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

Domestication

Domestication

Explanation

Domestication is the process whereby a population of living organisms is changed at the genetic level, through generations of selective breeding, to accentuate traits that ultimately benefit the interests of humans. A usual by-product of domestication is the creation of a dependency in the domesticated organisms, so that they lose their ability to live in the wild. Through domestication a change in the phenotypical expression and in the genotype of the animal occurs over generations. A domesticated species is defined as "a plant- or animal-species in which the evolutionary process has been influenced by humans to meet the needs of mankind". Therefore, an important factor on domestication is Artificial Selection by humans.
Humans have brought these populations under their control and care for a wide range of reasons, such as: to produce food (such as wheat, beans, milk) or valuable commodities (such as wool, cotton, or silk); to do types of work (such as transportation, protection, warfare); to use for scientific research; to enjoy as companions or ornaments (e.g. from plants).

Historical Frame

Charles Darwin was the first to describe how domestication, selection and evolution are interlinked, and based on natural heritable variation among individual plants and animals. Today we know that such natural variation is caused by mutations in genes coding for these traits, and by new combinations of already existing genetic variation, based on earlier mutations. Darwin described how the process of domestication can involve both unconscious and methodical elements. Routine human interactions with animals and plants create selection pressures that cause adaptation to human presence, use or cultivation. Deliberate selective breeding has also been used to create desired changes, often after initial domestication. These two forces, unconscious natural selection and methodical selective breeding, may have both played roles in the processes of domestication throughout history. Both have been described from human perspective as processes of Artificial Selection. also called Extrinsic Eugenics.

Examples

The domestication of wheat

 

Wild wheat plants fall to the ground to re-seed themselves, when ripened. But domesticated wheat stays upright on the stem, for easier harvesting by man. For a wild wheat plant, this 'uprightness' may not be a clever way of dispersing its seed. There is evidence that this change was possible because of a random mutation that happened in the wild populations at the beginning of wheat cultivation. Wheat plants with this mutation (i.e. a long-lasting erect stem) were harvested more frequently by humans, and thus became the seed for the next crop. Therefore, without realizing, early farmers selected for this mutation, which may otherwise have died out. The result is domesticated wheat, which now relies on farmers for its own reproduction and dissemination.

The domestication of dogs

It is speculated that thousands of years ago, certain wolves which were tamer than the average wolf and less wary of humans, selected themselves as dogs over many generations. Most animals love their freedom or independence, and hunt for their own food. Some wolves may be sick or crippled in a fight, and have to find other ways to get their meal for the day. So they become opportunistic. These wolves were able to thrive by following humans to scavenge for food near camp fires and garbage dumps; this behaviour gave them an advantage over more shy individuals. Eventually a symbiotic relationship developed between people and these 'proto-dogs'. The dogs fed on human food scraps, and humans found that dogs could warn them of approaching dangers, such as large predators or other intruders. Some dogs could help with hunting, act as pets, provide warmth, or supplement the food supply of humans (!). As this relationship progressed, humans eventually began to keep these self-tamed wolves and breed from them the types of dogs that we have today.

Scientific research on artificial selection

In recent times, selective breeding may best explain how continuing processes of domestication often work. Some of the best-known evidence of the power of selective breeding comes from the Farm-Fox Experiment by Russian scientist, Dmitri K. Belyaev, in the 1950s. His team spent many years breeding the domesticated silver fox (Vulpes vulpes) and selecting only those individuals that showed the least fear of humans. Eventually, Belyaev's team selected only those that showed the most positive response to humans. He ended up with a population of grey-coloured foxes whose behaviour and appearance was significantly changed. They no longer showed any fear of humans and often wagged their tails and licked their human caretakers to show affection. Their behaviour was more 'childlike' as if they were mentally stuck in a youngster-phase, but with an adult body (This is called Pedomorphosis: the retention of juvenile characteristics in the adult body). These foxes had floppy ears, smaller skulls, rolled tails and other traits commonly found in dogs. Domesticated foxes had less pronounced stress hormones (cortisol, adrenalin) and higher serotonin levels. It took Belyaev's team some 10 to 30 generations of artificially selecting fox offspring, to wilfully 'steer' the evolution of behaviour in their desired direction!

Negative aspects

Selection of animals for visible "desirable" traits may make them unfit in other, unseen, ways. The consequences for the captive and domesticated animals were reduction in size, piebald colour, shorter faces with smaller and fewer teeth, diminished horns, weak muscle ridges, and less genetic variability. Poor joint definition, late fusion of the limb bone epiphyses with the diaphyses, hair changes, greater fat accumulation, smaller brains, simplified behaviour patterns, extended immaturity, and more pathology are a few of the defects of domestic animals. All of these changes have been documented in direct observations of the rat in the 19th century, by archaeological evidence, and confirmed by animal breeders in the 20th century.

Other negative aspects of domestication have been explored. For example: Man substitutes controlled breeding for natural selection; animals are selected for special traits like milk production of passivity [e.g. child-friendly Golden Retriever dog], at the expense of overall fitness and nature-wide relationships. Though domestication broadens the diversity of forms (that is: increases visible polymorphism, for example, the many kinds of sizes and colours dogs have today) it undermines the crisp demarcations that separate wild species. And it cripples our (i.e. modern citizens) recognition of the species as a group. Knowing only domestic animals dulls our understanding of the way in which unity and discontinuity occur as patterns in nature, and substitutes an attention to individuals and breeds. The wide variety of size, colour, shape, and form of domestic horses, for example, blurs the distinction among different species of Equus that once were constant and meaningfully adapted to natural surroundings.

Linguistic derivation

The term Domestication is derived from the Latin word domesticus meaning "of the home".

External sources

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

http://10e.devbio.com/article.php?ch=23&id=223 (Fox breeding).

 

Entry link: 
  Domestication

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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Epigenetics

Epigenetics

In biology, epigenetics is the study of cellular and physiological traits that are not caused by changes in the DNA sequence. Epigenetics describes the study of stable, long-term alterations in the transcriptional potential of a cell. Some of those alterations are heritable. Unlike simple genetics based on changes to the DNA sequence (the genotype), the changes in gene expression or cellular phenotype of epigenetics have other causes, thus use of the term epi-genetics.

Relation between Genotype and Phenotype with Epigentic target

The term also refers to the changes themselves: functionally relevant changes to the genome that do not involve a change in the nucleotide sequence. Examples of mechanisms that produce such changes are DNA methylation and histone modification, each of which alters how genes are expressed without altering the underlying DNA sequence. Gene expression can be controlled through the action of repressor proteins that attach to silencer regions of the DNA. These epigenetic changes may last through cell divisions for the duration of the cell's life, and may also last for multiple generations even though they do not involve changes in the underlying DNA sequence of the organism; instead, non-genetic factors cause the organism's genes to behave (or "express themselves") differently.

Epigenetic mechanisms

One example of an epigenetic change in eukaryotic biology is the process of cellular differentiation. During Morphogenesis, totipotent stem cells become the various pluripotent cell lines of the embryo, which in turn become fully differentiated cells. In other words, as a single fertilized egg cell – the zygote – continues to divide, the resulting daughter cells change into all the different cell types in an organism (including neurons, muscle cells, epithelium, endothelium of blood vessels, etc.) by activating some genes while inhibiting the expression of others.

Influence of Epigenetics on Metamorphosis of butterfly

Linguistic derivation

The term Epigenetics is derived from the ancient Greek prefix epi (επί-) meaning "over, outside of, around, on top of" and the word genetics from genesis (γένεσις) meaning "origin", "source", or "birth".

 

Entry link: 
  Epigenetics

Eugenics

Eugenics

Eugenics is also called Eugenetics.

Explanation

Eugenics is the theory and practice of improving the genetic quality of the human population. It is a social philosophy advocating the improvement of human genetic traits through the promotion of higher reproduction of people with desired traits (positive eugenics), and reduced reproduction of people with less-desired or undesired traits (negative eugenics).

 

CPER's standpoint

CPER propagates the fundamental human right of Positive Intrinsic Eugenics (PIE), which belief is based on the following conditions:

  • It is a basic democratic human right to choose yourself (at free will) with whom you want to procreate in a natural way, for whatever reason you find fit. No one may forbid you to marry the reproduction partner of your own choice, (when your are an adult)!
  • A heterosexual man or woman may seek a reproduction partner with whom he/she hopes to create children that may display an improved ratio/combination of both their desired parental traits (physical or mental).
  • People hope, by procreating with the partner of choice, to increase the chances that, in the random combination of genetic traits at fertilization, their offspring become better adapted towards surviving the circumstances of life. This way, parents strive for a better biological Quality of Life for their future offspring.

Some examples of possible improved outcome (as perceived by the procreating partners) of intrinsic positive 'breeding' are:
Improved physical qualities: tallness, strength, agility, speed, physical endurance, improved immune system, longevity, beauty. Also: (striving for) offspring with normal health by avoiding having children from a partner with an illness caused by certain genetic factors, for example: some forms of cancer.
Improved mental competences: higher IQ or EQ, such as: photographic memory, numeracy skills, verbal fluency, perfect musical hearing/pitch, caring love, social communication talent, emotional stability, mental hardiness, stress resistance, leadership qualities.

CPER's definition of PIE (within humans):
Positive Intrinsic Eugenics is: the tendency for genetic improvement within a species, by means of natural procreation, caused by the inner (human) drive/impulse to seek a particular reproduction partner to increase the chance of desirable qualities in their communal offspring.
PIE is therefore the inner drive to seek positive qualities from a potential reproduction partner, with the hope of creating children equipped with survival characteristics, that are desired by both parents.

Opposite formulation:
Tendency of people to strive for offspring with a normal healthy constitution by avoiding creating children with a partner who carries an illness caused by certain inherited genetic factors, for example: some forms of cancer, or enzyme deficiencies.

Future evolution of mankind:
CPER states that, because of the accelerating human overpopulation, the struggle for life will become harder and harder worldwide. Thus people will do anything to produce offspring that has a better chance of surviving harsh reality.

 

Linguistic derivation

The term Eugenics is derived from the ancient Greek words eu- (εὖ), meaning "good/well", and -genes (γένος), meaning "born" or "race".

 

External sources

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

 

Entry link: 
  Eugenics

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

Feedback

Feedback

Definitions

  1. Communication / education: (critical) assessment on information produced (verbal of lexical).
  2. Cybernetics: the signal that is looped back to control a system within itself.
    (see: Positive Feedback Loop or Negative Feedback Loop).
  3. Music / electronics: the high-pitched howling noise heard when there's a loop between a microphone and a speaker.

 

Explanation

Cause-and-effect

Feedback occurs when outputs of a system are "fed back" as inputs as part of a chain of cause-and-effect that forms a circuit or loop. The system can then be said to "feed back" into itself. The notion of 'cause-and-effect' has to be handled carefully when applied to feedback systems: Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument. This makes reasoning based upon cause and effect tricky, and it is necessary to analyze the system as a whole. In this context, the term "feedback" has also been used as an abbreviation for:

  • Feedback signal: the conveyance of information fed back from an output, or measurement, to an input, or effector, that affects the system.
  • Feedback loop: the closed path made up of the system itself and the path that transmits the feedback about the system from its origin (for example, a sensor) to its destination (for example, an actuator).

 

 

Simple Feedback Loop without sign


Figure: Simple feedback loop showing circular cause-effect relationship.

Positive - Negative

The terms positive and negative feedback are defined in different ways within different disciplines:

  1. The altering of the gap between reference and actual values of a parameter, based on whether the gap is widening (positive) or narrowing (negative) [in physics, cybernetics, biology].
  2. The valence of the action or effect that alters the gap, based on whether it has a happy (positive) or unhappy (negative) emotional connotation to the recipient or observer [in didactics, comunication training].

 

Fields of Application

Biology

In biological systems such as organisms, ecosystems, or the biosphere, most parameters must stay under control within a narrow range around a certain optimal level under certain environmental conditions.
The deviation of the optimal value of the controlled parameter can result from the changes in internal and external environments. A change of some of the environmental conditions may also require change of that range to change for the system to function. The value of the parameter to maintain is recorded by a reception system and conveyed to a regulation module via an information channel. An example of this is Insulin oscillations.

Biological systems contain many types of regulatory circuits, both positive and negative. As in other contexts, positive and negative do not imply that the feedback causes good or bad effects. A negative feedback loop is one that tends to slow down a process, whereas the positive feedback loop tends to accelerate it.

Feedback is also central to the operations of genes and gene regulatory networks. Repressor (see Lac repressor) and activator proteins are used to create genetic operons, which were identified by Francois Jacob and Jacques Monod in 1961 as feedback loops. These feedback loops may be positive (as in the case of the coupling between a sugar molecule and the proteins that import sugar into a bacterial cell), or negative (as is often the case in metabolic consumption).

On a larger scale, feedback can have a stabilizing effect on animal populations even when profoundly affected by external changes, although time lags in feedback response can give rise to predator-prey cycles.
In zymology, feedback serves as regulation of activity of an enzyme by its direct product(s) or downstream metabolite(s) in the metabolic pathway.
The hypothalamic–pituitary–adrenal axis is largely controlled by positive and negative feedback.

Psychology

In psychology, the body receives a stimulus from the environment or internally that causes the release of hormones. Release of hormones then may cause more of those hormones to be released, causing a positive feedback loop. This cycle is also found in certain behaviour. For example, "shame loops" occur in people who blush easily. When they realize that they are blushing, they become even more embarrassed, which leads to further blushing, and so on.

 

External Sources

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

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

 

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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

 

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  Genotype

Intelligent Organism

When can you speak of an 'intelligent organism'?
A life form must meet the following requirements in order to obtain the significant meaning 'intelligent':
The organism must be able to adapt to its environment (or adapt its environment to itself).
The organism must have a good working memory.
The organism must be able to learn from experiences and apply knowledge.
The organism must be able to understand complex ideas.
The organism must be able to reason (in a logical way).
The organism must be able to solve problems by thinking (not only by haphazardly doing something).
Humans meet these conditions, but also monkeys, dolphins and even crows can show reasonably intelligent behavior.

This is obviously an anthropocentric definition.

 

Entry link: 
  Intelligent Organism

Morphogenesis

Morphogenesis

Morphogenesis is the biological process that causes an organism to develop its shape. It is one of three fundamental aspects of developmental biology along with the control of cell growth and cellular differentiation. The process controls the organized spatial distribution of cells during the embryonic development of an organism. Morphogenesis can take place also in a mature organism, in cell culture or inside tumor cell masses.

Morphogenesis also describes the development of unicellular life forms that do not have an embryonic stage in their life cycle, or describes the evolution of a body structure within a taxonomic group. Morphogenetic responses may be induced in organisms by hormones, by environmental chemicals ranging from substances produced by other organisms to toxic chemicals or radionuclides released as pollutants, and other plants, or by mechanical stresses induced by spatial patterning of the cells.

Linguistic derivation

The term Morphogenesis is derived from the ancient Greek words morphê (μορφή) meaning "shape" or "form", and genesis (γένεσις) meaning "creation", "origin", "source", or "birth" thus literally: "beginning of the shape".

External sources

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

 

 

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  Morphogenesis

Negative Feedback Loop

Negative Feedback Loop

Explanation

Negative feedback occurs when some function of the output of a system, process, or mechanism is fed back in a manner that tends to reduce the fluctuations in the output, whether caused by changes in the input or by other disturbances. Whereas positive feedback tends to lead to instability via exponential growth or oscillation, negative feedback generally promotes stability.

Negative feedback tends to promote a settling to equilibrium, and reduces the effects of perturbations. Negative feedback loops in which just the right amount of correction is applied in the most timely manner can be very stable, accurate, and responsive.

In place of the adjective "Negative" before feedback loop, alternative terms are used, such as: degenerative, self-inhibiting, self-correcting, self-dampening, balancing, discrepancy-reducing, or centripetal.

Negative feedback is widely used in mechanical and electronic engineering, but it also occurs naturally within living organisms, and can be seen in many other fields from chemistry and economics to physical systems such as the climate. General negative feedback systems are studied in control systems engineering.

In organisms, feedback enables various measures (eg body temperature, or blood sugar level) to be maintained within precise desired ranges by homeostatic processes. In many physical and biological systems, qualitatively different influences can oppose each other. For example, in biochemistry, one set of chemicals drives the system in a given direction, whereas another set of chemicals drives it in an opposing direction. If one or both of these opposing influences are non-linear, equilibrium point(s) result.

Time graph of Negative feedback loop

Figure: Both starting points eventuate in equilibrium, during a negative feedback loop.

Cybernetics

Early researchers in the area of cybernetics subsequently generalized the idea of negative feedback to cover any goal-seeking or purposeful behavior. All purposeful behavior may be considered to require negative feed-back. If a goal is to be attained, some signals from the goal are necessary at some time to direct the behavior.

Cybernetics pioneer Norbert Wiener helped to formalize the concepts of feedback control, defining feedback in general as "the chain of the transmission and return of information". Wiener defind negative feedback as the case when: "The information fed back to the control center tends to oppose the departure of the controlled from the controlling quantity...".

Operant conditioning

Confusion arose after BF Skinner introduced the terms positive and negative reinforcement, both of which can be considered negative feedback mechanisms in the sense that they try to minimize deviations from the desired behavior.

In a similar context, Herold and Greller used the term "negative" to refer to the valence of the feedback: that is, cases where a subject receives an evaluation with an unpleasant emotional connotation.

Differerence in Terminology

In biology, this process (in general, biochemical) is often referred to as homeostasis; whereas in mechanics, the more common term is equilibrium.

In engineering, mathematics and the physical, and biological sciences, common terms for the points around which the system gravitates include: attractors, stable states, eigenstates/eigenfunctions, equilibrium points, and setpoints.

In control theory, negative refers to the sign of the multiplier in mathematical models for feedback. In delta notation, −Δoutput is added to or mixed into the input.

In multivariate systems, vectors help to illustrate how several influences can both partially complement and partially oppose each other.

Some authors, in particular with respect to modelling business systems, use negative to refer to the reduction in difference between the desired and actual behavior of a system.

In a psychology context, on the other hand, negative refers to the valence of the feedback – attractive versus aversive, or praise versus criticism.

Negative versus Positive

Negative feedback is feedback in which the system responds so as to decrease the magnitude of any particular perturbation, leading to dampening of the original signal, resulting in stabilization of the process.
In contrast, positive feedback is feedback in which the system responds so as to increase the magnitude of any particular perturbation, resulting in amplification of the original signal instead of stabilization (see: Positive Feedback Loop).

Any system in which there is positive feedback together with a gain greater than one will result in a runaway situation. Both positive and negative feedback require a feedback loop to operate.
However, some negative feedback systems can still be subject to oscillations. This is caused by the slight delays around any loop. Due to these delays the feedback signal of some frequencies can arrive one half cycle later which will have a similar effect to positive feedback and these frequencies can reinforce themselves and grow over time. This problem is often dealt with by attenuating or changing the phase of the problematic frequencies. Unless the system naturally has sufficient damping, many negative feedback systems have low pass filters or dampers fitted.

Fields of application

Control systems

Examples of the use of negative feedback to control its system are: thermostat control, the phase-locked loop oscillator, the ballcock control of water level, and temperature regulation in animals.

WC

The ballcock or float valve uses negative feedback to control the water level in a cistern of a toilette.

The ballcock control of water level

Figure: the ballcock control of water level via negative feedback.

Thermostat

A simple and practical example is a thermostat. When the temperature in a heated room reaches a certain upper limit, the room heating is switched off so that the temperature begins to fall. When the temperature drops to a lower limit, the heating is switched on again. Provided the limits are close to each other, a steady room temperature is maintained. Similar control mechanisms are used in cooling systems, such as an air conditioner, a refrigerator, or a freezer.

Biology and chemistry

Control of endocrine hormones by negative feedback. Some biological systems exhibit negative feedback such as the baroreflex in blood pressure regulation and erythropoiesis. Many biological process (e.g., in human physiology) use negative feedback. Examples of this are numerous, from the regulating of body temperature, to the regulating of blood glucose levels.

The disruption of feedback loops can lead to undesirable results: in the case of blood glucose levels, if negative feedback fails, the glucose levels in the blood may begin to rise dramatically, thus resulting in diabetes.

For hormone secretion regulated by the negative feedback loop: when gland X releases hormone X, this stimulates target cells to release hormone Y. When there is an excess of hormone Y, gland X "senses" this and inhibits its release of hormone X.
As shown in the figure below, most endocrine hormones are controlled by a physiologic negative feedback inhibition loop, such as the glucocorticoids secreted by the adrenal cortex. The hypothalamus secretes corticotropin-releasing hormone (CRH), which directs the anterior pituitary gland to secrete adrenocorticotropic hormone (ACTH). In turn, ACTH directs the adrenal cortex to secrete glucocorticoids, such as cortisol. Glucocorticoids not only perform their respective functions throughout the body but also negatively affect the release of further stimulating secretions of both the hypothalamus and the pituitary gland, effectively reducing the output of glucocorticoids once a sufficient amount has been released.

Pituitary Axis: negative feedback

Figure: Stress hormone Cortisol dampens its own creation indirectly.

Self-organization

Self-organization is the capability of certain systems "of organizing their own behavior or structure". There are many possible factors contributing to this capacity, and most often positive feedback is identified as a possible contributor. However, negative feedback also can play a role.

Economics

In economics, automatic stabilisers are government programs that are intended to work as negative feedback to dampen fluctuations in real GDP. Free market economic theorists claim that the pricing mechanism operated to match supply and demand. However Norbert Wiener wrote in 1948: "There is a belief current in many countries and elevated to the rank of an official article of faith in the United States that free competition is itself a homeostatic process... Unfortunately the evidence, such as it is, is against this simple-minded theory."

External sources

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

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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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Ontogenesis

Ontogenesis

Explanation

Ontogenesis is also called: Ontogeny.

Biological meaning

  1. The entire sequence of origin and development of an individual organism from embryo to adult.
  2. The development of an individual organism or of an anatomical or behavioural feature from the earliest stage to maturity (of the animal/plant concerned).
  3. The process of an individual organism growing organically; a purely biological unfolding of events involved in an organism changing gradually from a simple to a more complex level during a lifetime.

 

Psychological meaning

The process during which personality and sexual behavior of an individual person mature through a series of stages (Psychosexual development).

 

Related concepts

Compare with: Phylogenesis , Palingenesis .

Phylogeny relates to genetic development of a species during evolution, while Ontogeny relates to physiological (and psychological) development of an individual during his / her lifetime. Phylogenesis: long-term time scale. Ontogenesis: short-term time scale.

 

Linguistic derivation

The term Ontogenesis is derived from the ancient Greek words ontos (ὄντος), meaning "being", plus the term genesis (γένεσις) meaning "origin", "source", or "birth".
In Ontogeny, the suffix -geny also expresses the concept of "mode of production".

 

Entry link: 
  Ontogenesis

Palingenesis

Palingenesis

Explanation

Biological meaning

  1. The repetition by a single organism of various stages in the evolution of its species during embryonic development.
  2. The phase in the development of an organism in which its form and structure pass through the changes undergone in the evolution of the species.
  3. The emergence -during embryonic development of an individual life form- of various characters or structures that appeared during the evolutionary history of the strain or species.

 In biology, Palingenesis (or palingenesia) is also called Recapitulation.

CPER uses this biological meaning.

Theological meaning

  1. Ancient Greek: the continual re-creation of the universe by the Demiurgus (Creator) after its absorption into himself.
  2. Christianity: spiritual rebirth symbolized by baptism.
  3. In general: the concept of rebirth, reincarnation or re-creation.

 

Related concepts

Compare with: Ontogenesis , Phylogenesis .

Phylogeny relates to genetic development of a species during evolution, while Ontogeny relates to physiological (and psychological) development of an individual during his / her lifetime. Phylogenesis: long-term time scale. Ontogenesis:  short-term time scale.

Palingenesis assumes that a fetus undergoes a condensed reflection of genetic evolutional traits (cf. phylogeny), during the embryonic development of this organism (cf. ontogeny).

 

Linguistic derivation

The term Palingenesis is derived from the ancient Greek word palingenesia (παλιγγενεσία) that exists of the terms palin (πάλιν), meaning "again", plus the term genesis (γένεσις) meaning "origin", "source", or "birth".

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  Palingenesis

Phenology

Phenology

Phenology is the study of periodic plant and animal life cycle events and how these are influenced by seasonal and interannual variations in climate, as well as habitat factors (such as elevation). Phenology has been principally concerned with the dates of first occurrence of biological events in their annual cycle. Examples include the date of emergence of leaves and flowers, the first flight of butterflies and the first appearance of migratory birds, the date of leaf colouring and fall in deciduous trees, the dates of egg-laying of birds and amphibia, or the timing of the developmental cycles of temperate-zone honey bee colonies. In the scientific literature on ecology, the term is used more generally to indicate the time frame for any seasonal biological phenomena, including the dates of last appearance (e.g.: the seasonal phenology of a species may be from April through September).

Because many such phenomena are very sensitive to small variations in climate, especially to temperature, phenological records can be a useful proxy for temperature in historical climatology, especially in the study of climate change and global warming. For example, viticultural records of grape harvests in Europe have been used to reconstruct a record of summer growing season temperatures going back more than 500 years. In addition to providing a longer historical baseline than instrumental measurements, phenological observations provide high temporal resolution of on-going changes related to global warming.

 

Linguistic derivation

The term Phenology is derived from the Greek phainō (φαίνω), meaning "to show, to bring to light, make to appear" + logos (λόγος), which has many meanings, such as "word, study, discourse, reasoning".

 

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Phylogenesis

Phylogenesis 

Explanantion

Phylogenesis is also called: Phylogeny.

  1. The evolutionary development and diversification of a life-form or taxonomic group of organisms (e.g. species, populations), or of a particular feature of an organism.
  2. The evolutionary history of a group of organisms, especially as depicted in a family tree.

Related concepts

Compare with: Ontogenesis , Palingenesis .

Phylogeny relates to genetic development of a species during evolution, while Ontogeny relates to physiological (and psychological) development of an individual during his / her lifetime.
Phylogenesis: long-term time scale.
Ontogenesis: short-term time scale.

 

Linguistic derivation

The term Phylogenesis is derived from the ancient Greek words phylé (φυλή) and phulon (φῦλον), meaning "tribe", "clan", or "race", plus the term genesis (γένεσις) meaning "origin", "source", or "birth".  
In Phylogeny, t
he suffix -geny also expresses the concept of "mode of production".

Entry link: 
  Phylogenesis

Positive Feedback Loop

Positive Feedback Loop

Explanation

Positive feedback is a process that occurs in a feedback loop in which the effects of a small disturbance on a system include an increase in the magnitude of the perturbation. That is, mechanism A produces more of B which in turn produces more of A. In contrast, a system in which the results of a change act to reduce or counteract it, has negative feedback (see: Negative Feedback Loop).

Positive Feedback Loop

Figure: Positive feedback loop.

Mathematically, positive feedback is defined as a positive loop gain around a closed loop of cause and effect. That is, positive feedback is in phase with the input, in the sense that it adds to make the input larger. Positive feedback tends to cause system instability. When the loop gain is positive and above 1, there will typically be exponential growth, increasing oscillations or divergences from equilibrium. System parameters will typically accelerate towards extreme values, which may damage or destroy the system, or may end with the system latched into a new stable state. Positive feedback may be controlled by signals in the system being filtered, damped, or limited, or it can be cancelled or reduced by adding negative feedback.

Positive feedback is used in digital electronics to force voltages away from intermediate voltages into '0' and '1' states. On the other hand, thermal runaway is a positive feedback that can destroy semiconductor junctions. Positive feedback in chemical reactions can increase the rate of reactions, and in some cases can lead to explosions. Positive feedback in mechanical design causes tipping-point, or 'over-centre', mechanisms to snap into position, for example in switches and locking pliers. Out of control, it can cause bridges to collapse. Positive feedback in economic systems can cause boom-then-bust cycles. A familiar example of positive feedback is the loud squealing or howling sound produced by audio feedback in public address systems: the microphone picks up sound from its own loudspeakers, amplifies it, and sends it through the speakers again.

In place of the adjective "Positive", alternative terms are used, such as: regenerative, self-stimulating, self-reinforcing, self-amplifying, discrepancy-increasing, or centrifugal.

Time graphs of Positive feedback loop

Figure: Possible results of a Positive feedback loop.

Overview

Positive feedback enhances or amplifies an effect by it having an influence on the process which gave rise to it. For example, when part of an electronic output signal returns to the input, and is in phase with it, the system gain is increased. The feedback from the outcome to the originating process can be direct, or it can be via other state variables. Such systems can give rich qualitative behaviours, but whether the feedback is instantaneously positive or negative in sign has an extremely important influence on the results. Positive feedback reinforces and negative feedback moderates the original process. Positive and negative in this sense refer to loop gains greater than or less than zero, and do not imply any value judgements as to the desirability of the outcomes or effects. A key feature of positive feedback is thus that small disturbances get bigger. When a change occurs in a system, positive feedback causes further change, in the same direction.

If the loop gain AB is positive, then a condition of positive or regenerative feedback exists. Thus depending on the feedback, state changes can be convergent, or divergent. The result of positive feedback is to augment changes, so that small perturbations may result in big changes.

In the real world, positive feedback loops typically do not cause ever-increasing growth, but are modified by limiting effects of some sort. According to Donella Meadows: "Positive feedback loops are sources of growth, explosion, erosion, and collapse in systems. A system with an unchecked positive loop ultimately will destroy itself. That’s why there are so few of them. Usually a negative loop will kick in sooner or later."

 

Fields of application

 

Biology

Positive feedback is a mechanism by which an output is enhanced, such as protein levels. However, in order to avoid any fluctuation in the protein level, the mechanism is inhibited stochastically (I). Therefore, when the concentration of the activated protein (A) is past the threshold ([I]), the loop mechanism is activated and the concentration of A increases exponentially if d[A]=k [A] .

Physiology

A number of examples of positive feedback systems may be found in physiology. One example is the onset of contractions in childbirth, known as the Ferguson reflex. When a contraction occurs, the hormone oxytocin causes a nerve stimulus, which stimulates the hypothalamus to produce more oxytocin, which increases uterine contractions. This results in contractions increasing in amplitude and frequency.

Another example is the process of blood clotting. The loop is initiated when injured tissue releases signal chemicals that activate platelets in the blood. An activated platelet releases chemicals to activate more platelets, causing a rapid cascade and the formation of a blood clot.

Lactation also involves positive feedback in that as the baby suckles on the nipple there is a nerve response into the spinal cord and up into the hypothalamus of the brain. The hypothalamus then stimulates the pituitary gland to produce more prolactin to produce more milk.

A spike in estrogen during the follicular phase of the menstrual cycle causes ovulation.

The generation of nerve signals is another example, in which the membrane of a nerve fibre causes slight leakage of sodium ions through sodium channels. This results in a change in the membrane potential, which in turn causes more opening of channels, and so on. So a slight initial leakage results in an explosion of sodium leakage which creates the nerve action potential.

In excitation–contraction coupling of the heart, an increase in intracellular calcium ions to the cardiac myocyte is detected by ryanodine receptors in the membrane of the sarcoplasmic reticulum. These ryanodine receptors transport calcium out into the cytosol in a positive feedback physiological response.

In most cases, such feedback loops culminate in counter-signals being released that suppress or breaks the loop. Childbirth contractions stop when the baby is out of the mother's body. Chemicals break down the blood clot. Lactation stops when the baby no longer nurses.

Gene regulation

Positive feedback is a well studied phenomenon in gene regulation, where it is most often associated with bistability. Positive feedback occurs when a gene activates itself directly or indirectly via a double negative feedback loop. Genetic engineers have constructed and tested simple positive feedback networks in bacteria to demonstrate the concept of bistability. A classic example of positive feedback is the lac operon in Escherichia coli. Positive feedback plays an integral role in cellular differentiation, development, and cancer progression, and therefore, positive feedback in gene regulation can have significant physiological consequences. Random motions in molecular dynamics coupled with positive feedback can trigger interesting effects, such as create population of phenotypically different cells from the same parent cell. This happens because noise can become amplified by positive feedback. Positive feedback can also occur in other forms of cell signaling, such as enzyme kinetics or metabolic pathways.

Evolutionary biology

Positive feedback loops have been used to describe aspects of the dynamics of change in biological evolution. For example, beginning at the macro level, Alfred J. Lotka (1945) argued that the evolution of the species was most essentially a matter of selection that fed back energy flows to capture more and more energy for use by living systems. At the human level, Richard Alexander (1989) proposed that social competition between and within human groups fed back to the selection of intelligence thus constantly producing more and more refined human intelligence. Crespi (2004) discussed several other examples of positive feedback loops in evolution. The analogy of Evolutionary arms races provide further examples of positive feedback in biological systems.

During the Phanerozoic the biodiversity shows a steady but not monotonic increase from near zero to several thousands of genera. It has been shown that changes in biodiversity through the Phanerozoic correlate much better with hyperbolic model (widely used in demography and macrosociology) than with exponential and logistic models (traditionally used in population biology and extensively applied to fossil biodiversity as well). The latter models imply that changes in diversity are guided by a first-order positive feedback (more ancestors, more descendants) and/or a negative feedback arising from resource limitation. Hyperbolic model implies a second-order positive feedback. The hyperbolic pattern of the world population growth has been demonstrated (see below) to arise from a second-order positive feedback between the population size and the rate of technological growth. The hyperbolic character of biodiversity growth can be similarly accounted for by a positive feedback between the diversity and community structure complexity. It has been suggested that the similarity between the curves of biodiversity and human population probably comes from the fact that both are derived from the interference of the hyperbolic trend (produced by the positive feedback) with cyclical and stochastic dynamics.

Immune system

A cytokine storm, or hypercytokinemia is a potentially fatal immune reaction consisting of a positive feedback loop between cytokines and immune cells, with highly elevated levels of various cytokines. In normal immune function, positive feedback loops can be utilized to enhance the action of B-lymphocytes. When a B-cell binds its antibodies to an antigen and becomes activated, it begins releasing antibodies and secreting a complement protein called C3. Both C3 and a B-cell's antibodies can bind to a pathogen, and when a B-cell has its antibodies bind to a pathogen with C3, it speeds up that B-cell's secretion of more antibodies and more C3, thus creating a positive feedback loop.

Psychology

In psychology, the body receives a stimulus from the environment or internally that causes the release of hormones. Release of hormones then may cause more of those hormones to be released, causing a positive feedback loop. This cycle is also found in certain behaviour. For example, "shame loops" occur in people who blush easily. When they realize that they are blushing, they become even more embarrassed, which leads to further blushing, and so on.

Winner (1996) described gifted children as driven by positive feedback loops involving setting their own learning course, this feeding back satisfaction, thus further setting their learning goals to higher levels and so on. Winner termed this positive feedback loop as a "rage to master."

Vandervert (2009) proposed that the child prodigy can be explained in terms of a positive feedback loop between the output of thinking/performing in working memory, which then is fed to the cerebellum where it is streamlined, and then fed back to working memory thus steadily increasing the quantitative and qualitative output of working memory. Vandervert also argued that this working memory/cerebellar positive feedback loop was responsible for language evolution in working memory.

In substance dependence a human seeks the effects of a drug, and the drug supplies an effect. The human thereafter continues to seek the effects from the drug. In time the body acclimates to the dosage of the drug, and finds a new homeostasis. The human then must consume a larger quantity of the drug to feel the effects the subject wants, a drug overdose may occur when seeking this new threshold of drug effect. If an accidental overdose doesn't kill the human, eventually the human body can no longer repair itself from the damage (ex. Kidney failure and Liver failure) and death is the final result to this positive feedback.

Economics

Market dynamics

According to the theory of reflexivity advanced by George Soros, price changes are driven by a positive feedback process whereby investors' expectations are influenced by price movements so their behaviour acts to reinforce movement in that direction until it becomes unsustainable, whereupon the feedback drives prices in the opposite direction.

Systemic risk

Systemic risk is the risk that an amplification or leverage or positive feedback process is built into a system. This is usually unknown, and under certain conditions this process can amplify exponentially and rapidly lead to destructive or chaotic behavior. A Ponzi scheme is a good example of a positive-feedback system: funds from new investors are used to pay out unusually high returns, which in turn attract more new investors, causing rapid growth toward collapse. W. Brian Arthur has also studied and written on positive feedback in the economy (e.g. W. Brian Arthur, 1990). Hyman Minsky proposed a theory that certain credit expansion practices could make a market economy into "a deviation amplifying system" that could suddenly collapse, sometimes called a "Minsky moment". Simple systems that clearly separate the inputs from the outputs are not prone to systemic risk. This risk is more likely as the complexity of the system increases, because it becomes more difficult to see or analyze all the possible combinations of variables in the system even under careful stress testing conditions. The more efficient a complex system is, the more likely it is to be prone to systemic risks, because it takes only a small amount of deviation to disrupt the system. Therefore well-designed complex systems generally have built-in features to avoid this condition, such as a small amount of friction, or resistance, or inertia, or time delay to decouple the outputs from the inputs within the system. These factors amount to an inefficiency, but they are necessary to avoid instabilities. The 2010 Flash Crash incident was blamed on the practice of high-frequency trading (HFT), although whether HFT really increases systemic risk remains controversial.

Human population growth

Agriculture and human population can be considered to be in a positive feedback mode, which means that one drives the other with increasing intensity. It is suggested that this positive feedback system will end sometime with a catastrophe, as modern agriculture is using up all of the easily available phosphate and is resorting to highly efficient monocultures which are more susceptible to systemic risk. Technological innovation and human population can be similarly considered, and this has been offered as an explanation for the apparent hyperbolic growth of the human population in the past, instead of a simpler exponential growth. It is proposed that the growth rate is accelerating because of second-order positive feedback between population and technology. Technological growth increases the carrying capacity of land for people, which leads to more population, and so more potential inventors in further technological growth.

Prejudice, social institutions and poverty Gunnar Myrdal described a vicious circle of increasing inequalities, and poverty, which is known as "circular cumulative causation".

In meteorology

Drought intensifies through positive feedback. A lack of rain decreases soil moisture, which kills plants and/or causes them to release less water through transpiration. Both factors limit evapotranspiration, the process by which water vapor is added to the atmosphere from the surface, and add dry dust to the atmosphere, which absorbs water. Less water vapor means both low dew point temperatures and more efficient daytime heating, decreasing the chances of humidity in the atmosphere leading to cloud formation. Lastly, without clouds, there cannot be rain, and the loop is complete.

In climatology

The climate system is characterized by strong positive and negative feedback loops between processes that affect the state of the atmosphere, ocean, and land.
Climate "forcings" may push a climate system in the direction of warming or cooling, for example, increased atmospheric concentrations of greenhouse gases cause warming at the surface. Forcings are external to the climate system and feedbacks are internal processes of the system. Some feedback mechanisms act in relative isolation to the rest of the climate system while others are tightly coupled. Forcings, feedbacks and the dynamics of the climate system determine how much and how fast the climate changes. The main positive feedback in global warming is the tendency of warming to increase the amount of water vapour in the atmosphere, which in turn leads to further warming. The main negative feedback comes from the Stefan–Boltzmann law, the amount of heat radiated from the Earth into space is proportional to the fourth power of the temperature of Earth's surface and atmosphere.
Other examples of positive feedback subsystems in climatology include:
A warmer atmosphere will melt ice and this changes the albedo which further warms the atmosphere. This is called the ice-albedo positive feedback loop whereby melting snow exposes more dark ground (of lower albedo), which in turn absorbs heat and causes more snow to melt.
Methane hydrates can be unstable so that a warming ocean could release more methane, which is also a greenhouse gas.

The Intergovernmental Panel on Climate Change

IPCC's Fourth Assessment Report states that "Anthropogenic warming could lead to some effects that are abrupt or irreversible, depending upon the rate and magnitude of the climate change."

In sociology

A self-fulfilling prophecy is a social positive feedback loop between beliefs and behaviour: if enough people believe that something is true, their behaviour can make it true, and observations of their behaviour may in turn increase belief. A classic example is a bank run.

Another sociological example of positive feedback is the network effect. When more people are encouraged to join a network this increases the reach of the network therefore the network expands ever more quickly. A viral video is an example of the network effect in which links to a popular video are shared and redistributed, ensuring that more people see the video and then re-publish the links. This is the basis for many social phenomena, including Ponzi schemes and chain letters. In many cases population size is the limiting factor to the feedback effect.

On the Internet

Internet recommendation systems are expected to increase the diversity of what we see and do online. They help us discover new content and websites among myriad choices. Some recommendation systems, however, unintentionally do the opposite. Because some recommendation systems (i.e. certain collaborative filters) recommend products based on past sales or ratings, they cannot usually recommend products with limited historical data. This can create positive feedback: a rich-get-richer effect for popular products. This bias toward popularity can prevent what are otherwise better recommendations for that user's preferences. A Wharton study details this phenomenon along with several ideas that may promote diversity.

Chemistry

If a chemical reaction causes the release of heat, and the reaction itself happens faster at higher temperatures, then there is a high likelihood of positive feedback. If the heat produced is not removed from the reactants fast enough, thermal runaway can occur and very quickly lead to a chemical explosion.

Similar terminology

  1. Vicious/virtuous circle: in social and financial systems, a complex of events that reinforces itself through a feedback loop.
  2. Positive reinforcement: a situation in operant conditioning where a consequence increases the frequency of a behaviour (B.F. Skinner).
  3. Praise of performance: a term often applied in the context of performance appraisal, although this usage is disputed.
  4. Self-reinforcing feedback: a term used in systems dynamics to avoid confusion with the "praise" usage.

External sources

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

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Practopoiesis

Practopoiesis

Explanation

Practopoiesis is an overarching biological theory from Danko Nikolic. The term reflects the fundamental presumption on what the common property is, that can be found across all the different levels of organization of biological systems: to Act.
For example: Gene expression mechanisms act; bacteria act; organs act; organisms as a whole act.

 

Cybernetics

 

Due to this focus on biological action, practopoiesis has a strong cybernetic flavour as it has to deal with the need of acting systems to close feedback loops.

Thus, the theory of practopoiesis builds on the concepts of cybernetics. Cybernetic laws assumes monitor-and-act machinery (either physical or biological):
input → processing → output → feedback (as new input).
In practopoiesis, input is needed to trigger actions and to determine whether more actions are needed. For that reason, the theory is founded in the basic theorems of cybernetics, namely that of Requisite Variety and of Good Regulator Theorem.

The key novelty of practopoiesis is that it introduces the mechanisms explaining how different levels of organization mutually interact. These mechanisms help explain how genes create anatomy of the nervous system, or how anatomy creates behaviour.

Intelligence 

Practopoiesis is a theory on how life organizes into a mind. It proposes the principles by which adaptive systems organize. It is a general theory of what it takes to be biologically intelligent. Being general, the theory is applicable to the brain as much as it is applicable to artificial intelligence (AI) technologies. What makes the theory so general is that it is grounded in the principles of cybernetics (e.g. feedback loops), rather than describing the physiological implementations of those mechanisms (e.g. inhibition/excitation, plasticity, etc.).

In practopoiesis there is no longer a cycle: action → representation → action … . Instead, practopoietic theory works with actions only, which interact and form a hierarchy: One action is in a service of another action. This hierarchy starts with actions of gene expression mechanisms and ends with our overt behavior. Perception and cognition are then understood as emergent properties of those cybernetics-like actions.

 

The practopoietic theory of Prof. Dr. Danko Nikolic consists of two parts:

  1. The first part is the foundation. This is where the basic principles of adaptive systems are formulated. These principles can be applied to various biological processes, not only to the brain. Also, the first part can be applied to non-biological systems, such as AI.
  2. The second part applies those principles to human mind and to the mind/body problem. The second part explains the ways in which the mind is special and different from any other adaptive system.

 

Linguistic derivation

The term Practopoiesis is derived from Ancient Greek words praxis  (πρᾶξις)) meaning "deed", "act" or "action" + poiesis (ποίησις, ποιέω) which means "to make". Practopoiesis means: creation of actions.

External Sources

http://www.danko-nikolic.com/practopoiesis/

https://www.singularityweblog.com/practopoiesis/

 

Entry link: 
  Practopoiesis

PseudoCode

Pseudocode

PseudoCode is a 'language syntax shorthand notation' that uses expressions in between human language and computer programming language. A programmer tries to write down cause-effect actions, using normal human words, so that a laymen (with no programming experience) can read and understand the serie of commands (= algorithms) that execute a specific computer process.

CPER paradigm:
In the category 'Bio-Mental Cybernetics', the term (Bio-)PseudoCode is used to denote the following: To describe in plain language (shorthand notation), mental algorithms based on neuronal mechanisms that form feelings or thoughts, especially in human beings.

Bio-Mental Cybernetics describes the underlying mechanism of certain cause-effect phenomena that emerged during evolution to give rise to brain processes with regard to emotions (feelings) and cognitions (thought).

Mental-pseudocode (also called: neuro-pseudocode) denotes, on the concrete level, the actual thought process as a result of functional neuronal process.

Evo-pseudocode (also called: meta-pseudocode) describes, on a meta level, the way evolution gave birth to processes by which animals obtained the ability to generate brain processes resembling human thought.

 

Entry link: 
  PseudoCode

Semi-Science

Semi-Science

Explanation

Semi-Science is an attribute given by scientists to questionable knowledge-findings of certain people or non-objective truth-claims of certain groups. This attribute indicates that the underlying facts are not (yet) properly proven according to scientific rules, or that they are based on half-truths.

CPER's principle:
CPER uses this term to denote the fact that science has the following limitations:

  • There are many phenomena that science cannot explain properly because of limited technical means.
  • Scientists have not investigated everything there is to investigate yet.
  • Some scientists do not do value-free research in order to find the objective truth.
  • Scientists avoid to investigate certain tricky phenomena, for example: many scientists refuse to do objective research on the aspect of homosexuality, because of moral or political reasons.
  • There is little integration of knowledge from different scientific areas related to cross-domain patterns or phenomena.
  • Universities focus more on in-depth research, rather than on expository education.
  • Universities are limited in their financial resources, so they choose to invest in research on popular or urgent themes.
  • To become famous or to get subsidies, current scientists investigate mainly unexplored new areas. This way, much relatively 'old' knowledge (but still useful for people at home) is not perpetuated in a global popular knowledge base. Useful scientific findings or solutions disappear into the background of daily concerns of society. (Four family generations later, a scientist re-invents the wheel again.)

This means that certain theorems can not be refuted, and should not be ignored by the scientific community, as long as science does not contain 'all answers to everything'.

CPER's vocabulary:
CPER applies the term 'semi-science' to emphasize those statements that may become scientifically proven in the future, by research done under supervision of a university. Thus, these semi-scientific statements, formulations, explanations or descriptions of phenomena are -for now- 'hypotheses': they are set candidacy for proper academic examination, and -who knows- may get included into the accepted repertoire of proven scientific knowledge, some day.

Linguistic derivation

The prefix Semi is derived from the Latin word semi, meaning "half" and the term Science from Latin scientia, meaning "knowledge".

 

Entry link: 
  Semi-Science

Taxonomy

Taxonomy

Taxonomy is the science of defining groups of biological organisms on the basis of shared characteristics and giving names to those groups. Organisms are grouped together into taxa (singular: taxon) and given a taxonomic rank. Groups of a given rank can be aggregated to form a super group of higher rank and thus create a taxonomic hierarchy. The Swedish botanist Carolus Linnaeus is regarded as the father of taxonomy, as he developed a system known as Linnaean classification for categorization of organisms and binomial nomenclature for naming organisms. With the advent of such fields of study as phylogenetics, cladistics, and systematics, the Linnaean system has progressed to a system of modern biological classification based on the evolutionary relationships between organisms, both living and extinct

The term taxonomy is derived from the Ancient Greek word taxis (τάξις) meaning "arrangement" and -nomia (νομία) meaning "method".

Taxon

In biology, a taxon (plural taxa; back-formation from taxonomy) is a group of one or more populations of an organism or organisms seen by taxonomists to form a unit. A taxon is usually known by a particular name and given a particular ranking.

Taxonomic rank

In biological classification, rank is the level (the relative position) in a taxonomic hierarchy. Examples of taxonomic ranks are species, genus, family, and class.
Each rank subsumes under it a number of less general categories.
The rank of species, and specification of the genus to which the species belongs is basic, which means that it may not be necessary to specify ranks other than these.
The International Code of Zoological Nomenclature defines rank as:
The level, for nomenclatural purposes, of a taxon in a taxonomic hierarchy (e.g. all families are for nomenclatural purposes at the same rank, which lies between superfamily and subfamily).

 

Taxonomic rank


Figure: Example of division terms used in Taxonomic rank.

 

Related concepts

Cladistics

Cladistics is an approach to biological classification in which organisms are grouped together. This grouping is based on whether or not organisms have one or more shared unique characteristics that come from the group's last common ancestor and are not present in more distant ancestors. Therefore, members of the same group are thought to share a common history and are considered to be more closely related. When these lineage-branching (with regard to common ancestor) are drawn in a diagram, this is called a cladogram.

The term cladistic is derived from the Ancient Greek word klados (κλάδος) meaning "branch".

 

Phylogenetic nomenclature

Phylogenetic nomenclature, often called cladistic nomenclature, is a method of nomenclature for taxa in biology that uses phylogenetic definitions for taxon names. This contrasts with the traditional approach, in which taxon names are defined by a type, which can be a specimen or a taxon of lower rank, and a diagnosis, a statement intended to supply characters that differentiate the taxon from others with which it is likely to be confused. Phylogenetic nomenclature is currently not regulated, but the International Code of Phylogenetic Nomenclature (PhyloCode) is intended to regulate it once it is ratified.

 

External sources

http://en.wikipedia.org/wiki/Taxonomy_(biology)

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

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

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

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

 

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Entry link: 
  Taxonomy

Vector

Vector

Explanation

CPER: a vector is an abstract reference pointer, that indicates the direction of an evolutionary trend. Within the framework of the theory of CPER, a vector resembles the definition of a mathematical pointer or physical impulse, rather than of a biological carrier.


Mathematics: a vector is a geometric pointer-entity endowed with magnitude and direction as well as a positive-definite inner product. It is an element of an Euclidean vector space that is used to represent physical quantities that have both magnitude and direction (X, Y, Z), such as force, in contrast to scalar quantities, which have no direction. An inner product space is a vector space with an additional structure called an inner product. This additional structure associates each pair of vectors in the space with a scalar quantity known as the inner product of the vectors. Inner products allow the rigorous introduction of intuitive geometrical notions such as the length of a vector or the angle between two vectors. An inner product naturally induces an associated norm, thus an inner product space is also a normed vector space.


Epidemiology: a vector is a carrier-organism, often an invertebrate arthropod, that transmits a pathogen from reservoir to host.


Molecular biology: a vector is a carrier-vehicle used to transfer genetic material to a target cell, such as a plasmid vector.

 

Linguistic derivation

 The word Vector is derived from the Latin term vehere, which means "to carry".

 

Entry link: 
  Vector


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