity and Complex Thermo-Economic Systems. https://doi.org/10.1016/B978-0-12-818594-0.00001-5 # 2020 Elsevier Inc. All rights reserved.
1
2
Chapter 1
Some recent approaches have their origins in ideas and concepts proposed by scientists many years ago: for example, neural networks, artificial intelligence, human-machine interfaces, organized therapies, etc. In these approaches, the majority of basic concepts and problems have already been formulated, mainly in the period of years 1940–80, by cyberneticians such as Von Bertalanffy (1928, 1968), Wiener (1948), Ashby (1952), Boulding (1978), Forrester (1961), Von Foerster (1979), Von Neumann (1958), McCulloch (1965), and Pattee (1973). Since their founding, cybernetics and systems science have struggled to achieve a level of respectability in the academic society. Cyberneticians especially have found it difficult to find their homes in academic institutions or to create their own. Presently, a number, perhaps still insufficient, of academic programs in cybernetics and systems science (CSS) exist, and those working in the new disciplines described seem not to always remember their cybernetic predecessors. What is the reason that the progress in the popularity of cybernetics is so slow? The difference between cyberneticians and researchers in the previously mentioned areas is that “the former stubbornly stick to their objective of building general, domain independent theories, whereas the latter focus on very specific applications, such as: expert systems, psychotherapy, thermodynamics, pattern recognition, etc. General integration of the former researchers is quite abstract, hence it is not sufficiently easy to be really appreciated” (Joslyn and Heylighen, 1992). As a common interdisciplinary field, cybernetics and systems science (CSS) offers common concepts used in multiple traditional disciplines and attempts to achieve a logically consistent unification by finding common terms for similar concepts in these multiple disciplines. Thus, CSS unifies individual concepts, theories, and terminologies in a specific discipline directed toward general, and perhaps idiosyncratic tools (usages). These new conceptual categories may not be recognizable to the traditional researchers, or they may find no reason in the use of the general concepts (Joslyn and Heylighen, 1992). Clearly, the problem of building a global theory is much more complex than any of the more down-to-earth goals of the fashionable approaches (Joslyn and Heylighen, 1992). But we may also say that the generality of the problem is dangerous in itself if it leads to being “stuck” in abstractions which are so far removed from everyday world that it is difficult to use them, interact with them, or test them on concrete problems; in other words, “to get a feel for how they behave and what their strengths and weaknesses are” (Joslyn and Heylighen, 1992). Although there are many exceptions, researchers in cybernetics and systems science tend to be trained in a traditional specialty (like biology, management, or psychology) and then come to apply themselves to problems in other areas, perhaps a single other area. Thus, their exposure to cybernetics and systems science concepts and theory tends to be somewhat ad hoc and specific to the two or three fields they apply themselves to (Joslyn and Heylighen, 1992). However, this
Systems science vs cybernetics 3
Capillaries in the lungs Pulmonary vein Pulmonary artery
Pulmonary circulation
Left atrium
Heart
Right atrium Right ventricle Left ventricle Aorta (main artery) Vena cava (main vein) Systemic circulation
Capillaries in the body Red: Oxygen-rich blood Blue: Oxygen-poor blood
Fig. 1.1 The human cardiovascular system.
doesn’t mean that the tremendous research record and great achievements of last years (especially those in biology after the discovery of DNA) are not appreciated enough. Rather, a much longer period is expected for inclusion of basic biological and biochemical systems into the general scheme of cybernetics and systems science (Voet and Voet, 1995; Menche, 2016; Schmidt et al., 2017). Fig. 1.1, which schematizes the human cardiovascular system, is an example of a very large number of complex biological subsystems found in living organisms. The blood circulatory system (cardiovascular system) consists of the heart and the blood vessels running through the entire body. The arteries carry blood away from the heart; the veins carry it back to the heart. There isn’t only one blood circulatory system in the human body, but two, which are connected: The systemic circulation provides organs, tissues, and cells with blood so that they get oxygen and other vital substances. The pulmonary circulation is where the fresh oxygen we breathe in enters the blood. At the same time, carbon dioxide is released from the blood (Menche, 2016; Schmidt et al., 2017).
1.3 Relation to other disciplines Ideas related to the domain of cybernetics and systems are firstly used in the emerging “sciences of complexity,” also called “complex adaptive systems,” studying selforganization and heterogeneous networks of interacting entities (e.g., the work of the Santa Fe Institute,
4
Chapter 1
Joslyn and Heylighen, 1992), and, secondly, in an associated research in the natural sciences such as far-from equilibrium thermodynamics, stability theory, catastrophe theory, chaos, and dynamical systems. An example is a bifurcation diagram of the stability theory, Fig. 1.2, which shows how the change of the control parameter C influences the change of the stability properties of the system. A third strand is constituted by different high-level computing applications such as artificial intelligence, neural networks, man-machine interaction, and computer modeling and simulation. Unfortunately, few practitioners in these recent disciplines seem to be aware that many of their concepts and methods have been proposed and used by cyberneticians for many years (Joslyn and Heylighen, 1992). Subjects like complexity, selforganization, connectionism, and X
(b1)
Stable
(a)
Stable
(a¢)
Unstable
Thermodynamic branch
Unique solution
Stable (b2) Multiple solutions
Fig. 1.2 Bifurcation diagram showing how the state variable X is affected when an arbitrary control parameter varies along the horizontal direction: the unique solution (a), the thermodynamic branch loses its stability at the point where the curvilinear boundary line touches the vertical straight line separating a unique solution and multiple solutions. At this value of control parameter, new branches of solutions (b1, b2), which are stable as implied by this example, are generated. Based on Nicolis, G., Prigogine, I., 1989. Exploring Complexity. W.H. Freeman. p. 72.
Systems science vs cybernetics 5 adaptive systems have already been extensively studied, in the 1940s and 1950s, by researchers like Wiener, Ashby, von Neumann, and von Foerster, and in discussion forums like the famous Josiah Macy meetings on cybernetics (Heims, 1991). Some popularizing books on “the sciences of complexity,” e.g., Waldroop (1993), seem to ignore the fact, creating the false impression that work on complex adaptive systems only started in earnest with the creation of the Santa Fe Institute in the 1980s (Joslyn and Heylighen, 1992).
1.4 What are cybernetics and systems science? Cybernetics and systems science (also “general system theory” or “system’s research”) constitutes a somewhat fuzzily defined academic domain which touches upon virtually all traditional disciplines, from mathematics, technology, and biology to philosophy and social sciences. It is more specifically related to the recently developing “sciences of complexity,” including artificial intelligence (AI), neural networks (NN), dynamical systems, chaos, and complex adaptive systems. The history of “sciences of complexity” dates back to the 1940s and 1950s when thinkers such as Wiener, von Bertalanffy, Ashby, and von Foerster founded the domain through a series of interdisciplinary meetings. Systems theory or system science argues that however complex or diverse the world that we experience is, we will always find different types of organizations in it, and such organizations can be described by concepts and principles which are independent from the specific domain at which we are looking. Hence, if we were to uncover these general laws, we would be able to analyze and solve problems in any domain, pertaining to any type of system. The system approach distinguishes itself from the more traditional analytical approach by emphasizing the interactions and connectedness of different components of a system. Although the system approach in principle considers all types of systems, in practice, it focuses on more complex adaptive, self-regulating systems which we might call cybernetic (Heylighen et al., 1999). Many concepts used by system scientists come from closely related approach of cybernetics: information, control, feedback, communication, etc. Cybernetics, derived from the Greek word for steersman (kybernetes), was first introduced by the mathematician Wiener, as the science of communication and control in the animal and the machine (to which we now might add: in social society and in individual human beings). It grew out of Shannon’s information theory, which was designed to optimize the transmission of information through communication channels and the feedback concept used in engineering control systems. See various schemes of a communication channels in Shannon and Weaver (1949) or other books on the information theory. In the present incarnation of “second order cybernetics,” the emphasis is on how observers construct models of the systems with which they interact (c.f. constructivism).
6
Chapter 1
In fact, cybernetics and systems theory study essentially the same problem. The problem is organization independent of the substrate in which it is embodied. Insofar as it is meaningful to make a distinction, systems theory is focused more on the structure of systems and their models, whereas cybernetics is focused on how systems function, that is to say how they control their actions and how they communicate with other systems or with their own components. Since structure and function of a system cannot be understood in separation, it is clear that cybernetics and systems theory should be viewed as two facets of a single approach (Heylighen et al., 1999). This insight has resulted in the two domains almost merging in practice: many, if not most, of the central associations, journals, and conferences in the field include both terms, systems and cybernetics, in their title. In spite of the absence of strict subdivisions, though, the field is rather fragmented with many different approaches, similar in some respects, different in others, existing side-by-side (Heylighen et al., 1999). Many “schools,” such as autopoietic systems, anticipatory systems, living systems, viable systems, or soft systems, are associated with a particular theorist or thinker, respectively Maturana and Varela (1987), Rosen (1970), Miller (1978), Beer (1975), Turchin (1997). As a result, occasionally, the cybernetics and systems domain still lacks clear foundations. Yet, the authors of the Principia Cybernetica Project (Heylighen et al., 1999), believe that “the commonalities are much larger than the differences,” and, therefore, it is worthwhile attempting to integrate different approaches in a common conceptual network. While the present chapter cites many statements discussed in the text of Heylighen et al. (1999), some important issues contained therein were omitted for brevity. This pertains, in particular, to information about outside internet links. Some very good, readable introductory books on cybernetics and systems can be downloaded from the Principia Cybernetica Library (Heylighen et al., 1999). Together with a dictionary and bibliography of basic books and papers, this should be sufficient for at least an introductory course in the domain. Optimization of energy systems is covered in three editions of the book written by the present author with Jacek Jez˙owski (Sieniutycz and Jez˙owski, 2009, 2013, 2018); for a broader perspective, a comprehensive review of various thermodynamic approaches to practical systems should be helpful (Sieniutycz, 2016). Occasionally useful are sources and links to diverse articles, books, and websites, which provide further information and references. In particular, diverse sources are essential for understanding basic problems of selforganization. Summing up, in this introduction, typical ingredients of the systems theory are specified and its link with cybernetics is pointed out. General systems theory (GST) is defined as a science investigating general laws for topologically complicated arrangements—“systems”—which constitute the functional integrities. This introductory chapter provides the reader with both a historical outline and brief state-of-art of the GST a well as the list of the scientists who
Systems science vs cybernetics 7 contributed to the birth of the field and to the development of its mathematical backgrounds (in particular to the development of computer methods and indication of their applicative potential).
References Ashby, R., 1952. Design for a Brain. Wiley, New York. Beer, S., 1975. Platform for Change. Wiley, Chichester. Boulding, K., 1978. Ecodynamics. Sage, Beverly Hills. Forrester, J.W., 1961. Industrial Dynamics. Appendix E. MIT Press, Cambridge, MA. Heims, S., 1991. The Cybernetics Group. MIT Press, Cambridge, MA. Heylighen, F., Joslyn, C., Turchin, 1999. What are Cybernetics and Systems Science? http://pespmc1.vub.ac.be/ CYBSWHAT.html. Joslyn, C., Heylighen, F., 1992. Cybernetics and Systems Science in Academics. http://pespmc1.vub.ac.be/ CYBSACAD.html. Klir, G., 1969. An Approach to General Systems Theory. Van Nostrand, New York. Klir, G. (Ed.), 1972. Trends in General Systems Theory. Wiley, New York. Klir, G., 1981. Special issue on reconstructibility analysis. Int. J. Gen. Syst. 7 (1), 1–107. Klir, G., 1985. Architecture of Systems Problem Solving. Plenum, New York. Klir, G., 1992. Facets of Systems Science. Plenum, New York. Klir, G., Folger, T., 1987. Fuzzy Sets, Uncertainty, and Information. Prentice Hall, Englewood Cliffs. Maturana, H.R., Varela, F., 1987. The Tree of Knowledge—The Biological Roots of Human Understanding. Shambala Publications, Boston. McCulloch, W., 1965. Embodiments of Mind. MIT Press, Cambridge, MA. Menche, N., 2016. Biologie Anatomie Physiologie. Urban und Fischer, Munich (refer to Fig. 1.1). Mesarovic, M.D., 1964. Views of General Systems Theory. Wiley, New York. Mesarovic, M.D., Macko, D., 1970. Theory of Hierarchical Multi-Level Systems. Academic Press, New York. Mesarovic, M.D., Takahara, Y., 1975. General Systems Theory: Mathematical Foundations. Academic Press, New York. Mesarovic, M.D., Takahara, Y., 1988. Abstract Systems Theory. Springer-Verlag, Berlin. Miller, J.G., 1978. Living Systems. McGraw Hill, New York. Pattee, H. (Ed.), 1973. Hierarchy Theory. George Braziller, New York. Rosen, R., 1970. Dynamical Systems Theory in Biology. Wiley, New York. Schmidt, R., Lang, F., Heckmann, M., 2017. Physiologie des Menschen: Mit Pathophysiologie. Springer, Berlin (refer to Fig. 1.1). Shannon, C.E., Weaver, W., 1949. The Mathematical Theory of Communication. The University of Illinois Press, Urbana. Sieniutycz, S., 2016. Thermodynamic Approaches in Engineering Systems. Elsevier, Oxford. Sieniutycz, S., Jez˙owski, J., 2009. Energy Optimization in Process Systems, first ed. Elsevier, Oxford. Sieniutycz, S., Jez˙owski, J., 2013. Energy Optimization in Process Systems and Fuel cells, second ed. Elsevier, Oxford. Sieniutycz, S., Jez˙owski, J., 2018. Energy Optimization in Process Systems, third ed. Elsevier, Oxford. Turchin, V., 1997. Phenomenon of Science. Columbia University, New York. Voet, D., Voet, J.G., 1995. Biochemistry, second ed. Wiley, New York. Von Bertalanffy, L., 1928. Kritische Theorie der Formbildung. Gebr€ uder Borntraeger, Berlin. Von Bertalanffy, L., 1968. General Systems Theory. George Braziller, New York. Von Foerster, H., 1979. In: Kripendorf, K. (Ed.), Cybernetics of Cybernetics. Gordon and Breach, New York. Von Neumann, J., 1958. Computer and the Brain. Yale University, New Haven. Waldroop, M.M., 1993. Complexity: The Emerging Science at the Edge of Order and Chaos. (A Popularizing Book on “The Sciences of Complexity”). Viking Books, London. Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and Machine. Massachusetts Institute of Technology Press, Cambridge, MA.
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Further reading Mittleton-Kelly, E., 2003. Ten principles of complexity. In: Complex Systems and Evolutionary Perspectives on Organizations. Elsevier, Oxford, ISBN: 0-08-043957-8. Nicolis, G., Prigogine, I., 1989. Exploring Complexity. W.H. Freeman. p. 72. Pschyrembel, 2017. Klinisches W€orterbuch. De Gruyter, Berlin.