Terminology


This glossary contains a vocabulary used by CPER concerning the overall site, regarding: meaning of scientific words, useful definitions, short explanations of some concepts, and references to reliable external sources of information on the Internet or on paper.

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HISTORY

Autopoiesis

Autopoiesis

Explanation

Autopoiesis refers to a system that is capable of creating, maintaining and reproducing itself. Autopoietic mechanisms can operate as self-generating feedback systems.

Historical Frame

The term was introduced in 1972 by Chilean biologists Humberto Maturana and Francisco Varela to define the self-maintaining chemistry of living cells. Since then the concept has been also applied to the fields of systems theory and sociology.

Autopoiesis was originally presented as a system description that was said to define and explain the nature of living systems. A canonical example of an autopoietic system is the biological cell. The eukaryotic cell, for example, is made of various biochemical components such as nucleic acids and proteins, and is organized into bounded structures such as the cell nucleus, various organelles, a cell membrane and cytoskeleton. These structures, based on an external flow of molecules and energy, produce the components which, in turn, continue to maintain the organized bounded structure that gives rise to these components.

Autopoiesis: Components - Boundary - Processes

 

Related concepts

Allopoietic system

An autopoietic system is to be contrasted with an allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized structure) which is something other than itself (the factory). However, if the system is extended from the factory to include components in the factory's 'environment', such as supply chains, plant / equipment, workers, dealerships, customers, contracts, competitors, cars, spare parts and so on, then as a total viable system it could be considered to be autopoietic. Thus, an autopoietic system is a closed topological space that continuously generates and specifies its own organization. It maintains this through its operation as a system of production of its own components, and does this in an endless turnover of components. Autopoietic systems are thus distinguished from allopoietic systems, which have as the product of their functioning something different from themselves.

Practopoiesis

A theory of how autopoietic systems operate is named Practopoiesis (praxis + poiesis, meaning creation of actions). The theory presumes that, although the system as a whole is autopoietic, the components of that system may have allopoietic relations. For example, the genome combined with the operations of the gene expression mechanisms create proteins, but not the other way around; proteins do not create genomes. In that case poiesis occurs only in one direction. Practopoietic theory presumes such one-directional relationships of creation to take place also at other levels of system organisation.

Self-organizing Intelligence

Many scientists have often used the term autopoiesis as a synonym for self-organization. An autopoietic system is autonomous and operationally closed, in the sense that there are sufficient processes within it to maintain the whole. Autopoietic systems are "structurally coupled" with their medium, embedded in a dynamic of changes that can be recalled as sensory-motor coupling. This continuous dynamic is considered as a rudimentary form of knowledge or cognition and can be observed throughout life-forms. Autopoiesis would be the process of the emergence of necessary features out of chaotic contingency, causing contingency's gradual self-organisation, thus leading to the gradual rise of order out of chaos.

Linguistic derivation

The term Autopoiesis is derived from ancient Greek words auto- (αὐτo-) meaning "self", and poiesis (ποίησις), meaning "creation" or  "production". 

External sources

http://en.wikipedia.org/wiki/autopoiesis

Book: Maturana, H., & Varela, F. (1992). The tree of knowledge: The biological roots of human understanding. Boston: Shambhala.

 

Internal links

 

Entry link: 
  Autopoiesis

Cybernetics

Cybernetics

Explanation

Cybernetics is the scientific study of how people, animals, and machines control and communicate information (for example, via feedback loops). Control mechanisms according to cybernetic principles, are also found in genetic evolutionary processes, as well as in the emergence and development of ecosystems.

Cybernetics investigates and describes the regulation and control in animals (including humans), in organizations, and in machines when they are viewed as self-governing whole entities, consisting of parts and their dynamic organization.

Cybernetics views communication and control in all self-contained complex systems as analogous. It differs from the empirical sciences (physics, biology, etc.) in not being interested in material form but in organization, pattern, and communication in entities. Because of the increasing sophistication of computers and the efforts to make them behave in humanlike ways, cybernetics today is closely allied with artificial intelligence and robotics, and it draws heavily on ideas developed in information theory.

Law of Requisite Variety

The total amount of cybernetic knowledge deposited within a system is related to the total number of different states that the system can assume while interacting with the environment. This is referred to as the cybernetic variety of the system. The demands on variety are determined by Ashby’s Law of Requisite Variety (Ashby 1958; Beer 1974), which states:
"For a successful control of a system, the system that controls has to have at least as many states as the system being controlled."
Thus, being a good model of the environment entails a sufficient number of states, which is a pre-requirement to store a sufficient amount of cybernetic knowledge within the systems.
Generally speaking: Knowledge requires variety.

Good Regulator Theorem

Cybernetic knowledge is necessarily subjected to Conant & Ashby’s Good Regulator Theorem (Conant & Ashby 1970), stating:
“Any successful control mechanism must be a model of the system that it controls”.
That is, one can deal with the surrounding world successfully only if one already possesses certain knowledge about the effects that one’s actions are likely to exert on that world.
Maturana and Varela (1980, 1992) expressed it as: 
“All doing is knowing and all knowing is doing.”

Historical Frame

The concept of cybernetic was conceived by Norbert Wiener, who coined the term in 1948.

Linguistic derivation

The term Cybernetics is derived from the Ancient Greek words kybernetes meaning "pilot", "governor"; or from kybernan = "to steer", "to govern". 

External sources

http://en.wikipedia.org/wiki/cybernetics

 

Entry link: 
  Cybernetics

Entropy

Entropy

Definitions

  • Entropy is defined as a thermodynamic parameter representing the state of disorder of a system at the atomic, ionic, or molecular level.
  • Entropy is a thermodynamic property which serves as a measure of how close a system is to equilibrium.
  • Entropy is a measure of disorder in a system; the higher the entropy the greater the disorder. In the context of entropy, "perfect internal disorder" is synonymous with "equilibrium".
  • Entropy is a measure of the unavailability of a system’s energy to do work; Thus, thermodynamic entropy is a measure of the amount of energy in a physical system that cannot be used to do work.
  • Entropy is a measure of the dispersal of energy; how much energy is spread out in a process, or how widely spread out it becomes, at a specific temperature.
  • Entropy is the capacity factor for thermal energy that is hidden with respect to temperature.
  • Entropy is a measure of disorder in the universe.
  • Entropy is the tendency of a system, that is left to itself, to descend into chaos.

Second Law of Thermodynamics

According to the second law of thermodynamics the entropy of an isolated system never decreases. An isolated system will spontaneously evolve toward thermodynamic equilibrium, the configuration with maximum entropy.
Systems that are not isolated may decrease in entropy, provided they increase the entropy of their environment by at least that same amount.
Since entropy is a state function, the change in the entropy of a system is the same for any process that goes from a given initial state to a given final state, whether the process is reversible or irreversible.

Irreversibility

The idea of "irreversibility" is central to the understanding of entropy. Most people have an intuitive understanding of irreversibility (a dissipative process): if one watches a movie of everyday life running forward and in reverse, it is easy to distinguish between the two. The movie running in reverse shows impossible things happening: water jumping out of a glass into a pitcher above it, smoke going down a chimney, water "unmelting" to form ice in a warm room, crashed cars reassembling themselves, and so on.
The intuitive meaning of expressions such as "you can't unscramble an egg", "don't cry over spilled milk" or "you can't take the cream out of the coffee" is that these are irreversible processes. There is a direction in time by which spilled milk does not go back into the glass (see: The arrow of time).
In thermodynamics, one says that the "forward" processes – pouring water from a pitcher, smoke going up a chimney, etc. – are "irreversible": they cannot happen in reverse, even though, on a microscopic level, no laws of physics would be violated if they did. This reflects the time-asymmetry of entropy.
All real physical processes involving systems in everyday life, with many atoms or molecules, are irreversible. For an irreversible process in an isolated system, the thermodynamic state variable known as entropy is always increasing.
The reason that the movie in reverse is so easily recognized is because it shows processes for which entropy is decreasing, which is physically impossible.

Entropy as energy dispersal

Entropy can also be described in terms of "energy dispersal" and the "spreading of energy", while avoiding all mention of "disorder", "randomness" and "chaos". In this approach, the second law of thermodynamics is introduced as: "Energy spontaneously disperses from being localized to becoming spread out if it is not hindered from doing so."

This explanation can be used in the context of common experiences such as a rock falling, a hot frying pan cooling down, iron rusting, air leaving a punctured tyre and ice melting in a warm room. Entropy is then depicted as a sophisticated kind of "before and after" yardstick: Measuring how much energy is spread out over time as a result of a process such as heating a system, or how widely spread out the energy is after something happens in comparison with its previous state, in a process such as gas expansion or fluids mixing (at a constant temperature).

The equations are explored with reference to the common experiences, with emphasis that in chemistry the energy that entropy measures as dispersing is the internal energy of molecules.

Chemical reactions

The second law of thermodynamics, states that a closed system has entropy which may increase or otherwise remain constant. Chemical reactions cause changes in entropy and entropy plays an important role in determining in which direction a chemical reaction spontaneously proceeds.

Systems ecology and Negentropy

Nowadays, many biologists use the term 'entropy of an organism', or its antonym 'negentropy', as a measure of the structural order within an organism.

Historical frame

The term entropy was coined in 1865 by the German physicist Rudolf Clausius, who stated that: “The entropy of the universe tends to a maximum.”.

Calculation

Unlike many other functions of state, entropy cannot be directly observed but must be calculated. Entropy can be calculated for a substance as the standard molar entropy from absolute zero temperature (also known as absolute entropy).
Entropy has the dimension of energy divided by temperature, which has a unit of joules per kelvin (J/K) in the International System of Units.
While these are the same units as heat capacity, the two concepts are distinct. Entropy is not a conserved quantity: for example, in an isolated system with non-uniform temperature, heat might irreversibly flow and the temperature become more uniform such that entropy increases.

The arrow of time

Entropy is the only quantity in the physical sciences that seems to imply a particular direction of progress, sometimes called an arrow of time. As time progresses, the second law of thermodynamics states that the entropy of an isolated system never decreases (but rather will increase). Hence, from this perspective, entropy measurement is thought of as a kind of clock (an isolated system has low entopy in the past, and high entropy in the future).
The Second Law of Thermodynamics allows for the entropy to remain the same regardless of the direction of time. If the entropy is constant in either direction of time, there would be no preferred direction. However, the entropy can only be a constant if the system is in the highest possible state of disorder, such as a gas that always was (and always will be) uniformly spread out in its container.
The existence of a thermodynamic arrow of time implies that the system is highly ordered (i.e. low entropy) in one time direction only, which would by definition be the "past". Thus this law is about the boundary conditions rather than the equations of motion of our world.

Linguistic derivation

The term Entropy is derived from the Ancient Greek word entropía (ἐντροπία) meaning “a turning towards”.
This is a combination of the prefix en- (ἐν) meaning "in", and the word tropḗ (τροπή) meaning "a turning", in analogy with energy.

External sources

http://en.wikipedia.org/wiki/Introduction_to_entropy

http://en.wikipedia.org/wiki/Entropy

http://en.wikipedia.org/wiki/Entropy_(order_and_disorder)

http://en.wikipedia.org/wiki/Entropy_(energy_dispersal)

http://en.wikipedia.org/wiki/Entropy_(arrow_of_time)

http://en.wikipedia.org/wiki/Biological_thermodynamics

http://en.wiktionary.org/wiki/entropy 

 

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  Entropy

Evolution

Evolution

Explanation

Biological Evolution is the change in the inherited characteristics of biological populations over successive generations. Evolutionary processes give rise to diversity at every level of biological organisation, including species, individual organisms and molecules such as DNA and proteins. All life on Earth is descended from a last universal ancestor that lived approximately 3.8 billion years ago. Repeated speciation and the divergence of life can be inferred from shared sets of biochemical and morphological traits, or by shared DNA sequences. These homologous traits and sequences are more similar among species that share a more recent common ancestor, and can be used to reconstruct evolutionary histories, using both existing species and the fossil record. Existing patterns of biodiversity have been shaped both by speciation and by extinction.

Historical frame

Charles Darwin (12 Feb. 1809 – 19 Apr. 1882 †) was the first to formulate a scientific argument for the theory of evolution by means of natural selection. Evolution by natural selection is a process that is inferred from three facts about populations: 

  1. More offspring are produced than can possibly survive.
  2. Traits vary among individuals, leading to different rates of survival and reproduction.
  3. Trait differences are heritable.

Thus, when members of a population die they are replaced by the progeny of parents that were better adapted to survive and reproduce in the environment in which natural selection took place. This process creates and preserves traits that are seemingly fitted for the functional roles they perform. Natural selection is the only known cause of adaptation, but not the only known cause of evolution. Other, non-adaptive causes of evolution include mutation and genetic drift.
In the early 20th century, genetics was integrated with Darwin's theory of evolution by natural selection through the discipline of population genetics. The importance of natural selection as a cause of evolution was accepted into other branches of biology. Moreover, previously held notions about evolution, such as orthogenesis (J.B. Lamarck) and "progress" became obsolete. Scientists continue to study various aspects of evolution by forming and testing hypotheses, constructing scientific theories, using observational data, and performing experiments in both the field and the laboratory.  Biologists agree that descent with modification is one of the most reliably established facts in science. Discoveries in evolutionary biology have made a significant impact not just within the traditional branches of biology, but also in other academic disciplines (e.g., anthropology and psychology) and on society at large.

 

CPER's standpoint

Members of CPER differ in their belief system about Biological Evolution from mainstream scientific agreements, in the following way: CPER acknowledges that, when an intelligent animal species emerges from millennial evolutional processes in nature, this species (at the moment: Human Being) can manipulate the outcome of evolutional tendencies. This way, the 'humanimal' starts giving accelerated direction to Progressive Evolution of its own life form, and eventually of future life in general.

 

Linguistic derivation

The term Evolution is derived from the Latin word ēvolūtiō, meaning "unfolding" or "unrolling".

 

External sources

http://en.wikipedia.org/wiki/Evolution .

 

 

Entry link: 
  Evolution

Genotype

The term Genotype, will be explained together with the term Phenotype.

Genotype codes for Phenotype in fly

Genotype

The genotype is the genetic makeup of a cell, an organism, or an individual.
For instance, the human CFTR gene, which encodes a protein that transports chloride ions across cell membranes, can be dominant (A) as the normal version of the gene, or recessive (a) as a mutated version of the gene. Individuals receiving two recessive alleles will be diagnosed with Cystic fibrosis.

Flower - Genotypical variety versus Phenotypal variety
It is generally accepted that inherited genotype, transmitted epigenetic factors, and non-hereditary environmental variation contribute to the phenotype of an individual.

The genotype of an organism is the inherited instructions it carries within its genetic code. Not all organisms with the same genotype look or act the same way because appearance and behavior are modified by environmental and developmental conditions. Likewise, not all organisms that look alike necessarily have the same genotype.

Phenotype

A phenotype is the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, phenology, behaviour, and products of behaviour (such as a bird's nest). A phenotype results from the expression of an organism's genes as well as the influence of environmental factors and the interactions between the two. When two or more clearly different phenotypes exist in the same population of a species, the species is called polymorph.

Phenotypic variation

Phenotypic variation (due to underlying heritable genetic variation) is a fundamental prerequisite for evolution by natural selection. It is the living organism as a whole that contributes (or not) to the next generation, so natural selection affects the genetic structure of a population indirectly via the contribution of phenotypes. Without phenotypic variation, there would be no evolution by natural selection.

The interaction between genotype and phenotype has often been conceptualized by the following relationship:
genotype (G) + environment (E) + genotype & environment interactions (GE) → phenotype (P)

The smallest unit of replicators is the gene. Replicators cannot be directly selected upon, but they are selected on by their phenotypic effects. These effects are packaged together in organisms. We should think of the replicator as having extended phenotypic effects. These are all of the ways it affects the world, not just the effects the replicators have on the body in which they reside.

Historical frame

This genotype-phenotype distinction was proposed by Wilhelm Johannsen in 1911 to make clear the difference between an organism's heredity and what that heredity produces. The distinction is similar to that proposed by August Weismann, who distinguished between germ plasm (heredity) and somatic cells (the body). The genotype-phenotype distinction should not be confused with Francis Crick's central dogma of molecular biology, which is a statement about the directionality of molecular sequential information flowing from DNA to protein, and not the reverse.

Evolution of genetic traits

The genotype–phenotype distinction is drawn in genetics. "Genotype" is an organism's full hereditary information. "Phenotype" is an organism's actual observed properties, such as morphology, development, or behavior. This distinction is fundamental in the study of inheritance of traits and their evolution.

It is the organism's physical properties which directly determine its chances of survival and reproductive output, while the inheritance of physical properties occurs only as a secondary consequence of the inheritance of genes. Therefore, to properly understand the theory of evolution via natural selection, one must understand the genotype–phenotype distinction. The genes contribute to a trait, and the phenotype is the observable expression of the genes (and therefore the genotype that affects the trait). Say a white mouse had both recessive genes that cause the colour of the mouse to be inactive (so "cc"). Its genotype would be responsible for its phenotype (the white colour).

The mapping of a set of genotypes to a set of phenotypes is sometimes referred to as the genotype–phenotype map.

Phenotypical expression of Dominant Brown eys color or Recessive blue ey color
Similar genotypic changes may result in similar phenotypic alterations, even across a wide range of species, for example: a DNA error in a gene necessary for the development of an eye, would result in a malformed eye in most species.

Identical twins

An organism's genotype is a major influencing factor in the development of its phenotype, but it is not the only one. Even two organisms with identical genotypes normally differ in their phenotypes. One experiences this in everyday life with monozygous (i.e. identical) twins. Identical twins share the same genotype, since their genomes are identical; but they never have the same phenotype, although their phenotypes may be very similar. This is apparent in the fact that their mothers and close friends can always tell them apart, even though others might not be able to see the subtle differences. Further, identical twins can be distinguished by their fingerprints, which are never completely identical.

Linguistic derivation

The term Genotype is derived from the ancient Greek word genes (γένος) meaning "born" or "race", and týpos (τύπος), meaning "type".

The term Phenotype is derived from the ancient Greek word phainein / phainō (φαίνω) meaning "to show, to bring to light, make to appear", and typos, meaning "type".

External sources

http://en.wikipedia.org/wiki/Genotype

http://en.wikipedia.org/wiki/Phenotype

http://en.wikipedia.org/wiki/Genotype-phenotype_distinction

 

Entry link: 
  Genotype

Negentropy

Negentropy

Negentropy has also been called: negative entropy, syntropy, extropy or entaxy.

Explanation

The negentropy of a living system is the entropy that it exports to keep its own entropy low. It lies at the intersection of entropy and life. Negentropy has been used by biologists as the basis for purpose or direction in life, namely cooperative or moral instincts.

Historical frame

The concept and phrase "negative entropy" were introduced by Erwin Schrödinger in his 1944 popular-science book 'What is Life?'. Later, Léon Brillouin shortened the phrase to negentropy, to express it in a more "positive" way of resoning: a living system imports negentropy and stores it. In 1974, Albert Szent-Györgyi proposed replacing the term negentropy with syntropy. That term may have originated in the 1940s with the Italian mathematician Luigi Fantappiè, who tried to construct a unified theory of biology and physics. Buckminster Fuller tried to popularize this usage, but negentropy remains common.

System dynamics

In 2009, Mahulikar & Herwig redefined negentropy of a dynamically ordered sub-system as the specific entropy deficit of the ordered sub-system relative to its surrounding chaos. Thus, negentropy has units [J/kg-K] when defined based on specific entropy per unit mass, and [K−1] when defined based on specific entropy per unit energy. This definition enabled: i) scale-invariant thermodynamic representation of dynamic order existence, ii) formulation of physical principles exclusively for dynamic order existence and evolution, and iii) mathematical interpretation of Schrödinger's negentropy debt.

Related fields

The term Negentropy is not only used in physics and biology, but also in other domains, such as Information Theory, Statistics, Organization management, though with a slightly different meaning, for example: In Risk Management, negentropy is the force that seeks to achieve effective organizational behavior and lead to a steady predictable state.

Related concepts

Extropy

Extropy is a concept that life will continue to expand throughout the universe as a result of human intelligence and technology.

External sources

http://en.wikipedia.org/wiki/Negentropy

http://en.wiktionary.org/wiki/negentropy

http://en.wiktionary.org/wiki/extropy

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TERMINOLOGY

Adaptive System

Adaptive system

Explanation

Biological adaptation

The term adaptation is used in biology in relation to how living beings adapt to their environments, but with two different meanings. First, the continuous adaptation of an organism to its environment, so as to maintain itself in a viable state, through sensory feedback mechanisms. Second, the development (through evolutionary steps) of an adaptation (an anatomic structure, physiological process or behavior characteristic) that increases the probability of an organism reproducing itself (although sometimes not directly).

General definition

Generally speaking, an adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts. Feedback loops represent a key feature of adaptive systems, allowing the response to changes; examples of adaptive systems include: natural ecosystems, individual organisms, human communities, human organizations, and human families. Some artificial systems can be adaptive as well; for instance, robots employ control systems that utilize feedback loops to sense new conditions in their environment and adapt accordingly.

The Law of Adaptation

Every adaptive system converges to a state in which all kind of stimulation ceases.

 

Benefit of Self-Adjusting Systems

In an adaptive system, a parameter changes slowly and has no preferred value. In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”. One of the most important qualities of self-adjusting systems is its “adaption to the edge of chaos” or ability to avoid chaos. Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster.

 

Practopoiesis

Adaptation across levels of organization

A theory of how systems adapt across different levels of organisation is called practopoiesis. According to that theory, the purpose of an adaptation processes at each lower level of organisation is creation of the adaptation mechanism at the next higher level of organisation. For a living system such as an animal or a person, a total of three such hierarchical steps of adaptation are needed — and such systems are denoted as T3:

  1. At the lowest level of a T3-system lay gene expression mechanisms, which, when activated, produce machinery that can adapt the system at higher levels of organization.
  2. The next higher level corresponds to various physiological structures other than gene expression mechanisms. In the nervous system, these higher mechanisms adjust the properties of the neural circuitry such that they operate with the pace much faster than the gene expression mechanisms. These faster adaptive mechanisms are responsible for e.g., neural adaptation.
  3. Finally, at the top of that adaptive hierarchy lays the electrochemical activity of neuronal networks together with the contractions of the muscles. At this level the behaviour of the organism is generated.

When an entire species is considered as an adaptive system, one more level of organization must be included: the evolution by natural selection—making a total of four adaptive levels, or a T4-system.

Artificial Systems

In contrast, artificial systems such as machine learning algorithms or neural networks are adaptive only at two levels or organizations (T2). According to practopoiesis, this lack of a deeper adaptive hierarchy of machines is the main limitation factor for their capability to achieve intelligence.

 

Linguistic derivation

The term Adaptive is derived from the Latin verb adaptāre, which is a combination of the prefix ad- meaning "to; at" + verb aptāre meaning "to fit".
The term System is derived from Latin systēma, which may originate from the Greek word sustēma (σύστημα), which is a combination of the prefix syn- meaning "with; together" + verb histanai meaning "to cause; to stand".

 

External Sources

http://en.wikipedia.org/wiki/Adaptive_system

Book:
José Antonio Martín H., Javier de Lope and Darío Maravall: "Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature" Natural Computing, December, 2009. Vol. 8(4), pp. 757-775.

 

Entry link: 
  Adaptive System

Autopoiesis

Autopoiesis

Explanation

Autopoiesis refers to a system that is capable of creating, maintaining and reproducing itself. Autopoietic mechanisms can operate as self-generating feedback systems.

Historical Frame

The term was introduced in 1972 by Chilean biologists Humberto Maturana and Francisco Varela to define the self-maintaining chemistry of living cells. Since then the concept has been also applied to the fields of systems theory and sociology.

Autopoiesis was originally presented as a system description that was said to define and explain the nature of living systems. A canonical example of an autopoietic system is the biological cell. The eukaryotic cell, for example, is made of various biochemical components such as nucleic acids and proteins, and is organized into bounded structures such as the cell nucleus, various organelles, a cell membrane and cytoskeleton. These structures, based on an external flow of molecules and energy, produce the components which, in turn, continue to maintain the organized bounded structure that gives rise to these components.

Autopoiesis: Components - Boundary - Processes

 

Related concepts

Allopoietic system

An autopoietic system is to be contrasted with an allopoietic system, such as a car factory, which uses raw materials (components) to generate a car (an organized structure) which is something other than itself (the factory). However, if the system is extended from the factory to include components in the factory's 'environment', such as supply chains, plant / equipment, workers, dealerships, customers, contracts, competitors, cars, spare parts and so on, then as a total viable system it could be considered to be autopoietic. Thus, an autopoietic system is a closed topological space that continuously generates and specifies its own organization. It maintains this through its operation as a system of production of its own components, and does this in an endless turnover of components. Autopoietic systems are thus distinguished from allopoietic systems, which have as the product of their functioning something different from themselves.

Practopoiesis

A theory of how autopoietic systems operate is named Practopoiesis (praxis + poiesis, meaning creation of actions). The theory presumes that, although the system as a whole is autopoietic, the components of that system may have allopoietic relations. For example, the genome combined with the operations of the gene expression mechanisms create proteins, but not the other way around; proteins do not create genomes. In that case poiesis occurs only in one direction. Practopoietic theory presumes such one-directional relationships of creation to take place also at other levels of system organisation.

Self-organizing Intelligence

Many scientists have often used the term autopoiesis as a synonym for self-organization. An autopoietic system is autonomous and operationally closed, in the sense that there are sufficient processes within it to maintain the whole. Autopoietic systems are "structurally coupled" with their medium, embedded in a dynamic of changes that can be recalled as sensory-motor coupling. This continuous dynamic is considered as a rudimentary form of knowledge or cognition and can be observed throughout life-forms. Autopoiesis would be the process of the emergence of necessary features out of chaotic contingency, causing contingency's gradual self-organisation, thus leading to the gradual rise of order out of chaos.

Linguistic derivation

The term Autopoiesis is derived from ancient Greek words auto- (αὐτo-) meaning "self", and poiesis (ποίησις), meaning "creation" or  "production". 

External sources

http://en.wikipedia.org/wiki/autopoiesis

Book: Maturana, H., & Varela, F. (1992). The tree of knowledge: The biological roots of human understanding. Boston: Shambhala.

 

Internal links

 

Entry link: 
  Autopoiesis

Bio-Mimicry

Bio-Mimicry

Biomimicry is also called Biomimetics.

Explanation

Biomimicry or biomimetics is the imitation of the models, systems, and elements of nature for the purpose of solving complex human problems.

Related concepts

Biological Mimicry

Historically, the term Mimicry originates from the domain of biologists, who described the following phenomenon: Some animal species, especially in the insect world, mimic the appearance of another kind of animal, to ward off predators. Other species obtained colours to blend away in their environment, thus to hide from predators. For example, some harmless flies have evolved over thousands of years to obtain the appearance of nasty wasps. Small birds do not dare to pick at a wasp, because of the possibility to get hurt by its nasty sting. The look-a-like wasp (but actually a fly) profits from this aversion and survives better than flies with a fly-like appearance. It then reproduces this genetic quality during evolution. This cyber-genetic mechanism secures the morphological adjustment in visual appearance in its future offspring.

Commercial Biomimicry

Nowadays, technological and commercial entrepreneurs are using this term to denote the following: Scientists investigate special qualities of certain plants or animals. Then, they try to imitate this quality or try to produce / improve it in laboratories as a biological technology. The next step is to industrialize it and release it as a commercial product to market, thus monetizing it through mass production.

Linguistic derivation

The term Biomimicry is derived from the ancient Greek words bios (βίος) meaning "life", and mimesis (μίμησις) meaning "to imitate".

External sources

http://en.wikipedia.org/wiki/Biomimetics

 

Entry link: 
  Bio-Mimicry

Cybernetics

Cybernetics

Explanation

Cybernetics is the scientific study of how people, animals, and machines control and communicate information (for example, via feedback loops). Control mechanisms according to cybernetic principles, are also found in genetic evolutionary processes, as well as in the emergence and development of ecosystems.

Cybernetics investigates and describes the regulation and control in animals (including humans), in organizations, and in machines when they are viewed as self-governing whole entities, consisting of parts and their dynamic organization.

Cybernetics views communication and control in all self-contained complex systems as analogous. It differs from the empirical sciences (physics, biology, etc.) in not being interested in material form but in organization, pattern, and communication in entities. Because of the increasing sophistication of computers and the efforts to make them behave in humanlike ways, cybernetics today is closely allied with artificial intelligence and robotics, and it draws heavily on ideas developed in information theory.

Law of Requisite Variety

The total amount of cybernetic knowledge deposited within a system is related to the total number of different states that the system can assume while interacting with the environment. This is referred to as the cybernetic variety of the system. The demands on variety are determined by Ashby’s Law of Requisite Variety (Ashby 1958; Beer 1974), which states:
"For a successful control of a system, the system that controls has to have at least as many states as the system being controlled."
Thus, being a good model of the environment entails a sufficient number of states, which is a pre-requirement to store a sufficient amount of cybernetic knowledge within the systems.
Generally speaking: Knowledge requires variety.

Good Regulator Theorem

Cybernetic knowledge is necessarily subjected to Conant & Ashby’s Good Regulator Theorem (Conant & Ashby 1970), stating:
“Any successful control mechanism must be a model of the system that it controls”.
That is, one can deal with the surrounding world successfully only if one already possesses certain knowledge about the effects that one’s actions are likely to exert on that world.
Maturana and Varela (1980, 1992) expressed it as: 
“All doing is knowing and all knowing is doing.”

Historical Frame

The concept of cybernetic was conceived by Norbert Wiener, who coined the term in 1948.

Linguistic derivation

The term Cybernetics is derived from the Ancient Greek words kybernetes meaning "pilot", "governor"; or from kybernan = "to steer", "to govern". 

External sources

http://en.wikipedia.org/wiki/cybernetics

 

Entry link: 
  Cybernetics


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