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Int. J. System of Systems Engineering, Vol. 6, Nos. 1/2, 2015 15

Copyright © 2015 Inderscience Enterprises Ltd.

Systems theory as a foundation for governance of complex systems

Kaitlynn Whitney Department of Engineering Management and Systems Engineering, Old Dominion University, Norfolk, Virginia, USA Email: [email protected]

Joseph M. Bradley* Leading Change, LLC, 1133 Belmeade Dr., Virginia Beach, VA 23455 Virginia Beach, Virginia, USA Email: [email protected] *Corresponding author

Dale E. Baugh Department of Engineering Management and Systems Engineering, Old Dominion University, 116 Commodore Lane, Smithfield VA 23430 Norfolk, Virginia, USA Email: [email protected]

Charles W. Chesterman Jr. Department of Engineering Management and Systems Engineering, Old Dominion University, 201 Sinclair Street, Norfolk, VA 23505 Norfolk, Virginia, USA Email: [email protected]

Abstract: The broad set of propositions identified in systems literature (circa 1900–2000s) provides an adequate, largely comprehensive subset of the complete set of all systems theory propositions. Discoverers’ induction can then be applied to integrate common ideas among propositions in order to produce a set of generalised laws (axioms). A proposal for a systems theory construct resting on an axiomatic set supported by unified systems theory propositions was presented by Adams et al. (2014). This paper refines the work of Adams et al. using discoverer’s induction and further describes the axioms provided and their role in complex systems.

Keywords: systems of systems; complex systems; systems theory.

16 K. Whitney et al.

Reference to this paper should be made as follows: Whitney, K., Bradley, J.M., Baugh, D.E. and Chesterman Jr., C.W. (2015) ‘Systems theory as a foundation for governance of complex systems’, Int. J. System of Systems Engineering, Vol. 6, Nos. 1/2, pp.15–32.

Biographical notes: Kaitlynn Whitney is a PhD student in the Frank Batten College of Engineering and Technology in the Department of Engineering Management and Systems Engineering at Old Dominion University in Norfolk, VA where she is also employed as a graduate assistant. She received her Master of Engineering Management in 2013 and Bachelor of Science in Applied Mathematics in 2011.

Joseph M. Bradley is the President and Founder of Leading Change, LLC, a small consulting firm focused on assisting clients with complex governance problems, challenges in systems of systems engineering and competency models. Formerly, he served as a Principal Research Scientist at the National Centers for System of Systems Engineering (NCSoSE), a research centre at Old Dominion University in Norfolk, Virginia. His research focus is on systems of systems engineering, systems dynamics, action science and performance measurement. He is retired from the US Navy, has served as a consultant in industry and is a member of the American Society of Naval Engineers and ASQ. He earned the degree of Doctor of Philosophy from Old Dominion University and holds a degree of Mechanical Engineer as well as Masters of Science in Mechanical Engineering from the Naval Postgraduate School and Bachelor’s degree in Civil Engineering from the Cooper Union.

Dale E. Baugh holds the position of Director of Aircraft Carrier Life Cycle Support Programs for the Gerald R. Ford Class aircraft carrier at Newport News Shipbuilding, VA. In addition, he is a board of directors’ member of the Virginia Ship Repair Association and the Hampton Roads Military and Federal Facility Alliance and has been a Vice President in Newport News Nuclear’s DOE programmes. He is a graduate of the US Naval Academy and the Navy Postgraduate School and has held positions of increasing responsibility for navy ship maintenance programmes. He retired from the navy after 32 years of service, culminating as Director of Fleet Maintenance for the US Fleet Forces Command. He also commanded Puget Sound Naval Shipyard and NAVSEA Industrial Activity and Logistics Directorate, SEA 04. He currently is a PhD student at Old Dominion University in the Engineering Management and Systems Engineering programme.

Charles W. Chesterman Jr. has over 40 plus years of experience in advanced technology, management and leadership in technological industry working in and for the federal government. He has been a senior level executive with experience in overall company management, operational logistics, budgeting, financial management, ship conversion, overhaul and repair and development and application of change management. He is currently a PhD student at Old Dominion University, Batten College of Engineering and Technology in the Department of Engineering Management and Systems Engineering.

Systems theory as a foundation for governance of complex systems 17

1 Introduction

At the present time, a universally agreed upon definition for systems theory does not exist, though the term is ubiquitous in systems literature. Adams et al. (2014) proposed a systems theory construct which rests on an axiomatic set supported by cited propositions in systems theory literature, developed by use of the axiomatic method. The resulting construct affords practitioners and theoreticians a prescriptive set of axioms by which a system must operate; and conversely, any entity defined as a system will be characterised by this set of axioms:

1 centrality axiom

2 contextual axiom

3 goal axiom

4 operational axiom

5 viability axiom

6 design axiom

7 information axiom.

The axioms as they are presently organised conform to the discoverers’ induction as proposed by William Whewell where knowledge can be constructed through the union of sensations and ideas (Snyder, 1997). The use of this inductive inference methodology provided insight of the common themes integrated among systems theory principles in order to produce a set of axioms that describe systems. There are two steps to discoverer’s induction, as follows (Snyder, 1997):

1 colligate known members of a class by the use of an idea or conception

2 generalise this concept over the complete class, including its unknown members.

Colligation as defined by Snyder, is “the mental operation of bringing together a number of empirical facts by ‘superinducing’ upon them some idea or conception that unites the facts and renders them capable of being expressed by a general law” (1997, p.585). This new knowledge adds to the current body of facts, causing them to be seen in a new light; in this case, elucidating generalised properties of systems. Generalisable knowledge projected onto unknown members of a class (i.e., unidentified propositions that would support the development of axioms) suggests that the listing of proposed axioms may be incomplete, or omitting some aspect of absolute truth; however, they are presented in confidence as they are well grounded in systems theory literature and justified by practitioner experience. This theoretical grounding provides a better understanding of complex systems to more effectively guide the design, execution and evolution of governance functions.


2 Background

Between WWI and WWII, a multidisciplinary problem-solving research effort began that incorporated a decomposition of the problem complex into individual problems related to the respective fields in which they applied, to then be solved independently of each other. The independent solutions were later aggregated. This was later realised as ineffective:

Different terms are used to refer to the same thing and the, same term is used to refer to different things. This state is aggravated by the fact that the literature of systems research is widely dispersed and is therefore difficult to track. Researchers in a wide variety of disciplines and interdisciplines are contributing to the conceptual development of the systems sciences but these contributions are not as interactive and additive as they might be [Ackoff, (1971), p.661].

Thus interdisciplinary research began, in which representatives from different disciplines confronted problem complexes together to solve them collaboratively. The growth of systems theory commenced from immense pressure to develop theories capable of interdisciplinary application. In 1954, biologist von Bertalanffy, economist Kenneth Boulding, physiologist Ralph Gerard and mathematician Anatol Rapoport collaborated at the Palo Alto Center for advanced study in behavioural sciences, where they discovered the wide applicability of their convergent thoughts stemming from their different fields of study (von Bertalanffy, 1968). They soon formed the original bylaws for the foundation of the Society for General Systems Research (SGSR) (Hammond, 2002):

1 to investigate the isomorphy of concepts, laws and models from various fields and to help in useful transfers from one field to another

2 to encourage development of adequate theoretical models in fields which lack them

3 to minimise the duplication of theoretical effort in different fields

4 to promote the unity of science through improving communications among specialists (pp.435–436).

von Bertalanffy (1968) continued writing on the subject throughout his career, recognising the gravitation towards integrated natural and social sciences, centred in systems theory. He noted that by unifying principles expressed in dissonant fields, the effort could eventually lead to a “much-needed integration in scientific education” (1968, p.37). Biologist Paul A. Weiss declared that this conceptual integration would “render the map of knowledge more complete, and more consistently coherent” [Laszlo, (1969), p.159].

There has not been a full adoption of a generally accepted canon of systems theory within the discipline, albeit the potential for systems theory has been realised in theory or practice, as noted by Checkland (1993). Still, practitioners can greatly benefit from the body of knowledge that does exist, which certainly provides necessary propositions that are relevant for common practice.


3 Systems theory propositions

The broad set of propositions identified in this paper reflects a current stage of knowledge through modification to the set provided by Adams et al. (2014). These propositions reflect widely accepted concepts that have been proposed about systems in systems theory literature (circa 1900–2000s). Each proposition is backed by empirical research from an array of disciplines that provides insight about the characteristics, tendencies and considerations of real-world systems. While the list of propositions is likely incomplete, in the view of the authors, this set of propositions provides an adequate, largely comprehensive subset. Each systems theory proposition, its primary proponent in the literature and a description are shown in Table 1. Table 1 Alphabetical listing of systems theory propositions

Principle and primary proponent Description of principle

1 Boundary (von Bertalanffy, 1968; Skyttner, 2005)1

The abstract, semi-permeable perimeter of the system defines the components that make up the system, segregating them from environmental factors and may prevent or permit entry of matter, energy and information.

2 Circular causality (Korzybski and Rouben Mamoulian Collection (Library of Congress), 1933)

An effect becomes a causative factor for future ‘effects’, influencing them in a manner particularly subtle, variable, flexible and of an endless number of possibilities.

3 Communication2 (Shannon, 1948a, 1948b; Skyttner, 2005)

Communication is a transaction between the information source terminal and the destination terminal, with the sole aim of generation and reproduction of symbols. Information is transmitted as a selection along possible alternative states.

4 Complementarity (Bohr, 1928)

Two different perspectives or models about a system will reveal truths regarding the system that are neither entirely independent nor entirely compatible.

5 Control (Checkland, 1993)

The process by means of which a whole entity retains its identity and/or performance under changing circumstances.

6 Incompressibility3 (Cilliers, 1998; Richardson, 2004)

Each element in the system is ignorant of the behaviour of the system as a whole and only responds to information that is available to it locally. As such, the best representation of a complex system is the system itself and that any representation other than the system itself will necessarily misrepresent certain aspects of the original system.

7 Dynamic equilibrium4 (von Bertalanffy, 1968; Miller, 1978)

An entity exists as expressions of a pattern of processes of an ordered system of forces, undergoing fluxes and continuing flows of matter, energy and information in an equilibrium that is not static.

Notes: 1Not included in the Adams et al. (2014) proposition table. In addition, viability was removed as a proposition but remains as an axiom, 2Definition modified and reference added, 3Previously referred to as darkness principle. Definition modified, 4Definition modified and reference added, 5Previously referred to as Pareto principle, although this principle and Zipf’s law are subsumed by power law. Definition modified and reference added


Table 1 Alphabetical listing of systems theory propositions (continued)


8 Emergence (Checkland, 1993; Aristotle, 2002)

Whole entities exhibit properties and patterns that are meaningful only when they are attributed to the whole, not its parts.

9 Equifinality (von Bertalanffy, 1950)

If a steady state is reached in an open system, it is independent of the initial conditions and determined by the system parameters, e.g., rates of reaction and transport.

10 Feedback (Wiener, 1948)

All purposeful behaviour may be considered to require negative feedback. If a goal is to be attained, some signals from the goal are necessary at some time to direct the behaviour.

11 Hierarchy (Pattee, 1973; Checkland, 1993)

Entities meaningfully treated as wholes are built up of smaller entities which are themselves, wholes. In a hierarchy, emergent properties denote the levels

12 Holism (Smuts, 1926) A system must be considered as a whole, rather than a sum of its parts.

13 Homeorhesis (Waddington, 1957, 1968)

The concept encompassing dynamical systems that return to an acceptable trajectory through adjustments in dynamic equilibrium controlled by interrelated regulation mechanisms.

14 Homeostasis (Cannon, 1929)

The property of an open system to regulate its internal environment so as to maintain a stable condition, by means of multiple dynamic equilibrium adjustments controlled by interrelated regulation mechanisms.

15 Information redundancy (Shannon and Weaver, 1949)

The number of bits used to transmit a message, minus the number of bits of actual information in the message.

16 Minimal critical specification (Cherns, 1976, 1987)

This principle has two aspects, negative and positive. The negative simply states that no more should be specified than is absolutely essential; the positive requires that we identify what is essential.

17 Multifinality (Buckley, 1967)

Radically different end states are possible from the same initial conditions.

18 Power law5 (Newman, 2006)

The probability of measuring a particular value of some quantity varies inversely as a power of that number.

19 Purposive behaviour (Rosenblueth et al., 1943)

Purposeful behaviour is meant to denote that the act or behaviour may be interpreted as directed to the attainment of a goal – i.e., to a final condition in which the behaving object reaches a definite correlation in time or in space with respect to another object or event.

20 Recursion (Beer, 1979) The fundamental laws governing the processes at one level are also present at the next higher level.

21 Redundancy (Pahl et al., 2011)

Means of increasing both the safety and reliability of systems by providing superfluous or excess resources.



Table 1 Alphabetical listing of systems theory propositions (continued)


22 Redundancy of potential command (McCulloch, 1965)

Effective action is achieved by an adequate concatenation of information.

23 Relaxation time (Clemson, 1984; Holling, 1996)

Systems need adequate time to recover from disorder that disturbs its equilibrium, at which point characteristic behaviour resumes.

24 Requisite hierarchy (Aulin-Ahmavaara, 1979)

The weaker in average are the regulatory abilities and the larger the uncertainties of available regulators, the more hierarchy is needed in the organisation of regulation and control to attain the same result, if possible at all.

25 Requisite parsimony (Miller, 1956; Simon 1974)

The capacity of human short-term recall is no greater than seven plus or minus two items.

26 Requisite saliency (Boulding, 1966)

The factors that will be considered in a system design are seldom of equal importance. Instead, there is an underlying logic awaiting discovery in each system design that will reveal the significance of these factors.

27 Requisite variety (Ashby, 1956)

Control can be obtained only if the variety of the controller is at least as great as the variety of the situation to be controlled.

28 Satisficing (Simon, 1955, 1956)

The decision-making process whereby one chooses an option that is, while perhaps not the best, good enough.

29 Self-organisation (Ashby, 1947)

The spontaneous emergence of order out of the local interactions between initially independent components.

30 Sub-optimisation (Hitch, 1953)

If each subsystem, regarded separately, is made to operate with maximum efficiency, the system as a whole will not operate with utmost efficiency.


A common objection noted with systems theory propositions is that each proposition should apply to all systems, which, they may not, or at least not noticeably. The model of the system may not be well enough defined to capture the principle or the perspective of the observer may not afford the ability to view the principle. As a system becomes more elaborate and complex, phenomena described in systems theory propositions which were previously unobservable become apparent. From the authors’ perspective, a system is an interacting set of elements conserving a set of relations among them, which work together toward some common objective or purpose (Ackoff, 1971; Blanchard and Fabrycky, 2006; Laszlo and Krippner, 1998). A compilation of distributed, independent component systems that, through complex interactions, strive toward the purpose of a common goal, form a system of systems (Maier, 1999).


4 Systems theory model

Systems theory serves as a foundation to explain phenomena, patterns and tendencies observed in real world systems. The model of systems theory espoused by Adams et al. (2014), demonstrates the intermediate relationship of axioms between theory and supporting propositions. This is depicted in Figure 1:

Figure 1 Relationship between theory, propositions, axioms and real system (see online version for colours)

Source: Redrawn from Adams et al. (2014)

The 30 systems theory propositions from Table 1 were inductively evaluated to colligate a new set of ideas about systems (the axioms) that make up systems theory. This notion of theory encompasses propositions originating from a large set of observations and findings that have been verified through scientific research and experimentation and may be considered factual (Angier, 2007). The real world system serves as the source for empirical results by which systems theory principles have propagated from various disciplines, as systems theory lies at the intersection of thought generated from well-defined, inherently multidisciplinary propositions described by distinguished authors of varying disciplines.

5 Construction of the systems theory axioms

The 30 propositions presented in Table 1 serve as the basis for inductive development of the axioms. Through inductive analysis, each axiom is presented in Table 2 alongside the systems theory propositions that support it.

This modified version of the typology of systems theory propositions and axioms proposed by Adams et al. reinforces the original intent to serve as a basis for understanding the behaviour of systems, to increase our competence in our interpretation, thinking, decision-making and action with respect to systems.


Table 2 Axioms for systems theory

Axiom Supporting propositions

Communication (Shannon, 1948a, 1948b) Control (Checkland, 1993) Emergence (Aristotle, 2002)

The centrality axiom states that central to all systems are two pairs of propositions; emergence and hierarchy and communication and control. The centrality axiom’s propositions describe the system by focusing on (1) a system’s hierarchy and its demarcation of levels based on emergence arising from sub-levels; and (2) systems control which requires feedback of operational properties through communication of information.

Hierarchy (Pattee, 1973)

Complementarity (Bohr, 1928) Incompressibility (Cilliers, 1998) Holism (Smuts, 1926)

The contextual axiom states that system meaning is informed by the circumstances and factors that surround the system. The contextual axiom’s propositions are those which bound the system by providing guidance that enable an investigator to understand the set of external circumstances or factors that enable or constrain a particular system.

Boundary (von Bertalanffy, 1968; Skyttner, 2005)

Minimal critical specification (Cherns, 1976, 1987) Power law (Newman, 2006) Requisite parsimony (Miller, 1956)

The design axiom states that system design is a purposeful imbalance of resources and relationships. Resources and relationships are never in balance because there are never sufficient resources to satisfy all of the relationships in a system’s design. The design axiom provides guidance on how a system is planned, instantiated and evolved in a purposive manner.

Requisite saliency (Boulding, 1966)

Equifinality (von Bertalanffy, 1950) Multifinality (Buckley, 1967) Purposive behaviour (Rosenblueth et al., 1943)

The goal axiom states that systems achieve specific goals through purposeful behaviour using pathways and means. The goal axiom’s propositions address the pathways and means for implementing systems that are capable of achieving a specific purpose. Satisficing (Simon, 1955, 1956)

Information redundancy (Shannon and Weaver, 1949)

The information axiom states that systems create, possess, transfer and modify information. The information axiom provides understanding of how information affects systems.

Redundancy of potential command (McCulloch, 1965)

Dynamic equilibrium (Bertalanffy, 1972; Miller, 1978) Homeorhesis (Waddington, 1957, 1968) Homeostasis (Cannon, 1929) Redundancy (Pahl et al., 2011) Relaxation time (Holling, 1996) Self-organisation (Ashby, 1947)

The operational axiom states that systems must be addressed in situ, where the system is exhibiting purposeful behaviour. The operational axiom’s propositions provide guidance to those that must address the system in situ, where the system is functioning to produce behaviour and performance.

Sub-optimisation (Hitch, 1953)


Table 2 Axioms for systems theory (continued)

Axiom Supporting propositions

Circular causality (Korzybski, 1994) Feedback (Wiener, 1948) Recursion (Beer, 1979) Requisite hierarchy (Aulin-Ahmavaara, 1979)

The viability axiom states that key parameters in a system must be controlled to ensure continued existence. The viability axiom addresses how to design a system so that changes in the operational environment may be detected and affected to ensure continued existence. Requisite variety (Ashby, 1956)

Figure 2 Centrality axiom (see online version for colours)

Figure 3 Contextual axiom (see online version for colours)

Figure 4 Design axiom (see online version for colours)


Figure 5 Goal axiom (see online version for colours)

Figure 6 Information axiom (see online version for colours)

Figure 7 Operational axiom (see online version for colours)

Figure 8 Viability axiom (see online version for colours)


5.1 The centrality axiom

• Central to all systems are two pairs of propositions: emergence and hierarchy and communication and control.

Systems are organised in hierarchies that are demarcated by levels based on the emergence of sublevels. Every model of a complex system exhibits emergent properties as a whole entity, as well as each level of recursion containing sublevels, which derives from the structure and interactions of component activities (Checkland, 1993). Control at each level of recursion requires feedback of system operations through communication of information. All communication in this context implies that transduction has occurred and that meaningful understanding has been transferred. The communication of information ‘drives’ the system and that causes interactions among systems.

5.2 The contextual axiom

• System meaning is informed by the circumstances and factors that surround the system.

In the search to analyse a complex system, contextual details aid the practitioner in understanding the dynamics of the system. System meaning is informed by the system’s context, or the set of circumstances, factors, conditions, values and patterns that make up the context of the system. The context is influential in enabling or constraining the behaviour of the system, as well as interpretation of that system, the system engineering process and the system solution design and deployment. The contextual axiom informs a worldview of the necessary presuppositions to form the most correct interpretation of a system. The supporting propositions for the contextual axiom provide insight into how systems are bounded and considered.

The boundaries that exist and separate systems are not considered solid such that separate systems are isolated. Instead, boundaries are such that separate systems are able to interact with their environments and internally. How the system is bounded and how the boundaries are conceived can greatly influence interpretation of the system. Because interpretations of systems are contingent on one or more vantage points, backgrounds and values, the effort to understand systems can be aided by accompanying perceptions of others, as complementarity suggests.

The basic doctrine of systems theory rests in Aristotle’s assertion that the whole is greater than its parts: the parts, in their structural arrangement and engaged in respective operations and interactions, constitute the whole (Smuts, 1926). It is this distinction that separates a system from simply an aggregate. By their very nature, systems require the observer to subscribe to holism in order to study systems. The holistic perspective permits a more complete representation of the system for discussion and analysis by recognising the various human, social, organisational, managerial, policy and political considerations (Keating, 2014). Finally, engineers of physical and social systems are for the most part well-equipped with the desire to understand and troubleshoot the systems that they are responsible for or are accomplishing troubleshooting upon and it is understood that darkness will always limit completeness and certainty of system knowledge.


5.3 The design axiom

• System design is a purposeful imbalance of resources and relationships.

Factors considered in a system design are seldom of equal importance. Accordingly the underlying logic that a system has will eventually reveal the significance of each factor in the design (requisite saliency). Similarly, humans having short-term memory are frequently incapable of recalling more than seven plus or minus two items as defined by requisite parsimony. This natural prioritisation is supportive of defining only the essential system requirements in system design. The complexity of systems and elaboration in their design and integration has rapidly evolved in developed societies, provoking unintentional and chaotic emergence that soon become an unmanageable ‘mess’.

In the design of systems, it is recognised that by being selective in defining a system’s minimum critical specifications, restrictions on flexibility are avoided so that the system may respond to conditions to support more consistent levels of metasystem performance and the integration of components or systems of systems (Djavanshir et al., 2012). Over-specification of constraints is found to reduce metasystem agility and responsiveness to the changing environment.

Power law relationships hold true for numerous instances of physical, biological and social phenomena. Power laws demonstrate the tendency for some fraction of a system to be responsible for an inverse amount of the system’s behaviour, i.e., the probability of measuring a particular value of some quantity varies inversely as a power of that number (Newman, 2006). This coincides with the popular 80/20 rule, which states that 80% of the objectives or outcomes are achieved with 20% of the means. This further supports the argument for restricting system design requirements to minimal critical specification so that defining only what is necessary for the system to produce the behaviour it is intended to produce and nothing beyond what is essential, will allow the system to most comfortably self-organise (Cherns, 1976), as will be discussed in the next section.

5.4 The goal axiom

• Systems achieve specific goals through purposive behaviour using pathways and means.

Systems demonstrate purposive behaviour in order to achieve specific goals. By studying the goals, goal-oriented behaviour functions and the purposes of the system, greater ability is rendered to evaluate and potentially improve system performance. The goal axiom’s supporting proposition equifinality aligns the nature of systems that have the capability of achieving a specific purpose through multiple strategies. These multiple paths from different initial conditions that result in the same output or outcomes for a complex system imply there is no single best solution to a complex problem, only different solutions that may or may not produce different results (Clemson, 1984; Skyttner, 2005). Conversely, any one strategy, even when executed under seemingly similar initial conditions, may lead to drastically different results, expressing the proposition of multi-finality. In adhering to constraints set by resource allocations while operating under uncertainty, improvements in systems operation through the use of a satisficing solution (which may or may not be the ideal solution) can lead to goal attainment.


5.5 The information axiom

• Systems create, possess, transfer and modify information.

The information axiom describes basic system behaviour with regards to how information is generated, processed, transferred and modified. The flow of information is governed by design to support both decision-making and action taken. While information redundancy is essential to protect against errors and promote system cohesion, information may be modified to reduce or eliminate unwanted redundancy.

Redundancy of potential command informs that power resides where information resides (McCulloch, 1965). The potential to make decisions and act effectively is conferred by sufficient concatenation of information (Skyttner, 2005). Command should pass, at least temporarily, to the region with the most important information. With ineffective organising and regulating interrelations in place, it is possible for ineffective or detrimental transduction of information to occur, emphasising the importance of metasystem influence.

5.6 The operational axiom

• Systems must be addressed in situ, where the system is exhibiting purposeful behaviour.

Observation and analysis of a system should take place in situ, or in its natural operating state, where the system is exhibiting purposeful behaviour (Adams et al., 2014). The propositions supporting the operational axiom provide guidance to those who must address the operational behaviour of the system.

Closed systems achieve equilibrium when opposing variables of the system are in balance. Open systems contain fluxes of matter, energy and information and hence their steady state is a dynamic equilibrium (Miller, 1978). The term dynamic equilibrium has been used interchangeably to describe both closed and open systems (von Bertalanffy, 1968). This is because in the construction of the ‘general system model’, intuition will lead us to regard all systems as open systems, as open systems become ‘closed’ when placed under constraints that equate their transport variables to equal zero (von Bertalanffy, 1972).

A system maintains its dynamic equilibrium by making internal adjustments that lead it back to a desired or intended state or trajectory. Homeostasis, or the ability of an open system to regulate its internal environment so as to maintain a stable condition, is achieved by means of multiple dynamic equilibrium adjustments controlled by interrelated regulation mechanisms (Cannon, 1929). Over the life cycle of a system, a particular stable state may not be possible or desired; thus homeorhesis describes the concept encompassing dynamical systems that return to an acceptable trajectory through adjustments controlled by interrelated regulation mechanisms (Waddington, 1968).

Relaxation time is a measurement of how much time a system requires to return to its equilibrium state, where resistance to disturbance and speed of return to the equilibrium are used to measure the property (Miller, 1978). By definition, system stability is possible only if the system’s relaxation time is shorter than the mean time between disturbances (Skyttner, 2005). Systems need adequate time to recover from disorder that disturbs its equilibrium, at which point characteristic behaviour resumes.


As noted earlier, observation and analysis of a system should take place in situ, or in its natural operating state, where the system is exhibiting purposeful behaviour (Adams et al., 2014). This includes mindfulness of how subsystem and component behaviour relates to the whole. If each subsystem, regarded separately, could operate with maximum efficiency, the system as a whole would not operate with utmost efficiency, enforcing the idea that systems must operate with sub-optimisation for optimal performance. Systems operate through behaviour derived from self-organisation that emerges as the system achieves its natural order that preserves its identity and purpose. A system is supplied with a redundancy of resources as a means of increasing both its safety and reliability of systems by providing what many might consider superfluous or excess resources.

5.7 The viability axiom

• Key parameters in a system must be controlled to ensure continued existence.

Viability is defined as the ability of a system to maintain independent existence (Keating, 2009). System viability relies on the control of key parameters in a system to ensure continued existence. A system’s identity is preserved through integration and serves as a source for consistency. For a system to maintain viability, a system must have a suitable balance along three primary axes: change (ranging from stability to adaptation), design (ranging from total self-organisation to purposeful) and control (ranging from integration to autonomy) (Beer, 1979). The metasystem is responsible for establishing and maintaining a balance among these dimensions, its success in doing so is highly dependent on meeting requisite variety, which becomes apparent through positive and negative feedback. Feedback mediates between system goals and system operations at each level of recursion within a system. The metasystem may increase the requisite hierarchy of the system by strengthening regulatory ability, or reducing the uncertainty of available regulators, in order to maintain desired levels or regulation and control.

A system’s response to external perturbations as it struggles to maintain identity and viability becomes a causative factor for future effects, influencing them in a manner particularly subtle, variable, flexible and of an endless number of possibilities, as described by circular causality. This axiom suggests that consideration should be made in how the system is designed to detect changes in the operational environment that may affect the continued existence of the system.

5.8 Integrated axiom construct

A system may be identified as such if it operates and can be understood within this interconnected set of axioms; conversely, any entity operating by and understood through the seven axioms is, by definition, a system (Adams et al., 2014). Adams et al. (2014) displays the system theory axiom set as a construct of a system (Figure 9) and defines construct as “a characteristic that cannot be directly observed and so can only be measured indirectly” (p.13).


Figure 9 The systems theory axiom set (see online version for colours)

6 Conclusions

The theoretical basis of systems theory increases our understanding of real world systems and provides for improved interpretation, while supplying the fundamental underpinning for analysis of complex systems. The construct for systems theory presented in this paper provides a foundation for understanding multidisciplinary systems by improving our ability to explain and predict the behaviour derived from the natural order of systems, enabling holistic analysis and problem solving. An associated language of systems is enabled in the assimilation of systems theory, which becomes a ‘lens’ to facilitate the interpretation of complex systems and related problems by allowing us to ground our observations in a theoretical-based foundation. Systems theory is also multidisciplinary in application, as it is removed from traditional unidisciplinary problem solving approaches. As such it provides an ideal groundwork for the consideration of governance in complex systems.

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