Concerning Systems

To understand the complexity that might be hidden behind a system, the best is to define it.

Following Mandel's (2011) claims, in a simple sentence, “a system is a family” with many constituents. However, there are just so many kinds of systems one could say almost everything is a system. As an antagonism, a “thing” by itself is not a system, but no one knows exactly what a

“thing” is. Consequently, due to these difficulties the best approach is to describe how a system behaves instead of defining it: “a system is what a system is doing” (Mandel, 2011).

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More formally, in its General System Theory, Bertalanffy (1976) defines a system as a “set of elements standing in interrelations among themselves and with environment”, i.e. a set of interoperable components combined in such a way as to perform a given function under specified conditions. Indeed, according to his view, a machine is an example of a tangible and clear cut system, but other exist which are not as tangible, e.g. systems involving people (e.g. cooperating in a workshop or within an enterprise) (Ho, 1998; Senge, 1994). The concept of system is recursive and may be composed of other sub-systems which are tangible or intangible interacting elements, thus one may generalise that a system is a view on the whole: a set of interacting entities performing a given activity that can be influenced by the surrounding environment (see Figure 3.1) (Couture, 2007).

Figure 3.1: View of a System

The system’s communication with its environment allows all necessary exchanges through the system’s boundaries, even though transformation and production mechanisms are influenced by internal rules, beliefs, constraints, culture and internal models (Holland, 1996). Despite that, most systems share a common number of characteristics, namely the behaviour with respect to the inputs, variables and conditioners. Moreover, they are evolving with respect to time, thus are not considered as static sets of elements. For example, a static hardware part may potentially become a system when put in action with other elements such as humans or software processes (Couture, 2007). This dynamical aspect is essential to support concepts of complexity theory as in CAS.

3.1.1. Classification of Systems

There are multiple ways of characterizing systems (e.g. Ackoff (1971); Freetutes.com (2007); G.

Lewis (2007); Selik et al. (2010); etc.), yet, classification is hardly complete or perfect for all purposes. As explained before, the most common method for approaching systems is regarding their function, but one can also look at systems concerning their tangibility, since they can be abstract and conceptual (e.g. formulas, representations, or models of real systems) or even physical if they are tangible entities that can be touched. In the last case, they can also be seen as static or dynamic in nature, and natural or designed, depending whether they are the product of human creation. Finally, regarding the interaction with the environment, systems are either open, or closed where no interaction with the environment is envisaged.

System Environment

Inputs / Variables / Constraints Output(s)

P a g e | 59 It is also possible to obtain a simple classification by analysing the properties of a system’s behaviour. In this case, they can be time continuous, discrete, or neither; they can be linear or nonlinear; time invariant or time varying; as well as stable or unstable. Also the causality might be important to consider when analysing a system. All "real-time" systems must be causal because output depends only on current or past inputs, but not future. With these variations, one can conclude that depending on the purpose of the analysis, systems can be classified differently. In fact, authors have developed along the years many classifications for systems, but among the most known are the classifications summarized in Table 3.1.

Table 3.1: Some Classifications of Systems

Classification Description Example

Ackoff (1998)

1. Deterministic/Mechanistic Neither parts nor whole has choice Machines

2. Animated The whole has choice, but not the parts People

3. Social Both parts and whole have choice Corporations

4. Ecological Some part have choice, but not the whole Earth

Miller & Miller (1982) _{1. Cells} Free-living or colonial forms -

2. Organs Carry out organism processes -

3. Organisms Multi-cellular plant and animal life forms -

4. Groups Two or more organisms interacting as systems -

5. Organisations Two or more echelons in their decider structures -

6. Societies Self-subsistent system -

7. Supranational systems Two or more societies United Nations

Boulding (1956)

1. Frameworks Static Bridge

2. Clock-works Simple dynamics with predetermined motion Engine 3. Thermostat Transmission and interpretation of information Thermostat

4. Open systems Self-maintaining structure Cell, River

5. Genetic Societal Division among cells to form a cell-society Plant 6. Animal Increased mobility, theological behaviour and

self-awareness

Birds

7. Human Intrinsic purpose or motivation, and self-consciousness

Human beings

8. Socio-cultural systems Distinguished by the role of humans in the environment

Families

9. Transcendental systems Unknowable’s -

Gharajedaghi (2005) 1. Passive One structure and one function in the environment - No choice on both

Linear tools

2. Reactive Different structure but a single function on the environment - No choice on both

Self-maintaining systems 3. Responsive Several structures and functions on the

environment - Has choice of means, not ends

Goal-seeking systems 4. Active Several structures and functions on the

environment - Has choice of means and ends

Purposeful systems Finally, important distinctions have also been made between hard and soft systems, where:

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 Hard systems are associated with areas such as systems engineering, operations research and quantitative systems analysis; and

 Soft systems, which are commonly associated with methods such as action research and emphasizing participatory designs.

It is often stated that “hard” systems thinking is appropriate in well-defined technical problems (systemic) and that “soft” systems thinking is more appropriate in fuzzy hill-defined situations involving human beings and cultural considerations (“the world is fuzzy but the enquiry is systemic”) (Checkland, 1999). In this line, the creation of information systems (IS) is usually a relevant, and often a core, concern of hard systems whose functionalities are emphasised by the technology that supports them. However, following a soft system methodology, it is important to separate the two (systems and technology) to encourage more appreciation for the human aspects of information systems, i.e. the human-interaction.

Continuing with this premise, organisations and their subsystem IS (which are open systems that interact with their environments) must consider human activity subsystems as part of the overall system modelling process, and therefore classification of information systems cannot be focused only on functional/transactional processing (Freetutes.com, 2007). Management and decision support must be also considered, where they can be treated as responsive systems according to the Gharajedaghi (2005) classification (see summary Table 3.2).

Table 3.2: Analysis of Systems

(Ackoff, 1998) Deterministic/Mechanistic Animated Social Ecological

Miller & Miller

Systems theory or science is the interdisciplinary study of the abstract organisation of phenomena, independently of substance, type, spatial or temporal scale of existence. It investigates both the principles common to all complex entities, and the (usually mathematical) models used to describe them (Heylighen & Joslyn, 1992). According to systems theory, all systems, however complex they are, have some kind of organisation which is often independent from the specific system or domain

P a g e | 61 (Frei et al., 2007). In this sense, rather than reducing an entity (e.g. the human body) to the properties of its parts or elements (e.g. organs or cells), systems theory focuses on the arrangement of the whole, i.e. holism (Bertalanffy, 1976).

Cybernetics is a closely related discipline that treats the aspects of communication and control by focusing on circular feedback mechanisms in complex systems (Frei et al., 2007). It has been established by Wiener (1965) as the science of communication and control in the animal and the machine, and focuses on how anything (digital, mechanical or biological) processes and reacts to information while changing itself. This approach of “Systems Thinking” is fundamentally different from that of traditional forms of analysis (Checkland, 1999). It is an attempt to avoid the reductionism of natural science, which focuses on separating the individual pieces of what is being studied. Systems’ thinking, in contrast, focuses on how the object of study interacts with the other systems of the same environment. This means that instead of isolating the smaller and smaller parts of the system being studied, systems thinking works by expanding its view to take into account larger numbers of interactions with same-level systems.

Conclusions obtained following a systems thinking analysis are sometimes strikingly different than those generated by the traditional forms of analysis, especially when what is being studied is dynamically complex or has a great deal of feedback from other sources. Examples of areas where systems thinking has proved relevant, include (Aronson, 1996):

 complex problems that involve helping many actors to see the big picture (e.g.

management);

 recurring problems or those that have been created by attempts of solving them;

 issues where actions affect the surrounding environment.

Cybernetics and systems thinking are well established disciplines in management literature and could provide, in principle, interesting theories to certain Enterprise Interoperability problems.

However, they are limited in scope to the boundaries of a system and by the perspective of holism.

Cybernetic systems operate at the level of basic processes that are relatively undisturbed, while systems’ thinking is an approach to problem solving when substantial changes occurred in processes, viewing disruptions as parts of an overall system. However, neither can deal with major environmental changes of collaborative networks (Schary, 2007). In fact, the information age has highlighted the complex nature of systems, which move between ordered and disordered states, and networked enterprises are an emerging logical form of organising (Black & Edwards, 2000).

3.1.3. Complex Systems

Complex systems are a particular type of systems that cannot be described by a single rule. They originally have multiple objectives but still exhibit unexpected features not contained within their specification (Couture, 2007). Therefore, complex systems have rich dynamics with patterns and fluctuations on many scales of space and time, along with the absence of equilibrium. In fact, a key ingredient of a complex system, are the non-linear interactions among its constituents. Under special circumstances, they can give rise to coherent, emergent, complex behaviours with a very

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rich structure. These cannot be attributed to single separate subsystems but rather to a collective effect, like in general systems theory, where the whole is seen as more than the sum of its parts (Bertalanffy, 1976).

Complex systems interrelationships are described by Gharajedaghi (2005) depending on the level and compatibility of their inner elements and finalities as:

 Cooperation – elements and finalities are compatible;

 Coalition – compatible elements but not finalities;

 Competition – compatible finalities but not elements; and

 Conflict – elements and finalities are incompatible.

All describe the capacity of a system to achieve its mission. Also, one can observe an intuitive judgment of the degree of complexity following Boulding’s classification of system types (see Table 3.1). His types range from structures and frameworks at level 1 to transcendental systems at level 9 with new complexity characteristics emerging at each level, and any problem exhibiting the characteristics of a certain level cannot be solved with a lower level system (Boulding, 1956).

Most complex systems are computationally irreducible, thus the only way to decide upon its evolution is to actually let them evolve in time. This means that no trivial single configuration of the system will satisfy all interactions simultaneously. Whereas simple and complicated (mechanical) systems are described in terms of Euclidian geometry, complex systems are better understood by drawing upon fractal geometry and related concepts of recursion, feedback, and self-similarity (University of Alberta, 2011). Nevertheless, what is most striking is that complex systems that apparently have little in common, e.g. a collection of machines in a manufacturing plant, nodes in a business network, or even a group of human agents in an economic setting, often share remarkably similar structures and behaviour (Couture, 2007). Thus, many natural phenomena can be considered to be complex systems, and their study (complexity science - section 3.2) is highly interdisciplinary.