Feedback loops are a key feature of the systems this article focuses on, such as ecosystems and individual organisms. They also exist in the human world, communities, organizations and families.
Artificial systems of this kind include robots with control systems that use negative feedback to maintain desired states.
Key Features
In an adaptive system, the parameter changes slowly and does not have a preferred value. However, in a self-regulating system, the value of the parameter depends on the history of the system's dynamics. One of the most important qualities of self-regulating systems is the ability to adapt to the edge of chaos, or the ability to avoid chaos. Practically speaking, by heading towards the edge of chaos without going further, the observer can act spontaneously, but without catastrophes. Physicists have proven that adaptation to the edge of chaos occurs in almost all feedback systems. Let the reader not be surprised by pretentious terminology, because such theories directly affect the theorychaos.
Practopoesis
Practopoiesis as a term coined by Danko Nikolic is a reference to a kind of adaptive or self-regulating system in which the autopoiesis of an organism or cell occurs through allopoetic interactions between its components. They are organized in a poetic hierarchy: one component creates another. The theory suggests that living systems exhibit a hierarchy of four such poetic operations:
evolution (i) → gene expression (ii) → non-gene-related homeostatic mechanisms (anapoiesis) (iii) → cell function (iv).
Practopoesis challenges modern neuroscience doctrine by arguing that mental operations mostly occur at the anapoetic level (iii), that is, that minds emerge from fast homeostatic (adaptive) mechanisms. This contrasts with the widely held belief that thinking is synonymous with neural activity (cell function at level iv).
Each lower level contains knowledge that is more general than the higher level. For example, genes contain more general knowledge than anapoetic mechanisms, which in turn contain more general knowledge than cell functions. This hierarchy of knowledge allows the anapoetic level to directly store the concepts necessary for the appearance of the mind.
Complex system
A complex adaptive system is a complex mechanism in which a perfect understanding of individual parts does not automatically provide a perfect understanding of the wholedesigns. The study of these mechanisms, which are a kind of subset of non-linear dynamical systems, is highly interdisciplinary and combines the knowledge of natural and social sciences to develop models and representations of the highest level that take into account heterogeneous factors, phase transition and other nuances.
They are complex in that they are dynamic networks of interactions, and their relationships are not collections of separate static objects, that is, the behavior of the ensemble is not predicted by the behavior of the components. They are adaptive in that individual and collective behaviors mutate and self-organize according to a change-initiating micro-event or set of events. They are a complex macroscopic collection of relatively similar and partially related microstructures, shaped to adapt to a changing environment and enhance their survival as a macrostructure.
Application
The term "complex adaptive systems" (CAS) or the science of complexity is often used to describe the loosely organized academic field that has grown up around the study of such systems. Complexity science is not a single theory - it covers more than one theoretical framework and is highly interdisciplinary, seeking answers to some fundamental questions about living, adaptable, changing systems. CAS research focuses on the complex, emergent, and macroscopic properties of a system. John H. Holland said that CAS are systems that have a largethe number of components, often referred to as agents, that interact, adapt, or learn.
Examples
Typical examples of adaptive systems include:
- climate;
- cities;
- firms;
- markets;
- governments;
- industry;
- ecosystems;
- social networks;
- electric networks;
- packs of animals;
- traffic flows;
- social insect colonies (e.g. ants);
- brain and immune system;
- cells and developing embryo.
But that's not all. Also, this list can include adaptive systems in cybernetics, which are gaining more and more popularity. Organizations based on social groups of people such as political parties, communities, geopolitical communities, wars and terrorist networks are also considered CAS. The Internet and cyberspace, composed, collaborating and managed by a complex set of human-computer interactions, are also seen as a complex adaptive system. CAS can be hierarchical, but it will always show aspects of self-organization more often. Thus, some modern technologies (for example, neural networks) can be called self-learning and self-adjusting information systems.
Differences
What distinguishes CAS from a pure multi-agent system (MAS) is the attention to top-level features and functions such as self-similarity, structural complexity, and self-organization. MAS is definedas a system consisting of several interacting agents, while in CAS the agents and the system are adaptive, and the system itself is self-similar.
CAS is a complex collection of interacting adaptive agents. Such systems are characterized by a high degree of adaptation, which makes them unusually resilient in the face of change, crises and catastrophes. This should be taken into account when developing an adaptive system.
Other important properties are: adaptation (or homeostasis), communication, cooperation, specialization, spatial and temporal organization and reproduction. They can be found at all levels: cells specialize, adapt, and multiply just like larger organisms do. Communication and collaboration occurs at all levels, from the agent to the system level. The forces driving cooperation between agents in such a system can in some cases be analyzed using game theory.
Simulation
CAS are adaptive systems. Sometimes they are modeled using agent-based and complex network models. Those based on agents are developed using various methods and tools, primarily by first identifying various agents within the model. Another method for developing models for CAS involves developing complex network models by using the interaction data of various CAS components, such as an adaptive communication system.
In 2013SpringerOpen / BioMed Central has launched an open access online journal on complex systems modeling (CASM).
Living organisms are complex adaptive systems. While complexity is difficult to quantify in biology, evolution has produced some amazing organisms. This observation has led the common misconception about evolution to be progressive.
Striving for complexity
If the above were generally true, evolution would have a strong tendency towards complexity. In this type of process, the value of the most common degree of difficulty will increase over time. Indeed, some artificial life simulations suggest that CAS generation is an inevitable feature of evolution.
However, the idea of a general trend towards complexity in evolution can also be explained by a passive process. This includes increasing the variance, but the most common value, mode, does not change. Thus, the maximum difficulty level increases over time, but only as an indirect product of the total number of organisms. This type of random process is also called a bounded random walk.
In this hypothesis, the obvious tendency to complicate the structure of organisms is an illusion. It arises from concentrating on a small number of large, highly complex organisms that inhabit the right tail of the complexity distribution, and ignoring the simpler and much more commonorganisms. This passive model emphasizes that the vast majority of species are microscopic prokaryotes, which make up about half of the world's biomass and the vast majority of Earth's biodiversity. Therefore, simple life remains dominant on Earth, while complex life appears more diverse only because of sampling bias.
If biology lacks a general tendency towards complexity, this will not prevent the existence of forces that drive systems towards complexity in a subset of cases. These minor trends will be counterbalanced by other evolutionary pressures that drive systems towards less complex states.
Immune system
The adaptive immune system (also known as the acquired or, more rarely, specific immune system) is a subsystem of the general immune system. It consists of highly specialized cells and processes that eliminate pathogens or prevent their growth. The acquired immune system is one of the two major immune strategies in vertebrates (the other being the innate immune system). Acquired immunity creates an immunological memory after an initial response to a particular pathogen and leads to an enhanced response to subsequent encounters with the same pathogen. This process of acquired immunity is the basis of vaccination. Like the innate system, the acquired system includes not only components of humoral immunity, but also components of cellular immunity.
History of the term
The term "adaptive" was first introducedused by Robert Good in relation to antibody responses in frogs as a synonym for acquired immune response in 1964. Goode acknowledged that he used the terms interchangeably, but explained only that he preferred to use the term. Perhaps he was thinking about the then implausible theory of antibody formation, in which they were plastic and could adapt to the molecular shape of antigens, or the concept of adaptive enzymes whose expression could be caused by their substrates. The phrase was used almost exclusively by Goode and his students, and by several other immunologists working on marginal organisms until the 1990s. Then it became widely used in conjunction with the term "innate immunity", which became a popular subject after the discovery of the Toll receptor system. in Drosophila, previously a marginal organism for the study of immunology. The term "adaptive" as used in immunology is problematic because acquired immune responses can be either adaptive or maladaptive in a physiological sense. Indeed, both acquired and immune responses can be adaptive and non-adaptive in an evolutionary sense. Most textbooks today use the term "adaptive" exclusively, noting that it is synonymous with "acquired".
Biological adaptation
Since the discovery, the classical meaning of acquired immunity has come to mean antigen-specific immunity mediated by rearrangements of somaticgenes that create antigen receptors that define clones. In the last decade, the term "adaptive" has been increasingly applied to another class of immune response that has not yet been associated with somatic gene rearrangements. These include the expansion of natural killer (NK) cells with as yet unexplained antigen specificity, the expansion of NK cells expressing germline-encoded receptors, and the activation of other innate immune cells into an activated state that provides short-term immune memory. In this sense, adaptive immunity is closer to the concept of "activated state" or "heterostasis", thus returning to the physiological meaning of "adaptation" to environmental changes. Simply put, today it is almost synonymous with biological adaptation.
