Critical Complexity – Paul #Cilliers

Collected Essays Edited by Rika Preiser #amreading

(NB: most of this book’s content was written before Cilliers death in 2011 so please keep this context in mind in light of some of his views esp around the topics of AI, neural nets, etc)
Theme 1: Characterising Complexity

Chapter 1 – The brain, the mental apparatus and the text: A post-structural neuropsychology #Cilliers
The characterisation of the brain as an open system where entities have no significance on their own, but derive significance from their relationships with the other components of the system, bears a striking resemblance to Saussure’s characterisation of language. #Cilliers
Because all the signs are related, any changes or any additions will eventually reverberate through the whole system, and in the end change even the agent of change. #Cilliers ->
<- Such change, however, is always incremental and therefore Saussure’s system will not tolerate the concept of an epistemological break. #Cilliers
Despite his insistence that language is a system that transcends the individual user, the sign remains for Saussure a psychological entity. Although the sign has no essential nature, the speaker always somehow gets it right. #Cilliers
Because the signifier and the signified are only fully united in the speaking subject, speaking is, for Saussure the true form of language. In writing, the close relationship between the speaker and his [sic] intention breaks down. #Cilliers
To describe the mode of interaction in an open system, Derrida introduces the concept of “différance”. The 1st sense of the word results from Saussure’s descrptn of lang as a system of differences. Traces are traces of difference,even if the differences are not of equal strength.
In the play of differences, meaning is generated. However, this play is always still in progress, the meaning is never produced finally, but continuously deferred. #Cilliers
As soon as meaning is generated for a sign, it resonates with the system, and this disturbance in the traces is invariably reflected back, shifting the meaning of the sign in question, even if imperceptibly. #Cilliers
The sign is real, but has no essence. Like the sign, the neuron only has significance in terms of its relationships with others. It is therefore constituted by the play of traces. #Cilliers
The venture to create artificial intelligence – that is to program computers so that they behave in an intelligent fashion – is the culmination of logocentric thought. #Cilliers
It is often thought that the computer plays such an important role in our culture because it is such a powerful tool, because it performs so many important functions that it insists itself on our thinking. In a way, it is the other way round. ->

Computers are important b/s they fit perfectly into a tradition of thought that could be traced from Plato through Descartes, Leibniz, Kant, British empiricism & logical positivism to the instrumental thinking that pervades much of modern scientific, economic & political thought.
<- It is a tradition that believes in the existence of abstract truths, raw data and stands under the spell of the Rule. Computer technology in general, and Artificial Intelligence in particular, marches under the same banner. #Cilliers
Because there is such a strong association between the formal systems of computers & the formal models of lang, the reasons for the failure of computers to simulate natural intelligence is closely related to the failure of formal grammars to give an adequate description of lang.
The idea of using neuron-like structures for computation can be traced back to the work of McCulloch and Pitts in the early forties. The first implementations of neuron-like computation devices were the perceptrons of the early sixties, but their success was shortlived. #Cilliers
Towards the end of the sixties Minsky and Papert (1969) provided a mathematical proof that the problems that can be addressed by perceptrons are severely limited, and this discouraged research in the field of neural networks for quite some time. #Cilliers
Towards the eighties it was realised, however, that the limitations of perceptrons can be overcome by means of changing its structure, and “Neural Networks” was established as a viable field of research. #Cilliers
The first important characteristic of neural nets is that knowledge is not represented locally in an iconic fashion (as is the case in conventional computers & rule-based systems), but that it is distributed over the whole system. #Cilliers
The second characteristic follows from this fact. If the functioning is purely relational, the system cannot be rule based on a first level, because there are only interactions (traces). ->

The network may appear to follow rules, but these are emergent properties that are abstracted from the functioning of the network, and not principles that determine the functioning. If the system is not rule-based, it is of course also non-algorithmic. #Cilliers
As a result of the distributed & parallel nature of these systems, they have a characteristic that they share with holograms, language & the brain, which is “graceful degradation”. Damage to any specific part of the system does not result in the loss of a specific characteristic.
Because info is distributed over the whole system, damage to a specific part will result in a slight deterioration of the performance of the system as a whole, but not in the loss of something specific. This is an important characteristic of all distributed systems. #Cilliers
The kinship between neural networks and post-structural models should be clear: no distinction between levels, no overarching algorithm, but everything in terms of relations. Relations not between positive entities, but always only relations of relations or traces. #Cilliers
Although they may in practice still have a closer resemblance to the structuralist descriptions of Saussure than to post-structural descriptions, their affinity with the spirit of post-structuralism is perhaps best exemplified by their non-algorithmic nature. #Cilliers
There is no programmer, no Scientist that can uncover the full Truth and the final significance of each element. There is, and was, always only the relationship of traces. #Cilliers
The end of chapter 1, #Cilliers
Chapter 2 -- Rules and relations

Some connectionist implications for cognitive science and language #Cilliers
Connectionism is a theory of mental activity inspired by models based on the functioning of the brain. #Cilliers
Neurons are interconnected in large networks with complex connection patterns, and since the weights determine the influence of one neuron on another, the characteristics of a network is determined by the values of these weights. #Cilliers
When a number of neurons are interconnected in a network, each neuron is continuously calculating its output in parallel with all the others, and patterns of activity, determined by the values of the weights, flow through the network. #Cilliers
The topology of the network, that is, the way in which the neurons are interconnected,is also important. A network can be sparsely connected, richly connected or fully connected. A fully interconnected network is one where every neuron is connected to every other neuron. #Cilliers
During a training period, the network can generate these weights automatically. Any specific weight has no significance, it is the patterns of weights in the whole system that bear information. #Cilliers
Since these patterns are complex & are generated by the network itself (by means of general learning strategy applicable to whole network), there is no algorithm available to describe the process used by the network to solve the problem. There are only complex patterns of r/ships
In a full-blown neural network no node has any specific significance. the significance lies in the values of the weights. Not, and this is crucial, in the value of any specific weight or even group of weights, but in the way they are related and activated each time. #Cilliers
Information is not stored in a symbol and recalled when necessary, as in traditional cognitive models, it is reconstructed each time that that part of the network is activated. #Cilliers
In many areas of science, where both theory and practice are concerned, there is a growing discontent with analytical and deterministic methods and descriptions. ->

One of the first responses to this unease was a rapid growth in statistical approaches, not only in the interpretation of experiments, but in the explanation of the results as well.

To think in terms of r/ships, rather than in terms of deterministic rules has always been seen as a part of qualitative descriptions & not as part of the quantitative descriptions & calculations deemed necessary ever since Kepler’s insistence that “to measure is to know”.
Many phenomena, especially in the life sciences, but also in physics and mathematics, simply cannot be understood properly in terms of deterministic, rule based or statistical processes. ->

Quantum-mechanical descriptions of subatomic processes are essentially relational, and even on a more macroscopic level, relations determine the nature of matter. ->

A striking eg of the importance of r/ships comes from mathematics known as fractal geometry. Normal Euclidean descriptions are often useless in describing natural shapes like clouds, rivers, mountains, turbulent flow. Nature does not often produce straight lines/smooth curves.
In the light of these examples, it is certainly strange that when it comes to descriptions of the functioning of the brain, an obviously relational structure, there is still such a strong adherence to deterministic algorithms. #Cilliers
<- One of the reasons for this is that cognitive science inherited its methodological framework from a deterministic, analytical tradition. This is in serious need of revision, and in this revision post-structural theory will have to play an important role. #Cilliers
Meaning is determined by the dynamic relationships between the components of the system. In the same way, no node in a neural network has any significance by itself – that is the central implication of a distributed representation.

<- Significance is derived from patterns of activity involving many units, patterns that result from a dynamic interaction between large numbers of weights. #Cilliers
Adopting a post-structural perspective on science will certainly be in conflict with much of what is accepted as canonical theory of science, but may have less radical effects on the practice of science than one expects. ->
<- Unless one would want to call the opening up of new space for creative thought something radical. #Cilliers
End of chapter 2 #Cilliers
A distinction is made in complexity theory between things that are “complicated” and things that are “complex”. Something that is complicated can have many components, and can be quite intricate, but the relationships between the components are fixed & clearly defined. #Cilliers
We can use the analytic method to analyse complicated things, i.e., we can take them apart and put them back together again, like a jumbo jet. #Cilliers
Something that is complex, on the other hand, is constituted through a large number of dynamic, nonlinear interactions. Therefore the important characteristics of a complex system are destroyed when it is taken apart, i.e., when the relationships between components are broken.
Living things, language, cultural and social systems are all complex. The behaviour of complicated things can be described by rules; the behaviour of complex systems is constituted through relationships. #Cilliers
Complex things have emergent properties, complicated things do not. Emergent properties are those we cannot predict merely by analysing the components of the system. Consciousness is an emergent property of the brain that cannot be predicted by examining a neuron. #Cilliers
The behaviour of complicated things, however, is predictable – as it mostly should be. No one would fly in a jumbo jet with emergent properties. #Cilliers
Something that can be understood fully in terms of a set of rules can, at best, be complicated. Chess may indeed have many possibilities that have not yet been realised, but all of these novelties can still be understood in terms of the basic, static, timeless rules of the game.
Because of the nonlinearity of the interactions constituting a complex system, it cannot be “compressed”. Any simplifying model will have to leave out something, and because of the nonlinearity, we cannot predict the significance of what is suppressed. ->

In order to capture all the complexity, we will have to “repeat” the system in its entirety. This is just as problematic. Since complex systems interact with their environment in intricate ways, it is never obvious where the limits of the system are. ->

When we deal with complexity, we cannot avoid framing our description thereof in some way or another. Models can, therefore, not function in an objective way, they have to be interpreted. #Cilliers
A rule based approach is not adequate when we want to model complex systems. [However, this is not to say] that rules are not important or useful. #Cilliers
The logic of the notion “rule” implies a certain generality. A rule should apply “without exception to the cases subsumed by the description incorporated in the rule” #Cilliers
Rule-based models generate descriptions of what such systems do, and perhaps how they are supposed to do it. They are not “wrong” or useless, they're all we have when we want to develop understanding of complex systems. We must merely be clear about the limitations of our models.
<- In order to do this, we cannot just talk of rules in a blanket fashion, we need to differentiate between different kinds of rules. #Cilliers
A distinction is often made between descriptive and prescriptive rules. This distinction relies on the existence of a clear differentiation between facts and values, one that is problematic to maintain, especially in the context of complex systems. #Cilliers
If one wants to generate a formal model of a complex system in terms of a set of rules, a model that can be simulated on a computer, the distinction between regulative and constitutive rule should be clearly understood. ->

The nitty-gritty rules of the model, those that determine or constrain its behaviour, are regulative rules. One can spend a lot of time developing and refining them, but one should not forget that they only have meaning in terms of the framework constituting that meaning. ->
When dealing with complicated things, the constitutive framework can normally be determined precisely, at least in principle. When modelling complex systems, however, the constitutive framework is not given, nor is it self-evident in a straightforward fashion. ->

In order to generate some general understanding, the framework has to reduce the complexity. A framework is selected in terms of the aims of our description of the system. The quality and usefulness of the model are primarily determined by this selection. #Cilliers
There is nothing mystical about the workings of a complex system. However, since the nature of the system is the result of countless, local, nonlinear, nonalgorithmic, dynamic interactions, it cannot be described completely and accurately in terms of a set of rules. #Cilliers
We cannot avoid the reduction of complexity in the process of modelling. #Cilliers
The patterns of structure in a complex system are much messier. Their borders are not clearly defined. They overlap and interpenetrate each other. #Cilliers
Rule-based models will not be able to provide general and accurate descriptions of complex systems, particularly not of things like human beings. #Cilliers
Large-scale, highly connected recurrent networks can serve as a general model of complex systems, but with qualifications... Even if we build these models, they would cease to be “models” in the sense that they reduce complexity & thereby improve our understanding of the system.
From the argument for the conservation of complexity – the claim that complexity cannot be compressed – it follows that a proper model of a complex system would have to be as complex as the system itself. ->

As a result, the behaviour of the model will be as complex – and unpredictable – as that of the system itself. Is this conclusion a desperate one? It may be for those scientists and managers who still dream of a perfect grip on reality, usually in order to control it. ->
For the rest of us, it serves as a reminder that our capabilities are limited, that there are limits to our understanding of the world. Nothing of what I said can be construed as an argument not to engage with those limits enthusiastically. #Cilliers
End of chapter 3 #Cilliers
Chapter 4 -- What can we learn from a theory of complexity? #Cilliers
Complexity theory has implications for the way we conceive of the structure of an organisation, as well as for the way in which complex organisations should be managed. However, a preliminary warning is necessary ->

The lessons to be learned from the study of complexity are somewhat oblique. Any hope that a study of complex systems will uncover the way of running an organisation is in vain. #Cilliers
General characteristics of complex systems. #Cilliers

These characteristics are not offered as a definition of complexity, but rather as a general, low-level, qualitative description.
Complexity and chaos – whether in the technical or the colloquial sense – have little to do with each other. A complex system is not chaotic, it has a rich structure. One would certainly not describe the brain or language, prime examples of complex systems, as “chaotic.” #Cilliers
Complexity is not compressible #Cilliers
We cannot accurately determine the boundaries of a complex system, because it is open.

In order to model a system precisely, we have to model each & every interaction in the system, each & every interaction with the environment – which is of course also complex–as well as each and every interaction in the history of the system. #Cilliers
<- In short, we will have to model life, the universe and everything. There is no practical way of doing this. #Cilliers
This is not the same as saying that complex systems are chaotic. Emergence is not a random or statistical phenomenon. Complex systems have structure, and, moreover, this structure is robust.

<- This does not imply that there is no point in developing formal models of complex systems either. We can develop models on the basis of certain assumptions and limitations, just as with any scientific model. #Cilliers
Since we don't know boundaries of the system, we never know if we've taken enough into consideration. We've to make selection of all possible factors, but under nonlinear conditions we'll never know if something that was left out b/c it appeared to be insignificant was indeed so.
A theory of complexity cannot provide us w/ a method to predict effects of our decisions, nor w/ a way to predict future behaviour of the system under consideration. Does this mean we should avoid decisions, hoping they make themselves? Most definitely not. We cannot avoid them.
Without activity in the system, without the energy provided by engaging with the system, it would probably wither away into a state of equilibrium, another word for death. Not to make a decision is of course also a decision. What, then, are the nature of our decisions? ->
Because we cannot base them on calculation only – calculation would eliminate the need for choice – we have to acknowledge that our decisions have an ethical nature. #Cilliers
We cannot shift the responsibility for the decision on to something else – “Don’t blame me, the genetic algorithm said we should sell!”

[Note: #Cilliers wrote this over 20 years ago!]
An awareness of the contingency and provisionality of things is far better than a false sense of security. Such an awareness is also an integral part of the notion “adaptive”. #Cilliers
“Ethics” is part of all the different levels of activities in an organisation. #Cilliers
The ethical position is not something imposed on an organisation, something that is expected of it. It is an inevitable result of the inability of a theory of complexity to provide a complete description of all aspects of the system. #Cilliers
All the models we construct – whether they are formal, mathematical models, or qualitative, descriptive models – have to be limited. We cannot model life, the universe, and everything. #Cilliers
There may not be any explicit ethical component contained within the model itself, but ethics has already played its part when the limits of the model were determined, when the selection was made of what would be included in the frame of the investigation. #Cilliers
<- The results produced by the model can never be interpreted independently of that frame. #Cilliers
We cannot make simple models of complex systems. Their non-linear nature, in other words, their incompressibility, demands that the model of a system be as complex as the system itself. #Cilliers
If it is in the nature of the system to behave, at least sometimes, in novel and unpredictable ways, the model must also do so. #Cilliers
Whatever we take the notion of ethics to mean, our analysis of what we can and cannot learn from a theory of complexity has shown that a proper reflection on complex organisations will have to involve the humanities.->

Perhaps we can describe the humanities as those disciplines that realise that their subject matter cannot be studied only by formal means. #Cilliers
Under certain conditions, a good novel may teach us more about human nature than mathematical models of the brain, or the theories of cognitive psychology. An engagement with the arts should not be a luxury in which we indulge after “work”, it should be intertwined with our work.
The end of chapter 4 #Cilliers
Chapter 5 -- Knowledge, complexity and understanding #Cilliers
When talking about the management of “knowledge”, whether by humans or computers, there is a danger of getting caught in objectivist/subjectivist, fundamentalist/relativist dichotomy. The nature of the problem changes if one acknowledges the complex, interactive nature of knwldg.
For those who want to computerise knowledge, knowledge has to be objective. It must be possible to gather, store and manipulate knowledge without the intervention of a subject. ->

The critics of formalised knowledge, on the other hand, usually fall back on arguments based on subjective or culture-specific perspectives to show that it is not possible, that we cannot talk about knowledge independently of the knowing subject. #Cilliers
The dialectical relationship between knowledge and the system within which it is constituted has to be acknowledged. The two do not exist independently, thus making it impossible to first sort out the system (or context), and then identify the knowledge within the system.
<- This codetermination also means that knowledge and the system within which it is constituted are in continual transformation. What appears to be uncontroversial at one point may not remain so for long. #Cilliers
Complex systems have a history and they cannot be conceived of without taking their context into account. #Cilliers
Complexity is incompressible. There is no accurate (or, rather, perfect) representation of the system that is simpler than the system itself. #Cilliers
In building representations of open systems, we are forced to leave things out, and since the effects of these omissions are nonlinear; we cannot predict their magnitude. #Cilliers
<- This is not an argument claiming that reasonable representations should not be constructed, but rather one that the unavoidable limitations of the representations should be acknowledged. #Cilliers
Reduction of complexity always leads to distortion. What are the implications of the arguments from complexity for our understanding of the distinction between data and knowledge? ->

<- 1) it problematises any notion that data can be transformed into knowledge through a pure, mechanical and objective process.

2) it problematises any notion that would see the two [data and knowledge] as totally different things. #Cilliers
There are facts that exist independently of the observer of those facts, but the facts do not have their meaning written on their faces. Meaning only comes to be in the process of interaction. Knowledge is interpreted data. #Cilliers
We should not allow the importance of machines (read computers) in our world to lead to a machine-like understanding of what it is to be human. #Cilliers
Having access to untold amounts of information does not increase our understanding of what it means. Understanding, and therefore knowledge, follows only after interpretation. #Cilliers
Since we hardly understand how humans manage knowledge, we should not oversimplify the problems involved in doing knowledge management computationally. #Cilliers
Although systems that filter data enable us to deal with large amounts of it more effectively, we should remember that filtering is a form of compression. We should never trust a filter too much. #Cilliers
Good data management is tremendously valuable, but cannot be a substitute for the interpretation of data. #Cilliers
Since human capabilities in dealing with complex issues are also far from perfect, interpretation is never a merely mechanical process, but one that involves decisions and values. #Cilliers
The importance of context and history means that there is no substitute for experience. Although different generations will probably place the emphasis differently, the tension between innovation and experience will remain important. #Cilliers
The end of chapter 5 #Cilliers
Chapter 6 -- Boundaries, hierarchies and networks in complex systems #Cilliers
Why the enthusiasm, and more particularly, why is there so much of [complexity theory] in the organisational sciences? #Cilliers
Complexity theory did not appear on the scene without antecedents. In many ways, it is a continuation of what was done in cybernetics, general systems theory and chaos theory. #Cilliers
<- These disciplines also generated lots of hype – and lots of results but could never quite deliver the theories and tools required for a general theory of complexity. #Cilliers
They did not pay enough attention to the historical nature of complex systems, & consequently to the radically contingent nature of a complex system. Complexity was taken to be symmetrical in time, a point of view no longer tenable after the work of Prigogine. #Cilliers
Is it possible to have a general theory of complex systems? Although we can say a lot of important things about complexity in general, it is not possible to develop a general model for complex systems. This has to do with the meaning of the notions “model” and “complexity”.
Is it possible to have a science of complexity? I would argue that it is, but that it implies a revision of our notion of what constitutes science. #Cilliers
Purely quantitative models of complex systems, which abstract from a set of real properties to numerical values, are problematic. The underlying problem with models of complexity is, however, even more serious. ->
<- No matter how we construct the model, it will be flawed, and what is more, we do not know in which way it is flawed. #Cilliers
In a non-linear world where we cannot track a clear causal chain, something that may appear to be unimportant now may turn out to be vitally important later. Or vice versa. Our models have to “frame” the problem in a certain way & this framing inevitably introduces distortions.
The notion of a constraint is not a negative one. It's not something which merely limits possibilities, constraints are also enabling. They provide a framework that enables descriptions to be built up around it. When dealing w complexity, though, these frameworks cannot be fixed.
Complex systems are neither homogeneous nor chaotic. They have structure, embodied in the patterns of interactions b/n the components. Some of these structures can be stable & long-lived (and are therefore easier to catch in or model), whilst others can be volatile and ephemeral.
These structures are also intertwined in a complex way. We find structure on all scales. In order to see how difficult it is to grasp these structures, it is necessary to look at the boundaries of complex systems, and to the role of hierarchies within them. #Cilliers
Boundaries: One way of dealing with the problem of boundaries is to introduce the notion of “operational closure”. For a system to maintain its identity, it must reproduce itself (internally). These arguments often follow from the work by Maturana and Varela on autopoiesis.
One should be careful not to overemphasise the closure of the boundary. The boundary of a complex system is not clearly defined once it has “emerged”.Boundaries are simultaneously a function of the activity of the system itself & a product of the strategy of description involved.
We can never be sure that we have “found” or “defined” it clearly, and therefore the closure of the system is not something that can be described objectively. #Cilliers
We often fall into the trap of thinking of a boundary as something that separates one thing from another. We should rather think of a boundary as something that constitutes that which is bounded. This shift helps us see the boundary as something enabling rather than as confining.
In a critically organised system we are never far away from the boundary. ->

If the components of the system are richly interconnected, there will always be a short route from any component to the “outside” of the system. There is thus no safe “inside” of the system, the boundary is folded in, or perhaps, the system consists of boundaries only. #Cilliers
Everything is always interacting and interfacing with others and with the environment; the notions of “inside” and “outside” are never simple or uncontested. #Cilliers
Complex systems cannot do without hierarchies. Hierarchies establish unambiguous routes of communication. If the system is hierarchical, an algorithm can be developed that would ensure that information would get from A to B. #Cilliers
Complex systems are not homogeneous things. They have structure, and moreover, this structure is asymmetrical. There are subsections with functions, and for them to exist at all, there has to be some form of hierarchy.

Problems arise, however, when these hierarchies are seen as either too clearly defined or too permanent. #Cilliers
Similar to the notion of boundaries, the structure of a complex system cannot be described merely in terms of clearly defined hierarchies. This is because the structure of complexity is usually fractal, there is structure on all scales. #Cilliers
Part of the vitality of a system lies in its ability to transform hierarchies. Although hierarchies are necessary in order to generate frameworks of meaning in the system, they cannot remain unchanged. As the context changes, so must the hierarchies. #Cilliers
Complexity theory increases our understanding of complex systems like organisations, but it does not present us with tools which can predict or control the behaviour of a specific organisation accurately.

We may be able to learn a lot about the kind of dynamics involved in the functioning of such systems, but we will not be able to use these general principles to make accurate predictions in individual cases.

On identity of an organisation: It may appear from these arguments that such a notion will be difficult to maintain. I think not. The identity of an organisation cannot be static, but neither should it be too fluid. ->

<- It emerges exactly from the way in which the boundaries and the hierarchies of that organisation are simultaneously maintained and transformed. #Cilliers
End of chapter 6 #Cilliers
Chapter 7 -- Why we cannot know complex things completely #Cilliers
Despite wonderful advances in the mathematics & science of complexity, despite clever modelling techniques, despite fantastic computing machines, complexity theory will not lead to a grand science that will solve many difficult outstanding problems of science & philosophy.
The study of the characteristics of complex dynamic systems shows us exactly why limited knowledge is unavoidable – or, to be more precise, why knowledge has to be limited. #Cilliers
The study of complexity, in other words, is not going to introduce us to a brave new world in which we will be able to control our destiny; it confronts us with the limits of human understanding. #Cilliers
As far as complex systems are concerned, our knowledge will always be contextually and historically framed. #Cilliers
[It] is not that there is something metaphysically unknowable about complex systems, but rather that we cannot “know” a system in all its complexity despite the fact that we may be able to model its behaviour on a computer. #Cilliers
Most models of complex systems are used to display general complex behaviour, not to model specific, empirical complex systems. This may remain so, not for metaphysical reasons, but because behaviour of complex models will be as unpredictable as that of the systems they model.
"Knowledge” is one of the words that have become commodified in our times. We talk of a “knowledge industry” and of “knowledge management.” ->

<- These terms create the impression that knowledge is something in which we can trade, independently of the subject that has the knowledge. In this way knowledge is reified, turned into something that “exists”, that can be put on a website. #Cilliers
The term “knowledge,” should be reserved for information that is situated historically and contextually by a knowing subject. Knowledge is that which has meaning, it is the result of a process of interpretation. #Cilliers
The subject is not an independent whole, not a free-floating ego that makes “subjective” observations. It is a complex thing in itself, constituted through the web of relationships. The subject itself can therefore only be understood as something contextualised through & through.
We cannot maintain a representational theory of meaning. Meaning is not something complete and abstract, linked to the sign that represents it, but is the result of a dynamic interaction between all the meaningful components in the system, itself a complex process. #Cilliers
If meaning is relational, not representational, there are potentially an infinite amount of relationships at stake each time the meaning of something is generated. #Cilliers
For meaning or knowledge to exist at all, there have to be limits. We cannot comprehend the world in all its complexity. #Cilliers
We have to reduce that complexity in order to generate understanding. This is not some terrible fate that befell human subjects, it is merely the result of having to deal with the world in real time with finite means. #Cilliers
Limits and boundaries: a boundary is something with two sides, like the boundary of a country. A limit, on the other hand, we can only know from one side, that is, we cannot know what is beyond it. #Cilliers
Without falling back into a crude dichotomy between epistemology and ontology, we could argue that the world itself does not have limits, only boundaries. ->

Limits exist in our understanding and descriptions of the world (keeping in mind that these descriptions are not arbitrary constructions, but that they are constrained by reality, that they are “about” the world). ->

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