Everything I've encountered on "systems theory" suggests to me that there is no such thing. The writings generally consist of a large quantity of words about the definitions of other words, but no mathematics and no predictions that were not already there before pulling it into the ambit of "systems".
Are there any counterexamples to this?
I consider control theory to be a part of systems theory and given that you give a talk on the virtues of control theory I think you value it. Apart from that my thoughts about the space:
If you look at Seth Roberts's Shagri-La diet it's based on systems thinking. It gives different answers than the standard nutritional paradgim which thinks that losing weight is about linear effect of eating less and exercising more.
You don't need any math for understanding the Shangri-La diet but you do need a certain intellectual framework that considers systems to be important.
Mathematical predictions are only one aspect a theory can provide. Systems theory provides phenomenlogical primitives that can prevent you from dismissing the Shangri-La as strange and obviously crazy. It provides you with a better ontology that allows you to consider new solutions.
Hakob Barseghyan describes in his HPS100 course very well how the notion of a life force being important for biology came after Newton changed the accepted ontology and that people basically thought that there's matter and that matter interacts via forces with other matter. Our current mainstream ontology of physicalism doens't consider that a place for a vital force exists.
If we go back to Seth Roberts, Seth considers it a good idea to measure the fitness of a human by measuring the reaction time to short math queries. I don't think Seth wrote anywhere that he's measuring a "vital force" by doing so, but if you go back in history you find that people have measured the vital force via reaction tests.
Gunnar Stollberg argues that modern systems biology has a concept like the vital force with self-organisation/autopoiesis. A reemergence of vitalism could help explain why a student person who writes down their ideal life or writes about an emotional trauma for 4 days afterwards lead to significantly less sickness as Laura King showed in "The Health Benefits of Writing About Life Goals".
Systems theory puts us in the position were we don't have to postulate any paranormal chi for a notion like life force to exist. It doesn't need math for that task.
I consider control theory to be a part of systems theory and given that you give a talk on the virtues of control theory I think you value it.
I certainly value the theory of control systems, and I think everyone should know its basic concepts. But the real thing looks like this (and all of the stuff that that links to). This is quite unlike what I've seen under the banner of "systems", including some of the references in the OP.
To be more positive, I get from some of your examples the idea that "systems thinking" means "not being stupid." Specifically, not being the sort of stupid that consists of thinking up a theory that is "obviously" true and failing to see whether it is. I don't have a problem with that sort of "systems theory".
Gunnar Stollberg argues that modern systems biology has a concept like the vital force with self-organisation/autopoiesis.
But he concludes by admitting that biologists have not taken this up (and briefly, absurdly, considers the hypothesis of a conspiracy to suppress it).
This is where it seems to me to wander off into the fog. Vitalism is an idea with no moving parts. As soon as you have moving parts to explain the phenomena that people pointed to and said "vital force!" as an explanation, the notion of vital force, goes away. Likewise autopoiesis. The observation that organisms maintain certain variables fixed despite disturbing influences is not explained by giving the phenomenon a name. The thing for a scientist to do is to discover the mechanism. For example, where does a mammal's body sense its core temperature? How is the reference signal generated, and raised in case of fever? And so on. When the whole story is known, we know not merely that it self-regulates, but how.
To be more positive, I get from some of your examples the idea that "systems thinking" means "not being stupid."
Not being stupid in way where the majority of our society is stupid.
But he concludes by admitting that biologists have not taken this up
Just as the majority of nutrition scientists haven't taken up Seth Robert's Shangri-La diet or did research in that direction.
When hearing the discussion of a topic like homeopathy I never see references to the fact that quizing a patient for two hours for the trauma of his life instead of talking with him for 5 minutes has a good chance to have benefitial health effects. It's well replicated that writing about your trauma's creates health benefits. That's because of the paradigm in which our medicine is practiced.
Calling that mysterious variable that get's raised by writing about trauma "vital force" might not be a good explanation but once you go that step you can ask new research questions. If you allow people to simple call it vital force you allow new questions. Is there a way to measure it? Can we measure the "vital force" a day after the trauma writing and see whether the writing worked at raising the vital force? Can we then use that score to predict days of illness?
The thing for a scientist to do is to discover the mechanism
If you believe that's the only thing scientists are allowed to do then they won't be able to do work where predictions can be made but where the underlying mechanism is illusive.
Would you forbid psychologists from talking about IQ and g because they can't tell you the mechanism in which IQ/g works?
Currently dualism isn't dead. Psychologists who work on the mind are allowed to use the concept of IQ without providing an mechanism but biologists are not allowed to do something similar with a life force metric.
Just be complete, no I don't think that there's a conspiracy that forbids biologists from doing so. It's just that the paradigm and people being stupid. I think systems theory provides a way out.
Would you forbid psychologists from talking about IQ and g because they can't tell you the mechanism in which IQ/g works?
It depends what they say about it. There are observable and fairly robust statistical correlations from which g can be constructed, and g can be used to make (rather weak in the individual case) predictions of various sorts. That does not make g a thing. I predict that if we ever find out how the brain works, g will not be a part of that mechanism, just a rough statistical regularity, as it is at present.
Currently dualism isn't dead. Psychologists who work on the mind are allowed to use the concept of IQ without providing an mechanism but biologists are not allowed to do something similar with a life force metric.
If life force is going to be the same sort of thing as g, it might be useful in medicine, which to a substantial and increasing extent is based on statistical trials with little knowledge of mechanisms. But I don't see it as useful for research into how things work.
If life force is going to be the same sort of thing as g, it might be useful in medicine, which to a substantial and increasing extent is based on statistical trials with little knowledge of mechanisms. But I don't see it as useful for research into how things work.
I think that "finding out how things work" should not be the goal of science. The goal should be to develop models that provide reliable and useful predictions.
Newton postulate gravitation as a force without telling his audience how gravity works. The fact that Newton couldn't explain that slowed down adoption of his model, yet accepting his model brought science a huge step forward. Even on many issues that are about research into how things work. Theories that provide additional predictive power help science advance even if their proponents can't explain everything from the ground up.
To get back to system theory. It allows us to say: "Emergence" when we don't know how something come about and still work with what comes about. When someone tells you that homeopathy doesn't work because there are no infinitively small numbers of atoms he has a valid argument. Our ontological framework doesn't allow the infinitively small numbers of atoms. People who have never heard of systems theory and subfield of it like control theory will have a similar reaction to the Shangri-La diet as to homeopathy. The ontology doens't allow for it.
System theory then allows for an ontology in which it can happen. That's valuable. When you go through a specific example you can also think about what the various words of system theory might be when you apply it to the system you study. That provides you with a structure to model the problem even if you don't have enough data for mathematical modelling.
We have no idea how the set point for blood pressure is that in the human body, but it's worthwhile to think of blood pressure regulation as a sytem that has a set point even if we don't know how that is set. From a medical standpoint we can think differently about the system through looking at it with the lense of system theory.
To get back to the life force, it's good when we get more free and focus on increasing the predictive power of our models without worrying too much about whether we know at the moment the mechanism behind a certain value. Sometimes it can even be useful to free our concepts from wanting to explain mechanisms. A term like Shaken Baby syndrome can be quite problematic if you find out that 1% of the cases of babies with "Shaken baby syndrome" weren't shaken.
The thing for a scientist to do is to discover the mechanism
If you believe that's the only thing scientists are allowed to do then they won't be able to do work where predictions can be made but where the underlying mechanism is illusive.
"Discover", not "have discovered". Newton's work was a step; Einstein finding more of a mechanism was a further step.
I think that "finding out how things work" should not be the goal of science. The goal should be to develop models that provide reliable and useful predictions.
It's difficult to get the latter without the former, if you want to make successful way-out-of-sample predictions. Otherwise, you're stuck in the morass of trying to find tiny signals and dismissing most of your data as noise.
It's difficult to get the latter without the former, if you want to make successful way-out-of-sample predictions.
I think you can do a lot of successful predictions with IQ without knowing the mechanism of IQ. I don't think you build better IQ tests by going into neuroscience but giving the tests to people and seeing how different variables correlate with each other.
Otherwise, you're stuck in the morass of trying to find tiny signals and dismissing most of your data as noise.
I don't think that's true. The present approach of putting compounds through massive screening arrays based on theoretical reasoning that it's good to hit certain biochemical pathways is very noise-laden and produces a lot of false positives. >90% of drug candidats that get put into trials don't work out.
I think "system theory" used to be called cybernetics and (in its contemporary form) was basically invented by Norbert Wiener.
This might be splitting hairs, but I would probably call it a "framework" in the sense that it provides a context (e.g. language and concepts) within which more specific "theories" exist. Which theory, for example, would consider the similarities between feedback mechanisms in financial markets and in ecological systems?
Which theory, for example, would consider the similarities between feedback mechanisms in financial markets and in ecological systems?
Dynamic systems. I am not convinced it gains from being associated with a wider "systems theory".
Of perhaps, like Molière's bourgeois gentilhomme discovering that he had been speaking prose all his life, the message is that "systems thinking" is what I have always been doing?
Of perhaps, like Molière's bourgeois gentilhomme discovering that he had been speaking prose all his life, the message is that "systems thinking" is what I have always been doing?
Might be :-) I think cybernetics / system theory basically dissolved into a set of disciplines or theories, much like natural philosophy did a long time ago or, say, geography did fairly recently.
I think of it more like a particular lens from which to view problems, i.e. it is an alternative to reductionism. But, perhaps it's most useful aspect is that it allows the development of techniques which can be used to simulate complex systems. Ludwig von Bertalanff described the set of theories that together comprise the framework of systems thought in the following passage:
Now we are looking for another basic outlook on the world -- the world as organization. Such a conception -- if it can be substantiated -- would indeed change the basic categories upon which scientific thought rests, and profoundly influence practical attitudes. This trend is marked by the emergence of a bundle of new disciplines such as cybernetics, information theory, general system theory, theories of games, of decisions, of queuing and others; in practical applications, systems analysis, systems engineering, operations research, etc. They are different in basic assumptions, mathematical techniques and aims, and they are often unsatisfactory and sometimes contradictory. They agree, however, in being concerned, in one way or another, with "systems," "wholes" or "organizations"; and in their totality, they herald a new approach. Quoted from: Systems Theories: Their Origins, Foundations, and Development
If you want to deep dive into complex systems, I found this to be useful.
My impression is that there are a few core ideas which get turned into frameworks by different people every few years because rediscovery + the generative effect are more fun/epiphany inducing than reviewing the entire literature.
WRT predictions: systems theory is about modelling, and modelling is always making implicit predictions about the causal structure of the system. The better 'systems theory frameworks' encourage turning these into explicit predictions/tests.
You seem to be tacit assuming that only a quantiative, predictive theory is a theory at all, but as far as general usage goes, the horse has bolted, because we have critical theory, cultural theory and other such handwavey things.
Many things are called theories, but they are not all the same sort of thing. I know little of critical theory or cultural theory, but I have a very slight acquaintance with music theory, so let me say what sort of thing that appears to be to me, and ask if these other "theories", including systems theory, are a similar sort of thing.
Musical theory is not the same sort of thing as the theory of Newtonian mechanics. It is more like (pre-neo-Darwinian) biological taxonomy (although different in important ways I'll come to). That is, it is an activity of classifying things into a framework, a structure of concepts. It makes no predictions, other than that these regularities will continue to be observed. Just as in taxonomy: when you come across a creature that you identify as a heron, you can be sure of a lot of things that you will subsequently observe if you follow it around. But there is no biology here: the classification is based purely on the appearance (perhaps including the results of microscopy) and behaviour of the organism, with no deeper knowledge to tell you how the variety of creatures came to be, or the biochemical processes by which they function. And just as in the history of taxonomy various classification schemes have been proposed, so in music theory there are alternatives to the standard stuff found in elementary textbooks (e.g. Schenkerian theory, Riemannian theory). There are even flamewars over them in internet forums.
Music theory and taxonomy are more like maps of contingent landscapes than a theory predicting things beyond the observed phenomena.
Biological taxonomy differs from music theory in two important ways. Firstly, the organisms exist independently of the taxonomical activity. In contrast, practitioners of music -- composers and performers -- are influenced by the theories. They create music within the frameworks that were derived from the music before them, or deliberately react against them and invent new theories to compose new sorts of music in, such as serialism.
Secondly, the development of biology has put empirical foundations underneath the taxonomical activity. (Here is a history of that process.)
[ETA: Sometimes to the effect of exposing some of the concepts as purely conventional. We know what physically underlies the concept of a species, and also know how fuzzy it can get. For other parts it has demonstrated that, e.g. there is no such thing as a genus, or a family, or a kingdom, any more than one can empirically distinguish twigs, branches and boughs: all the levels above species are just conventions convenient to have.]
No such empirical foundation exists for music. Composers are free to flout anyone's theory of what they are doing, and are ultimately bound only by the limits of the human ear.
So, I can read "cultural theory" and "critical theory" as being the same sort of activity as music theory. But that is at the expense of reading them as making true statements about something outside of themselves. They are descriptive maps, or rather, a multitude of competing and conflicting maps of the same territory. In fact, the activity of cultural theory might even be considered to be more like musical performance than musical theory. One does not go to a lecture in the area of cultural theory, critical studies, semiotics, and the like to learn true things, but to experience an intellectually entertaining assemblage of ideas floating as independently of the real world as an interpretation of a Rorschach blot.
What do you think? And where does this leave systems theory? If systems theory were like to musical performance I would have little use for it, but I think its practitioners intend a more solid connection to the real world than that. Perhaps it is like taxonomy? Or something else?
The initial definition is equivalent to one in which clauses 2 and 3 are replaced by "Every element is affected by every other". It seems unlikely that this is intended, both because surely there are plenty of things that count as "systems" in which that isn't true and because it would be easier to say it directly if it were intended. But it's not like it's difficult to see that clauses 2 and 3 have this implication (consider a "subgroup" consisting of just elements A and B; then clause 2 says A is affected by B and B is affected by A).
This leaves me a bit unimpressed with the quality of Ackoff's thinking. (And doesn't do much to dispel my prejudice along the same lines as Richard Kennaway's.)
EDITED to add: It looks as if I misunderstood what Ackoff meant by clause 3. My criticism may therefore be invalid. See discussion downthread.
I don’t think this: “All possible subgroups of elements also have the first two properties” is the same as “All possible subgroups of elements can themselves be considered systems and so must have the first two properties”, which it looks like you are reading it as. This means that rule 2: “Each element is affected by at least one other element in the system” says that the subgroup of elements you have selected can be affected by an element that is in the system, but not in the subgroup of elements you have selected.
For example, imagine that the corners in this square represented four elements and the lines the relations between them.
As per my understanding of the rules, this is a system. The first two rules are obviously true. If you look at the third one with the elements on the left side of the square, then the two selected elements don’t have any relations to each other, but they do have relations to other elements in the system. So, I believe that this passes the rule.
Ackoff talks a little more about it here.
A system is a set of interrelated elements. Thus a system is an entity which is composed of at least two elements and a relation that holds between each of its elements and at least one other element in the set. Each of a system’s elements is connected to every other element, directly or indirectly. Furthermore, no subset of elements is unrelated to any other subset. (Ackoff, 1971, p. 662)
Oh! So the subgroups are being considered as elements rather than as systems, and condition 3 is actually saying that every set of elements (other than the whole system, I assume) is affected by something outside itself? (Equivalently, however you partition the elements into two partitions there are influences flowing both ways across the boundary.)
You're right: that's a much more sensible definition, and I retract my claim that Ackoff's definition shows bad thinking. I maintain, however, that it shows bad writing -- though perhaps in context it's less ambiguous.
That last quotation, though. At first glance it nicely demonstrates that he has "your" reading in mind rather than "mine"; good for him. But look more closely at the last sentence. "No subset of elements is unrelated to any other subset". In particular, take two singleton subsets; his condition implies once again that every element is "related to" every other. So maybe I have to accuse him of fuzzy thinking again after all :-).
Regenerative cycling (autopoiesis) is another common feature of self-organizing systems. To destroy exergy...
Exergy happens to be a word that most readers likely don't know and you don't define it.
One good definition of emergence is that it is:
the arising of novel and coherent structures, patterns and properties during the process of self-organization in complex systems
Not that good, since novelty is subjective. If the underlying system is deterministic then whatever emerge is predictable given enough computational power.
It has supervenience (downward causation) - The system shapes the behaviour of the parts (roads determine where we drive)
That isn't how supervenience is usually defined.
I rewrote that section, but I meant it like the upper-level properties of a system (where people drive, for example, are determined by its lower level properties (roads).
Thanks to ScottL for writing this concise yet (apparently) thorough overview of systems theory. I've long been curious about systems theory, mostly because the term systems biology sounds interesting, and this helps scratch that itch.
I may "Ankify" it, at least for org-drill.
RichardKenneway's posts here also added a lot of value. Based on this introduction, I basically agree that systems theory is a map without much predictive value. But I'll add that a map, or a vocabulary if you will, is useful in that it lets us indicate what we're talking about.
ScottL's Ludwig von Bertalanff quote indicates that systems theory was invented about the time we started thinking about systems in general for real - biological systems, software systems, etc. At some point, you start needing some more precise language to use to build predictive theories.
BTW, I think of Marxism's dialectic as more-or-less of a systems theory. Like systems theory, the dialectic has passionate adherents, and a lot of people who think it's incoherent. I find it very moderately useful.
I would it appreciate it if you could Ankify this knowledge and find one sentence descriptions of the individual terms to be able to learn them with Anki.
I don't think the time spent trying to create one sentence descriptions would help me to understand the terms better. They would help me memorize them, but that isn't my goal.
In my experience trying to focus on the essence of a concept does help with understanding it better. But if Wikipedia already has a good list I will use that.
Below are some notes that I took while trying to understanding what exactly Systems theory is all about.
System
There is no universally agreed upon definition of ‘system’, but in general systems are seen as at least two elements that are interconnected. It is also common for systems to be talked about as if all of the components in the system work together to achieve some overall purpose or goal. The primary goal is often survival. A commonly accepted definition is below (note that the word ‘element’ is often replaced with ‘component’ for generality purposes):
Non-systems are generally considered to be single instances or a set of elements that lack interconnections, although these may be part of a system.
Environment
The environment is often referred to as the context in which the system is found or as its surroundings. Systems are considered closed if they have no interaction with their environment. It is often the case that systems are considered closed for practicality reasons even though they may not technically be absolutely closed, but just have limited interaction with their environment.
Boundary
The boundary is the separation between the system and environment. The actual point at which the system meets its environment is called an 'interface'. It is often the case that the boundary is not sharply defined and that boundaries are conceptual rather than existing in nature.
Interactions (Inputs/Outputs)
Closed systems are those which are considered to be isolated from their environment. This property of 'closedness' is often required in scientific analysis as it makes it possible to be able to calculate future states with accuracy. The problem is that many systems are open, for example, living organisms are open systems that exchange matter with their environment. A living organism requires oxygen, water and food in order to survive. It gains all of this by interacting with its environment. This interaction has two components: input, that which enters the system from the outside and output that which leaves the system for the environment.
Subsystem and supersystem
The environment can itself consist of other systems interacting with their environment. A greater system is referred to as a super system, or suprasystem. A system that contains subsystems is said to have a hierarchy. That is different levels in the system may be different sets of systems. An intuitive idea demonstrating hierarchy, specifically nested hierarchy, is that of Russian nesting dolls. Other types of hierarchies include :
(Booch, et al., 2007)
Hard and soft systems
Systems are commonly differentiated based on whether they are hard or soft. Hard systems are precise, well defined and quantifiable whereas soft systems are not. With soft systems, the system doesn’t really exist and is instead a label or theory about some part of the world and how it operates. The hard and soft difference is really about different approaches in how to view the world systemically. The hard system approach sees the world as systemic and the soft system approach sees the process of inquiry as systemic:
Complexity
Some concepts which are related to and sometimes mistaken for complexity are (Edmonds, 1996, pp. 3-6):
There are many definitions of complexity. Most of them revolve around the idea that the complexity of a phenomenon is a measure of how difficult it is to describe. One example of a decent definition that avoid the problem described above is:
Another common definition that is used is:
The second definition highlights the point that complexity often leads to an inability for a single language or single perspective to describe all the properties of a system. This means that multiple languages and different perspectives are required just to understand a complex system. This has a very important consequence. It means that no single perspective is absolutely correct there are multiple truths and values, although some are more correct than others.
Organisation
Complexity is normally viewed as being either of the type organised or disorganised. Disorganised complexity problems are ones in which the Law of Large Numbers works. This means that even though there may be a multitude of agents all interacting together their stochastic elements average out and so become predictable (on average) with statistics. Said another way, individual variation tends to reduce potential predictability, but the aggregate behaviour, if the individual variations cancel each out, can be predicted. An example would be rolling a die. The exact outcome cannot be known, assuming the die is not loaded, but if you have a large enough sample size you can know that the average result is (3.5). Problems of organised complexity on the other hand are not problems:
Complex Systems
Although there is no formally accepted definition of complexity or complex systems, there are a number of intuitive features that appear in many definitions (Heylighen, 2008, pp. 4-7), (Ladyman, Wienser , & Lamber, 2013)
The below features are also found and will be described in their own sections below:
Feedback
Feedback is a circular causal process in which some portion of a system’s output is returned (fed back) into the system’s input. Feedback is an important mechanism in achieving homeostasis also known as steady state or dynamic equilibrium. An example of a feedback mechanism in humans would be the release of the hormone insulin in response to increased blood sugar levels. Insulin increases the body’s ability to take in and convert glucose. This has an overall effect of restoring the blood sugar levels back to what they originally were.
Positive feedback is when small perturbations (system deviations) reinforce themselves and have an amplifying effect. An example is emotional contagion. If one person starts laughing, then this is likely to make others start laughing as well. Another example is the spread of a disease, where a single infection can eventually turn into a global pandemic. In positive feedback the effects are said to larger than the causes. When it is the other way around (the effects are smaller than the causes), then you have negative feedback. Negative feedback is when perturbations are slowly supressed until the system eventually return to its equilibrium state. Negative feedback has a dampening effect.
Positive feedback can have an effect of amplifying small and random fluctuations into unpredictable and wild swings in the overall system behaviour, which would then be considered chaotic. Negative feedback makes a system more predictable by supressing the effect of such swings and fluctuations. A consequence of this predictably is a loss of controllability. If negative feedback is present, then a system when pushed out of its equilibrium state will undertake some action to return to it. An example in social systems would be social protest when leaders or governments try to implement unwanted changes.
Interactions that involve positive feedback are very sensitive to their initial conditions. An extremely small and often undetectable change in the initial conditions can lead to drastically different outcomes. This is known as: “the butterfly effect”. The phrase refers to the idea that a change as tiny as the flapping or non-flapping of a butterfly’s wings can have a drastic effect on the weather patterns in another location in the world even going so far as leading to a tornado. Please note that the flapping of the wings does not cause the tornado. They are one instead just one part of the initial conditions that caused the tornado. The flapping wing represents a tiny, seemingly insignificant, change in the initial conditions that turns out to be extremely significant due to a cascading i.e. domino effect.
The butterfly effect is actually a concept relating to chaotic systems. It is important to note that if the initial conditions of the chaotic system were unchanged between two simulations to an infinite degree of precision, the outcome of the two will be the same over any period of time. This means that the systems are still deterministic. A similar, but distinct notion in complex systems is the ‘global cascade’ (Watts, 2002). This is basically a network-wide domino effect that occurs in a dynamic network. It has been noted that systems may appear stable for long period of time and be able to withstand many external shocks and then suddenly and apparently for no explicable reasons exhibit a global cascade. For this reason, systems are both robust and fragile. They can withstand many shocks making them robust, but global cascades can by triggered by shocks that are indistinguishable from others which have previously been withstood. Due to the fact that the original perturbations can be undetectable, the outcomes are then in principle unpredictable.
Complex systems tend to exhibit a combination of both positive and negative feedback. This means that the effects from certain changes are amplified and others dampened. This leads to the overall system behaviour being both unpredictable and uncontrollable.
Self-organization
The second law of thermodynamics says that “energy spontaneously tends to flow only from being concentrated in one place to becoming diffused and spread out.” (Lambert, 2015). An illustrating example is the fact that a hot frying pan cools down when it’s taken off the kitchen stove. Its thermal energy ("heat") flows out to the cooler room air. The opposite never happens.
The second law of thermodynamics might at first glance appear to be implying that all systems need to degrade and cannot be sustained, but this is not the case. The second law of thermodynamics was formulated based on a separate class of phenomena (steam engines originally) than living systems. The original class relates to steady state phenomena close to thermodynamic equilibrium (having the same thermodynamic properties, e.g. heat). Living and more complex systems are steady state phenomena far from thermodynamic equilibrium. They are not isolated but depend on a steady flux of energy that is dissipated to maintain a local state of organisation.
In other words, at the macro level there is an apparent reduction in entropy(measure of the spontaneous dispersal of energy), but at the micro level random processes greatly increase entropy. The system exports this entropy to its environment for example when we breathe we excrete carbon dioxide.
The term waste is not really suitable for the products of excretion because they may actually be used as input for other systems. Plants excrete oxygen which we humans require to survive. A better term is negentropy which is the entropy that a living system exports in order to keep its own entropy low. So, in summary living systems delay decay into thermodynamical equilibrium, i.e. death, by feeding upon negentropy in order to compensate for the entropy that they produce while living or to put it even more simply they suck orderliness from their environment.
Autopoiesis
Regenerative cycling (autopoiesis) is another common feature of self-organizing systems.
The reason why more complex systems tend to be nested is that nested complex systems may have a larger capacity to degrade exergy because of the multiple layers of the network reinforcements by feedback than non-nested systems.
Dissipative structures
The view of self-organization that has been covered so far leads nicely into ‘dissipative structures’.
A whirlpool is an example of dissipative structure and it could have been called ‘doubly dissipative ‘because it requires a continuous flow of matter and energy to maintain its form. When the influx of external energy stops or falls below a certain threshold, the whirl pool will degrade. Other examples of dissipative structures include refrigerators, flames and hurricanes.
Attractors
In relation to self-organization, the term attractor will come up frequently. It is a mathematical term which refers to a value or set of values toward which variables in a dynamical system tend to evolve. A dynamical system is a system whose state evolves with time over a state space according to a fixed rule. A state space is the set of value that a process or system can take.
Attractors emerge, or at least will get stronger, when systems are moved out of equilibrium. Exergy is the energy that is available to be used. After the system and surroundings reach equilibrium, the exergy is zero.
One of the most common ways in which systems reach these attractors is through simple and random fluctuations which are then amplified by positive feedback. This process is referred to as: “order from noise”, a special case of the principle of selective variety. In summary, “order from noise” means that random perturbations ("noise") cause the system to explore a variety of states in its state space. This exploration increases the chance that the system will arrive into the basin of a "strong" or "deep" attractor, from which it would then quickly enter the attractor itself.
Multiple equilibria occur when several different local regions of the same phase space are attractors. Minor perturbations can cause the system to shift between different equilibria or attractors, causing abrupt and dramatic changes in the system.
Thresholds
Thresholds mark the borders between different equilibria. This means that crossing crossing thresholds can have dramatic changes in the system. The term 'threshold' is used to broadly define the minimum amount of change that is required before impacts cause bifurcations or are recognized as important or dangerous. Thresholds can also be conditionally dependent. That is, there may be many interdependent thresholds or thresholds that become apparent only after other specific conditions have been met. This along with their dependence on initial conditions, couplings with other system components, and rapid change between multiple equilibria often make thresholds hard to predict accurately.
Emergence
An intuitive understanding of emergence can be gained by looking at a painting painted with the technique of pointillism. When you look at it up close, all you can see is dots, but as you move further back the overall image begins to resolve. Unfortunately emergence, although it can be understood intuitively, is not a well clarified concept (Corning, 2002, pp. 6-8).
The concept of emergence is generally seen in contexts where the two metaphysical claims are discussed (Christen & Franklin, p. 1-2):
One well known argument for why argues entities of the world, which evolved under disruptive conditions, are likely to be organised hierarchically is (Simon, 1960, p.470):
Emergence can be categorized into a few different types (Christen & Franklin, p. 6-7):
Adaption
Adaptation is a relationship between a system and its environment. Systems are often classified as adaptable (able to be modified by an external agent) and/or adaptive (able to change itself).
An example problem (Ashby, 1960, p. 11) demonstrating the concept of adaptive behaviour is that of the cat and fire. The cat’s behaviour in response to the fire is likely to at first be unpredictable and inappropriate. It may paw at it or stalk it like it is a mouse or walk unconcernedly onto it. It is unlikely use the fire as a method to achieve homeostasis in body temperature. That is it may sit far from the fire even when cold. Later, when the cat has had enough relevant experience with the fire it will approach the fire and seat itself in a place where the heat is moderate. If the fire is burning low, it will move nearer. If a hot coal happens to fall out, it will jump away. Its behaviour towards the fire is now considered ‘adaptive’.
Resilience
A nice way of thinking of resilience is as follows:
It is important to note that resilience doesn't mean that the system is static or constant. Resilient system can be and often are very dynamic. Short-term oscillations, fluctuations and long cycles of climax and collapse may be the norm. Systems that are constant over time can be un-resilient. This presents a problem because people often desire that systems be measurable and for variations over time periods to be minimised. Most people are unaware of what actually makes a system resilient as it is often hard to see.
Complex Adaptive Systems
Many natural systems, e.g. brains, immune systems, societies, are complex adaptive systems. Complex adaptive systems display the complexity of complex systems, but they are also able to adapt and evolve with a changing environment. It is often referred to as co-evolution rather than just as adaptation to a single distinct environment. This is because other systems are in the environment.
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