a. How We Understand a Complex Universe

How We Understand a Complex Universe


In this article I will describe the way in which we human beings make sense of the world that we inhabit, and the implications of this for the theory of General and Social Systems.


My starting point is the concept of the relationship. Normally, to describe a relationship, we use a diagram showing an arrow, the relationship, between two points, the related entities. However, this image can be misleading. A relationship is not something separate and distinct from other physical entities in the universe. Rather it comprises the two related entities in conjunction for a period of time. Thus, the relationship is also a physical entity, albeit one comprising two parts. The nature of the relationship is the nature of the conjunction of these parts.

A valid scientific theory is a statement of a causal relationship, in which entities of one type, the cause, always result in entities of another type, the effect. In fact, it comprises the cause and the effect for so long as the causal relationship exists between them. These theories are a subset of all relationships, and the same principles apply to them. A causal relationship differs mainly in that several recognised and named causes may be necessary for the effect, but it is their un-named and unrecognised conjunction that is sufficient for it to occur.

Physical Entities

Relationships do, of course, relate two physical entities. Every physical entity can be regarded as lying on a scale of complexity, from the smallest and least complex ones known by us, to the entire universe. The sub-atomic level is the lowest level of complexity at which we know all of the building blocks of the universe. This comprises three particles, i.e., protons, neutrons, and electrons, together with four fundamental interactions, i.e., the weak nuclear force, the strong nuclear force, the electro-magnetic force, and gravity. The latter interactions can be regarded as relationships between the particles. There are, of course, lower levels of complexity, comprising quarks and leptons, but they and their interactions are not yet fully understood. So, for the purpose of this article, protons, neutrons, and electrons will be treated as elementary particles.

Measuring Complexity

Every physical thing or entity can be regarded as lying on a scale of complexity, from a simple sub-atomic particle or interaction to the entire universe. The term “complexity” does not imply that the entity is either ordered or disordered. Rather, it merely refers to the number of sub-atomic particles and interactions that comprise it.

Ultimately, every physical entity, its properties, and its relationships with other entities are the consequence of a complex of sub-atomic particles and their interactions. The more complex an entity, the greater the number of particles and interactions. The same is true of relationships. The complexity of the relationship is the sum of the complexity of its two components.

Meaningful Entities and Relationships

If we draw a boundary around any part of the universe, call everything within it an entity or relationship, and give it our focus of attention, then for the most part its contents will be random, disorganised, and meaningless. However, in some cases the boundary will contain organisation and order. The more ordered an entity, the more likely it is to recur. The entities and relationships which are meaningful to us are those which we recognise as recurring. We then symbolise them by, for example, naming them, or creating an image of them.


However, we lack the ability to store and process the amount of information involved in representing more complex entities via their sub-atomic particles and interactions. There is a threshold of comprehension beyond which we are unable to understand entities in this way.

To overcome this, we simplify at or before this threshold of comprehension is reached. We do so by identifying a new set of elementary entities and relationships from recurrences at that level of complexity. For example, in chemistry, our elementary entities are atoms, and our relationships are atomic bonds. In sociology, our elementary entities are individual people, and our relationships are their social interactions.

We then use these new elementary entities and relationships to deal with greater complexity. They have substantially less information content and, initially, remain within our threshold of comprehension.

Ultimately, however, with further increases in complexity, each simplification reaches the threshold of comprehension once more. To remain within it we must simplify yet further. This leads to several fields of knowledge, each with its own elementary entities, relationships, and theories, each dependant on the speciality below, and each lying on a path of increasing complexity.

Possible Limits to Simplification

With each simplification, information is lost. It is also true that, unless emergent theories are based on careful and accurate observations of the real world, with each simplification comes the introduction of error. There is, therefore, likely to be an upper threshold to complexity beyond which a reliance on pure theory cannot take us. Observation is necessary to progress further.

However, there are difficulties in observing reality at very high levels of complexity. In general, the more ordered the elementary components and relationships within an entity, the greater the likelihood of it recurring and being recognised. However, it is also true that the more complex it is, the greater its size, and the greater its size, the less likely it is that we will be able to perceive it. Furthermore, it is less likely that it will recur within a timeframe that allows us to recognise its recurrence. Thus, there may be an upper threshold to complexity beyond which we are unable to perceive recurrence, and thus, recognise and name entities, including scientific theories.


Because each relationship, and thus, each scientific theory relies on a minimum amount of complexity, it cannot also apply in a less complex field. Thus, it will appear to emerge as complexity increases. However, the reverse is not necessarily the case. A relationship which requires relatively little complexity can, of course, exist within an entity of much greater complexity.


Increases in complexity can follow different paths, e.g., from an elementary particle to the cosmos, or from an elementary particle to an ecosystem. Different valid scientific theories emerge on each path. Those which emerge for life will, for example, differ for those which emerge for astro-physics.

The following path of increasing complexity is relevant to human social systems. At each level new theories emerge:

  1. Logic.
  2. Causality.
  3. Physics.
  4. Chemistry.
  5. Biochemistry.
  6. Evolution.
  7. Social Sciences, i.e., Psychology, Social Psychology, Sociology, Economics, Political Science.
  8. Ecology.

The path for astro-physics is, of course, different.

The Search for Understanding in Practice

In the above article, I have described the process of understanding reality from the elementary particles and their interactions upwards. However, in practice, the starting point in our search for understanding was reality at the human scale, i.e., the world in which we live and its direct impacts upon us. From here, the search has not only been in the upward direction towards ever greater complexity, but also in the downward direction towards ever less complexity. Both processes are ongoing, but the more we understand what underlies the sub-atomic world, the more this increases the complexity above it.

Implications for General Systems Theory

Because systems and entities are essentially the same concept, the implication for General Systems Theory is, of course, that there is no single set of rules applicable to all systems. So, for example, human social systems will have their own set, some of which are shared by less complex levels, and some of which are particular to the field. Any General Systems Theory is therefore likely to be a meta-theory, i.e., a theory of theories, that explains what theories emerge on a particular path of increasing complexity, and why. This would require an explanation of the relationships between simplifying concepts at one level of complexity, and those at the levels above and below.

Implications for the Social Sciences

In the social sciences, the search is for valid macro-causal rules, or theories which emerge at, as yet un-simplified higher levels of complexity. However, in the same way as other entities, their recognition depends on their recurrence. Unfortunately, the greater the level of complexity, the less frequently these recurrences will be observed. Furthermore, the larger the scale at which they operate, the less likely it is that we will recognise them.

To add to these difficulties, human behaviour is caused by emotion, knowledge, and reasoning skills. If we were to recognise a new macro-causal rule, then this would constitute new knowledge, and might alter our behaviour. This, in turn, might invalidate the theory. For example, if it becomes known that an event, x, always causes war, then, whenever x is encountered, effort will be put into avoiding its consequence. So, rather than stating that “x always causes war”, the theory should in fact state that “x, and not knowing that x always causes war, and the absence of any inhibitors always cause war”.

The decreasing likelihood of our recognising causal rules or theories as complexity increases could well mean that there is a maximum level of complexity at which human recognition can occur. If so, then beyond that point, causal rules are unrecognisable, and so, not affected by human agency. However, whilst they are fixed, we are unable to recognise and take advantage of them.

If for example, they could be identified using advanced artificial intelligence, or by a General Systems Meta-theory, then the newly discovered macro-causal rules would no longer be fixed, and the threshold would move upwards. This increased knowledge would, of course, also alter our culture.

Is New Social Knowledge Worth Pursuing?

Given all these difficulties, the reader may question whether new social knowledge is worth pursuing. Personally, I believe that it is, but that an ethical framework is needed to control its use. New knowledge can change human behaviour for the better, for example, by avoiding war. However, it can also change it for the worse. An elite may, for example, keep the knowledge to themselves and use it to manipulate others, as may already be the case in the fields of politics and advertising. To avoid this, it is important that there be open access to any new knowledge in the social sciences, that it be used ethically, and that these requirements are policed.

c. Further Principles of General Systems Theory

Further Principles of General Systems Theory

I will describe General Systems Theory in more detail in the next few articles, and then provide a systems based model which can be used to understand human society, how it works, and why it sometimes fails. This model uses the principles described below.

Near Decomposability. Many natural and artificial systems are structured hierarchically, and their components can be seen as occupying levels. At the highest level is the system in its entirety. Its components occupy lower levels. As we move down through the levels we encounter ever more, smaller, and less complex components. The rates of interaction between components at one level tend to be quicker than those at the level above. The most obvious example of this is the speed with which people make decisions. An individual can make decisions relatively quickly, but the rate steadily slows as we move up the hierarchy through small groups, organisations, and nations, to global society.

Sub-optimisation. This principle recognises that a focus on optimising the performance of one component of a system can lead to greater inefficiency in the system as a whole. Rather the whole system must be optimised if it is to perform at maximum efficiency. Its components must sometimes operate sub-optimally.

Darkness. This principle states that no system can be known completely. The best representation of a complex system is the system itself. Any other representation will contain errors. Thus, the components of a system only react to the inputs they receive, and cannot “know” the behaviour of the system as a whole. For the latter to be possible then the complexity of the whole system would need to be present in the component. The expression “black box” is used to describe a system or component whose internal processes are unknown, and “white box” to describe one whose internal processes are known. Most systems are, of course, “grey boxes”.

An interesting question arises from the principles of near composability and darkness. As explained in previous articles, human beings are motivated by needs and contra-needs. The question is, of course, whether groups of individuals, species, and ecosystems also have needs and contra-needs which differ from their individual members. Are reduced birth rates, for example, a natural species response to population pressures? If so, then near decomposability implies that, because groups, species, and ecosystems are more complex systems than single individuals, the processes which satisfy those needs will proceed more slowly. Darkness implies that as individuals we would be unable to “know” the processes involved, although as a society we might.

Equifinality. The processes in a system can, but do not necessarily, have an equilibrium point, i.e., a point at which the system normally operates. If, for any reason, the processes are displaced from it, then they will subsequently alter to approach that point once more. This characteristic is known as homeostasis. Thus, a given end state can be reached from many initial states, a feature known as equifinality. For example, if a child’s swing is displaced from the vertical and released, then, after swinging to and fro for a while, it will eventually return to the vertical.

Multifinality. It is possible for the processes in a system to have more than one stable point. If a process is displaced a little from one of them, it may ultimately return. However, if it is displaced too far, then it may subsequently approach another equilibrium point. This is a feature of natural ecosystems. If they are damaged in some way, they will ultimately return to a stable state. However, this state will often differ from the earlier, damaged, original.

Dynamic Equilibrium. This principle is like that of equifinality but applies to rates of change in systems. Some systems are dynamic and have a stable rate of change. If displaced from that rate of change for any reason, they will ultimately return to it. This is known as homeorhesis, a term derived from the Greek for “similar flow”. Again, a dynamic system may have several stable rates of change.

Relaxation Time. Relaxation means the return of a disturbed system to equilibrium. The time it takes to do so is known as the relaxation time.

Circular Causality or Feedback. Feedback occurs when the outputs of a system are routed back as inputs, either directly or via other systems. Thus, a chain of cause and effect is created in the form of a circuit or loop. The American psychologist Karl Weick explained the operation of systems in terms of positive and negative feedback loops. Systems can change autonomously between stable and unstable states depending on the dominant form of feedback. Feedback is, therefore, the basis of self-maintaining systems which will be discussed in the next article.

b. General Systems Theory

General Systems Theory

In this article, I will describe a branch of science known as General Systems Theory. I will do so because it provides an extremely powerful set of tools for understanding human nature and society.

The aim of General Systems Theory is to provide an overarching theory of organisation which can be applied to any field of study. It aims to identify broadly applicable concepts rather than those which apply only to one field. It can, therefore, apply in the fields of mathematics, engineering, chemistry, biology, the social sciences, ecology, etc. One of the principal founders of General Systems Theory was the Austrian biologist Ludwig von Bertalanffy (1901 – 1972), although there have been many other contributors. To date, its principal application has been in the popular fields of business, the environment, and psychology, but it is equally applicable to human nature and society.

A system comprises a collection of inter-related components, with a clearly defined boundary, which work together to achieve common objectives. Within this boundary lies the system, and outside lies its environment. Systems are described as being either open or closed. In the case of a closed system, nothing can enter it from, or leave it to, the environment. It is a hypothetical concept, therefore. In reality, all systems are open systems comprising inputs, processes and outputs to the environment. In a closed system, the 2nd Law of Thermodynamics applies, entropy will steadily increase, and the system will fall into disorder. However, in an open system, it is possible to resist decay, or even to reverse it and increase order.

In summary, an open system comprises inputs, processes, and outputs. In the case of an individual human being, our inputs are satisfiers and contra-satisfiers, our processes comprise our needs, contra-needs and decision-making, and our outputs are our behaviour.

The basis of General Systems Theory is causality. Everything we regard as being a cause or effect comprises components, which can also be regarded as causes and effects. Ultimately, causality has its foundation in particle physics, therefore. Furthermore, every cause or effect is a component of yet greater causes and effects, up to the scale of the universe in its entirety. Similarly, General Systems Theory regards everything from the smallest particle to the entire universe as a system. Thus, every system comprises components which are also systems, and every system is a component of yet greater systems. A system, a cause, and an effect are all one and the same thing, therefore.

In causality, events of one type cause events of another type by passing matter, energy or information to them. These are the equivalent of the inputs and outputs of a system. As Einstein explained, matter is organised energy. Information is also conveyed in the way that matter or energy are organised. So, causality is the transfer of energy, in an organised or disorganised form, from one system to another. This transfer can be regarded as an output from the cause, and an input to the effect. Causes and effects form chains or loops, and so create recurring, and thus, recognisable patterns of energy flow. It is such recognisable patterns that enable us to understand and predict the world in which we live, and which are of interest to General Systems Theory.

Causes can, of course, be necessary or sufficient. For a system or system component to carry out its function, several inputs from the environment or other components may be necessary. Only together may they be sufficient for the system to function. Furthermore, inhibitors also have a part to play in preventing effects on processes. Thus, the relationships between a system and its environment, and the relationships between the components of a system can be complex and chaotic.

A feature of systems is that they often display emergent properties. These are characteristics that the component parts of a system do not have, but which, by virtue of these parts acting together, the system does have. In other words, “the whole is more than the sum of its parts”. This concept dates to at least the time of Aristotle. The classic example is consciousness. A human being experiences consciousness, but his or her component cells do not. Similarly, systems also display vanishing properties. These are properties that a system does not have, but which its component parts do. For example, individual human beings may be compassionate but an organisation comprising such people may not. Emergent and vanishing properties are thought to be related to the way that energy is organized and flows in a system. They are recognizable patterns of energy flow.

Continuum changes of state occur when a variable characteristic of something alters. For example, when a child puts on weight or grows in height. System complexity is one such variable characteristic. Changes in a variable characteristic can be imperceptible in the short term but aggregate over time until we can perceive them. For example, in the longer term, a person can change his or her state from that of being a child to that of being an adult, but the changes which occur in a week are imperceptible. Emergent and vanishing properties are thought to be continuum changes of state which occur as the complexity of systems grow. They can be identified by comparing things that are similar, but either more or less complex than one another, e.g., a chimpanzee and a human being.

We tend to think of systems as falling into categories which are organised hierarchically, e.g., the popular categories:  animal, vegetable, and mineral. The best way of categorising the levels in a hierarchy of systems is via emergent properties. This is because with new properties, new rules also emerge. One emergent property of particular importance is self-maintenance. This appears in life, beginning with replicative molecules and moving up through viruses, bacteria, and multi-cellular organisms, to ourselves. This self-maintenance property is the same as life’s struggle to maintain its integrity in the face of entropy.

Self-maintaining systems are characterised by two types of feedback loop. One is internal and the other external. The internal feedback loop is known in systems theory as the command feedback loop. It gathers information from within the system and modifies its operation. The external feedback loops are particularly relevant to human society. They comprise the system interacting with its environment, through its outputs, to create circumstances conducive to the supply of its necessary inputs. The goal of both is, of course, to ensure the continued survival of the system in changing circumstances.

Individual human beings, organisations, and societies can be regarded as systems. So too can the natural environment in which we live, for example, the weather and natural ecosystems. However, their behaviour can be chaotic rather than deterministic. We can predict them to a limited extent, but the probability of any prediction proving correct diminishes as distance into the future increases.