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Artificial life

It (commonly Alife or alife) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution through simulations using computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American computer scientist, in 1986. There are three main kinds of alife, named for their approaches: soft from software; hard, from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate biological phenomena. The term artificial life is often used to specifically refer to soft alife. 


Any change in the structure or function of an entity (say, a biological organism) that allows it to survive and reproduce more effectively in its environment.

Adaptive walk

A process by which a system changes from one state to another by gradual steps. The system walks' across the fitness landscape, each step is assumed to lead to an improvement in the performance of the system against some criteria (adaptation).

Agents - based model

An agent-based model (ABM) (also sometimes related to the term multi-agent system or multi-agent simulation) is a class of computational models for simulating the actions and interactions of autonomous agents (both individual and collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole. It combines elements of game theory, complex systems, emergence, computational sociology, multi-agent systems, and evolutionary programming. Monte Carlo Methods are used to introduce randomness. ABM's are also called individual-based models.

Agile Development

The Agile family of development methodologies was born out of a belief that an approach more grounded in human reality would yield better results. Agile emphasizes building working software that people can get hands on with quickly, versus spending a lot of time writing specifications up front. Agile focuses on small, cross-functional teams empowered to make decisions, versus big hierarchies and compartmentalization by function, and Agile focuses on rapid iteration, with as much customer input along the way as possible. Often when folks learn about Agile, there is a glimmer of recognition – it sounds a lot like back in the start-up days, when we just did it.

Amoeba management

Amoeba management seeks to structure a company into small, fast-responding, customer focused, entrepreneurially oriented business units operating like independent companies that share a united purpose, i.e., the parent organization’s goals and objectives. The amoebas are intended to act in oordinated independence from each other. The goal is to empower each amoeba to the point that each is akin to an independent company, with each seeking to manage its profitability.

The use of the word amoeba is meant to capture the concept of an entity at its smallest, most elemental level, as well as to describe its life-like capability to “multiply and change shape in response to the environment (Inamori, 1999: 57). In other words, amoeba management is intended to offer a spontaneous, homeostatic response to a business world that features rapid, dynamic change. Amoebas typically consist of 5-50 employees. Each amoeba is responsible for a meaningful organizational activity, an activity that is meant to mirror what currently exists (or could exist) in the outside, competitive environment. The amoeba manager and his/her employees are encouraged to act like the owner of a small, independent company.


An attractor is a set towards which a dynamical system evolves over time. That is, points that get close enough to the attractor remain close even if slightly disturbed. Geometrically, an attractor can be a point, a curve, a manifold, or even a complicated set with a fractal structure known as a strange attractor. Describing the attractors of chaotic dynamical systems has been one of the achievements of chaos theory.

A trajectory of the dynamical system in the attractor does not have to satisfy any special constraints except for remaining on the attractor. The trajectory may be periodic or chaotic or of any other type.

Autonomous (or Adaptive-) Agent

An entity that, by sensing and acting upon its environment, tries to fulfill a set of goals in a complex, dynamic environment. Properties: (1) it can sense the environment through its sensors and act on the environment through its actuators; (2) it has an internal information processing and decision making capability; (3) it can anticipate future states and possibilities, based on internal models (which are often incomplete and/or incorrect); (4) this anticipatory ability often significantly alters the aggregate behavior of the system of which an agent is part. An agent's goals can take on diverse forms: (i) desired local states;(ii) desired end goals;(iii) selective rewards to be maximized; (iv) internal needs (or motivations) that need to be kept within desired bounds. Since a major component of an agent's environment consists of other agents, agents spend a great deal of their time adapting to the adaptation patterns of other agents.


Autopoiesis literally means self-reproduction, and expresses a fundamental complementarity between structure and function. More precisely, the term refers to the dynamics of non-equilibrium structures; that is, organized states (sometimes also called dissipative structures) that remain stable for long periods of time despite matter and energy continually flowing through them. A vivid example of a nonequilibrium structure is the Great Red Spot on Jupiter, which is essentially a gigantic whirlpool of gases in Jupiter's upper atmosphere. This vortex has persisted for a much longer time (on the order of centuries) than the average amount of time any one gas molecule has spent within it.

Business Ecosystems

The term business ecosystem, first introduced by James Moore, defines the ecosystem as being made up of customers, market intermediaries including agents and channels, and those who sell complementary products and services), suppliers, and of course, oneself. We have extended and refined Moore's original concept to recognize the importance of creating value for customers through the provision of additional information, goods, and services and the use of the Internet and other enabling technologies.  Value creation enables a business to distinguish itself from competitors and provides a means of establishing a bond with the consumer; Internet technologies provide the interconnectedness that supports the creation of new business ecosystems.

Basin of attraction.

A region in phase space associated with a given attractor. The basin of attraction of an attractor is the set of all (initial) points that go to that attractor.


A bifurcation is a pattern of instability that often manifests as a sudden, spontaneous change in the attractor pattern of a dynamical system. Within nonlinear states, as control parameters are increased or decreased smoothly, bifurcations often arise abruptly at transition zones in response to tiny changes in a control parameter. Within graphical depictions of state space, bifurcations appear as crossroads in a system’s trajectory, such as the switch from a fixed point to a limit cycle attractor or the progression from order to chaos, whose bifurcation sequence reveals fractal structure. In the reverse situation, where order self-organizes spontaneously out of chaotic bases, complexity builds as bifurcations reduce entropy in local areas. When applying nonlinear theory to living organisms, bifurcations can be inherent in either discrete state changes that occur within real time or discrete stage changes that occur within developmental time. In the human infant unevenness in the emergence of new capacities means that different bifurcations exist for different developmental functions, such as speech or motor coordination, both within and between individuals. Within the experience-dependent, self-organizing right brain, transitions in development, both towards greater order or the breakdown of previous order, can be graphed as one or more bifurcations in state space. Bifurcations are inherent in all catastrophe models. Bifurcation diagram is Visual summary of the succession of period-doubling produced as a control parameter is changed. Also see logistic map.


The splitting into two modes of behavior of a system that previously displayed only one mode. This splitting occurs as a control parameter is continuously varied. In the Logistic Equation, for example, a period-doubling bifurcation occurs whenever all the points of period-2n cycle simultaneously become unstable and the system becomes attracted to a new period-2n+1 cycle.

Bio-inspired computing

Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social behaviour and emergence. It is often closely related to the field of artificial intelligence, as many of its pursuits can be linked to machine learning. It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model nature, and simultaneously the study of nature to improve the usage of computers. Biologically inspired computing is a major subset of natural computation


Biomimicry (from bios, meaning life, and mimesis, meaning to imitate) is a new discipline that studies nature's best ideas and then imitates these designs and processes to solve human problems. Studying a leaf to invent a better solar cell is an example. I think of it as innovation inspired by nature. The core idea is that nature, imaginative by necessity, has already solved many of the problems we are grappling with. Animals, plants, and microbes are the consummate engineers. They have found what works, what is appropriate, and most important, what lasts here on Earth. This is the real news of biomimicry: After 3.8 billion years of research and development, failures are fossils, and what surrounds us is the secret to survival. Like the viceroy butterfly imitating the monarch, we humans are imitating the best adapted organisms in our habitat. We are learning, for instance, how to harness energy like a leaf, grow food like a prairie, build ceramics like an abalone, self-medicate like a chimp, create color like a peacock, compute like a cell, and run a business like a hickory forest. The conscious emulation of life's genius is a survival strategy for the human race, a path to a sustainable future. The more our world functions like the natural world, the more likely we are to endure on this home that is ours, but not ours alone.


Chaos describes the behavior of a system that appears random, but is actually produced by deep order underneath. Chaos can be characterized by simple deterministic equations. The hallmark of a system in chaos is sensitive dependence on initial conditions, which means that slight changes in starting places dramatically alter the dynamical system’s course. Chaotic systems are deterministic, in that current behavior is based precisely upon past states, even though future states are fundamentally unpredictable. Numerical sequences that are generated by chaotic equations are also bounded and non-repeating; both of these principles are matters of degree. The basin or outer rim of a chaotic attractor is a fractal pattern. Chaos has been identified in physiological, human social, and economic phenomena.


A catastrophe is a discontinuous change of events, which is produced by a process that involves an underlying continuity. According to catastrophe theory, all discontinuous changes of events can be modeled by one of seven elementary topological models (with qualifications). The models vary in complexity, which is illustrated by the number and type of attractors, order parameters, control parameters, and bifurcations that are involved in the process. Catastrophe models are useful for describing the global changes that result from self-organizing events. The cusp catastrophe model, which is one of the most widely used of the elementary seven models


The evolutionary process of a biological species in nature is often described as though that species were trying to adapt to a fixed environment. However, such a description only crudely approximates what really happens. In nature, the environment consists of both a relatively (but not completely) stable physical environment as well as other species of organisms that are simultaneously trying to adapt to their environment. The actions of each of these other species typically affect the actions of all other species that occupy the same physical environment. In biology (and hence Artificial Life and studies involving Genetic Algorithms), the terms co adaptation and co-evolution are sometimes used to refer to the fact that all species simultaneously co-adapt and co-evolve in a given physical environment.

Closed system.

Also known as a Hamiltonian system, a closed system in one in which the entities inside the system have no interaction with entities outside the system. Closed systems are conservative of  energy, unlike dissipative systems. For real-world systems, the designation of open or closed is more of a matter of degree. A system containing water, vapor, a sealed container, and a heat source would be closed. A loose social network where members of the network can join or leave regularly is relatively open.


Cognition is the scientific term for "the process of thought". Usage of the term varies in different disciplines; for example in psychology and cognitive science, it usually refers to an information processing view of an individual's psychological functions. Other interpretations of the meaning of cognition link it to the development of concepts; individual minds, groups, and organizations. Maturana (1970) defined cognition as the operation of organizationally closed networks of processes. A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Cognition in a self-sustaining system thus comprises homeostatic processes within the system responding to perturbations in order to maintain the entity's capacity for self-maintenance and self-sustainment.

Complex Adaptive System (CAS) 

A CAS is a complex, self-similar collection of interacting adaptive agents. The study of CAS focuses on complex, emergent and macroscopic properties of the system. Various definitions have been offered by different researchers:

A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents.

A CAS behaves/evolves according to three key principles: order is emergent as opposed to predetermined (c.f. Neural Networks), the system's history is irreversible, and the system's future is often unpredictable. The basic building blocks of the CAS are agents. Agents scan their environment and develop schema representing interpretive and action rules. These schema are subject to change and evolution.

Macroscopic collections of simple (and typically nonlinear) interacting units that are endowed with the ability to evolve and adapt to a changing environment

 Collective intelligence

Collective intelligence is older than humankind itself is. Here is a broad, straightforward  Definition:

Collective intelligence is any intelligence that arises from ‐‐ or is a capacity or characteristic of  groups and other collective living systems.

Primal forms of collective intelligence manifest in the synergies and resilience of ecosystems. This is often referred to as the wisdom of nature", which "learns from its experience" through the interactive create‐and‐test dynamics of evolution. Collective intelligence becomes more obvious in groups of social animals like ants, bees, certain fishes and birds, and many mammals, including wolves and primates. Members of the first human groups shared with those evolutionary ancestors the instinct to combine their respective information and expertise to meet survival tasks they could not possibly meet separately. Those early forms of collective intelligence gave rise to language and tools, which, in turn, enabled new forms of collective intelligence to evolve that, were capable of absorbing more complexity. In today's world, collective intelligence serves diverse functions, comes in diverse forms, and has many diverse names. For example, there is statistical collective intelligence, also known as the "wisdom of crowds" (named after the book with the same title), in which people simply "act in their own self‐interest by playing the game to win", and their compounded


Intuitively, complexity is usually greatest in systems whose components are arranged in some intricate difficult-to-understand pattern or, in the case of a dynamical system, when the outcome of some process is difficult to predict from its initial state. In it’s lowest precisely when a system is either highly regular, with many redundant and/or repeating patterns or when a system is completely disordered. While over 30 measures of complexity have been proposed in the research literature, they all fall into two general classes:

(1) Static Complexity which addresses the question of how an object or system is put together (i.e. only purely structural informational aspects of an object), and is independent of the processes by which information is encoded and decoded;

(2) Dynamic Complexity -which addresses the question of how much dynamical or computational effort is required to describe the information content of an object or state of a system. Note that while a system's static complexity certainly influences its dynamical complexity, the two measures are not equivalent. A system may be structurally rather simple (i.e. have a low static complexity), but have a complex dynamical behavior.


The cohesiveness, coordination, and correlation characterizing emergent structures in self-organizing systems. For example, laser light is coherent compared to the light emanating from a regular light bulb. That emergent structures show a kind of order not found on the lower level of components suggests that complex systems contain potentials of functioning that have not been recognized before. Businesses and institutions can facilitate and utilize the coherence of emergent structures in place of the imposed kind of order found in the traditional bureaucratic hierarchy.


Criticality is a concept borrowed from thermodynamics. Thermodynamic systems generally get more ordered as the temperature is lowered, with more and more structure emerging as cohesion wins over thermal motion. Thermodynamic systems can exist in a variety of phases -gas, liquid, solid, crystal, plasma, etc. -and are said to be critical if poised at a phase transition. Many phase transitions have a critical point associated with them, that separates one or more phases. As a thermodynamic system approaches a critical point, large structural fluctuations appear despite the fact the system is driven only by local interactions. The disappearance of a characteristic length scale in a system at its critical point, induced by these structural fluctuations, is a characteristic feature of thermodynamic critical phenomena and is universal in the sense that it is independent of the details of the system's dynamics.


Any autocatalytic, self-regulating, adaptive, nonlinear, complex organism, organization, or system, whether physical, biological or social, the behavior of which harmoniously exhibits characteristics of both order and chaos. 2: an entity whose behavior exhibits patterns and probabilities not governed or explained by the behavior of its parts. 3: the fundamental organizing principle of nature and evolution.

Chaos theory

Chaos theory can be defined as the qualitative study of unstable a periodic behavior in deterministic non-linear dynamical systems (Kellert, 1993). It is a part of complexity theory, which concerns itself with non-linear dynamic systems whose behavior does not follow clearly predictable and repeatable pathways. In linear systems, the relationship between an environmental factor and system behavior is predictable and easily modeled. As the presence of an environmental factor increases, the system behavior changes linearly in response to it. In contrast, behavior in chaotic systems may be perceived as unpredictable. Periods of inactivity may be punctuated by sudden change, apparent patterns of behavior may disappear, and new patterns unexpectedly emerge. Such behavior emerges in complex systems. This chaotic behavior does not indicate a lack of order. Rather, the order is difficult or impossible to describe in simple terms and requires complex narrative description.

Dissipative Structure

A system that is characterized by semi-permeable boundaries and which leaks energy into the environment. Dissipative symptoms were first thought to be symptomatic of a system that would eventually suffer from heat death, It is now known that dissipative systems self-organize to maintain their functionality. Also see closed system

Dissipative Dynamical Systems

Dissipative systems are dynamical systems that are characterized by some sort of "internal friction" that tends to contract phase space volume elements. Phase space contraction, in turn, allows such systems to approach a subset of the space called an Attractor (consisting of a fixed point, a periodic cycle, or Strange Attractor), as time goes to infinity.


The phrase edge-of-chaos refers to the idea that many complex adaptive systems, including life itself, seem to naturally evolve towards a regime that is delicately poised between order and chaos. More precisely, it has been used as a metaphor to suggest a fundamental equivalence between the dynamics of phase transitions and the dynamics of information processing. Water, for example, exists in three phases: solid, liquid and gas. Phase transitions denote the boundaries between one phase and another. Universal computation -that is, the ability to perform general purpose computations and which is arguably an integral property of life exists between order and chaos. If the behavior of a system is too ordered, there is not enough variability or novelty to carry on an interesting calculation; if, on the other hand, the behavior of a system is too disordered, there is too much noise to sustain any calculation. Similarly, in the context of evolving natural ecologies, edge-of-chaos refers to how -in order to successfully adapt -evolving species should be neither too methodical nor too whimsical or carefree in their adaptive behaviors. The best exploratory strategy of an evolutionary "space" appears at a phase transition between order and disorder. Despite the intuitive appeal of the basic metaphor, note that there is currently some controversy over the veracity of this idea.


Emergence refers to the appearance of higher-level properties and behaviors of a system that while obviously originating from the collective dynamics of that system's components -are neither to be found in nor are directly deducable from the lower-level properties of that system. Emergent properties are properties of the whole that are not possessed by any of the individual parts making up that whole. Individual line of computer code, for example, cannot calculate a spreadsheet; an air molecule is not a tornado; and a neuron is not conscious. Emergent behaviors are typically novel and unanticipated.


A measure of the degree of randomness or disorder in a system. Determines a system's capacity to evolve irreversibly in time. Specific definitions vary depending on the type of system considered. Examples: (1) in statistical systems, the entropy is proportional to the logarithm of the total number of possible states with the same energy as the state under consideration.; (2) in classical thermodynamics, the differential change in entropy of a system near equilibrium is the differential change in absorbed heat divided by the system temperature; (3) in nonlinear deterministic dynamical systems, the Kolmogorov-Sanai entropy is often used as a measure. It is defined as the sum of the positive Lyapunov Exponents of the system.


A general term referring to the dynamical unfolding of behavior over time. Darwinian evolution refers to the unfolding of higher (i.e. more complex) life forms out of lower life forms.

Evolutionary Programming

Evolutionary  programming, originally conceived by Lawrence J. Fogel in 1960, is a stochastic optimization  strategy  similar  to  genetic      algorithms,  but  instead  places  emphasis on the behavioral linkage between parents and their offspring, rather than seeking  to  emulate      specific  genetic  operators  as  observed  in  nature. Evolutionary     programming is similar to  evolution  strategies,  although  the  two     approaches developed independently like  both  ES  AND  GAs,  EP is a useful method of optimization when     other techniques such  as  gradient  descent  or  direct,  analytical      discovery  are  not  possible. Combinatoric and real-valued function     optimization in which the optimization surface or fitness landscape  is  rugged,  possessing  many  locally  optimal solutions, are well      suited for evolutionary programming. 


Ecology study of is the scientific study of the relation of living organisms with each other and their surroundingsEcology is a sub-discipline of biology, the study of life. The word ecology was coined in 1866 by the German scientist Ernst Haeckel (1834–1919).


An ecosystem is a biological environment consisting of all the organisms living in a particular area, as well as all the nonliving, physical components of the environment with which the organisms interact, such as air, soil, water, and sunlight.[1] It is all the organisms in a given area, along with the nonliving (abiotic) factors with which they interact; a biological community and its physical environment.

Ecological Footprint

The measure of the planet's ability to support human demand of natural resources and impact on biological ecosystems

Evolutionary Stable Strategy

A concept from a generalized form of Game Theory. Animals are endowed with a finite set of possible strategies that they can use in their interactions with other animals. Strategies may be "pure," in which the animal acts according to a prescribed set of instructions in all contexts, or "mixed," in which the animal adopts different strategies with different probabilities. The evolutionary stable strategy (ESS) is a strategy, or set of strategies such that if it is adopted by all animals no other strategy can invade the population.


A geometrical pattern, structure, or set of points which is self-similar (exhibiting an identical or similar pattern) on different scales. For example, Benoit Mandelbrot, the discoverer of fractal geometry, describes the coast of England as a fractal, because as it is observed from closer and closer points of view (i.e., changing the scale), it keeps showing a self-similar kind of irregularity. Another example is the structure of a tree with its self- similarity of branching patterns on different scales of observation, or the structure of the lungs in which self-similar branching provides a greater area for oxygen to be absorbed into the blood. Strange attractors in chaos theory have a fractal structure. The imagery of fractals has been popularized by the fascinating graphical representations of fractals in the form of Mandelbrot and Julia Sets on a personal computer.Unlike the whole number characteristic of our usual dimensions, e.g., two or three dimensional drawings, the dimension of a fractal is not a whole number but a fractional part of a whole number such as a dimensionality of 2.4678.

 Fitness Landscape

A name for the landscape representing the fitness measure (or Cost Function) of a problem. Examples: Traveling Salesman Problem, survivability of a real or virtual creature.

Genetic Algorithms

Genetic algorithms are a class of heuristic search methods and computational models of adaptation and evolution based on natural selection. In nature, the search for beneficial adaptations to a continually changing environment (i.e. evolution) is fostered by the cumulative evolutionary knowledge that each species possesses of its forebears. This knowledge, which is encoded in the chromosomes of each member of a species, is passed from one generation to the next by a mating process in which the chromosomes of "parents" produce "offspring" chromosomes. Genetic algorithms mimic and exploit the genetic dynamics underlying natural evolution to search for optimal solutions of general combinatorial optimization problems. They have been applied to the travelling salesman problem, VLSI circuit layout, gas pipeline control, the parametric design of aircraft, neural net architecture, models of international security, and strategy formulation.

Genetic Programming

Genetic programming is essentially an application of genetic algorithms to computer programs. Typically the genome is represented by a LISP expression, so that what evolves is a population of programs, rather than bit-strings as in the case of a usual genetic algorithm.


The genetic instruction code of an individual.


A holon is a system (or phenomenon) which is an evolvingself-organizingdissipative structure, composed of other holons, whose structures exist at a balance point between chaos and order. It is maintained by the throughput of matter-energy and information-entropy connected to other holons and is simultaneously a whole in and itself at the same time being nested within another holon and so is a part of something much larger than itself. Holons range in size from the smallest subatomic particles and strings, all the way up to the multiverse, comprising many universes. Individual humans, their societies and their cultures are intermediate level holons, created by the interaction of forces working upon us both top-down and bottom-up. On a non-physical level, words, ideas, sounds, emotions—everything that can be identified is simultaneously part of something, and can be viewed as having parts of its own, similar to sign in regard of semiotics.


Holism asserts that everything exists in relationship, in a context of connection and meaning  and that any change or event causes a realignment, however slight, throughout the entire pattern. The whole is greater than the sum of its parts” means that the whole is comprised of a pattern of relationships that are not contained by the parts but ultimately define them. Holism, stands in stark opposition to the method of reductionism, which holds that analysis, dissection, and strict definition are the tools for understanding reality. Holism asserts that phenomena can never be fully understood in isolation; it asserts that reductionism can only give us a partial view of anything it dissects. Holism cannot be pin down precisely, because by its very nature it embraces paradox, mystery, and outright contradiction. Holism is not an ideology but a spiritual quest for compassion and peace. Holism treasures diversity, variety, uniqueness.


Virtually all natural hierarchies are composed of holons - wholes that are simultaneously parts of other wholes. For this reason, Arthur Koestler pointed out that the word hierarchy should really be holarchy. All natural hierarchies - that is, all natural holarchies are composed of whole/parts or holons, and they show increasing orders of wholeness, unity, and functional integration.In a holarchy (or hierarchy) each successive holon transcends but includes its predecessors. Each senior element contains or enfolds its juniors as components in its own makeup, but then adds something emergent, distinctive, and defining that is not found in the lower level: it transcends and includes. For example, atoms contain neutrons, but neutrons do not contain atoms; molecules contain atoms, but not vice versa; cells contain molecules, but not vice versa.


Homeostasis (from Greek: hómoios, similar and στάσις, stásis, standing still defined by Claude Bernard and later by Walter Bradford Cannon in 1926, 1929 and 1932) is the property of a system, either open or closed, that regulates its internal environment and tends to maintain a stable, constant condition. Typically used to refer to a living organism, the concept came from that of milieu interieur that was created by Claude Bernard and published in 1865. Multiple dynamic equilibrium adjustment and regulation mechanisms make homeostasis possible.


Hierarchies consist of levels each of which include all lower levels; i.e. systems within systems within systems within the total system in question. Evolution in complex systems leads to differentiation in multilevel hierarchic systems.

Keystone Species

A keystone species is a species that is disproportionately connected to more species in the food-web. Keystone species have lower levels of biomass in the trophic pyramid relative to the importance of their role. The many connections that a keystone species holds means that it maintains the organization and structure of entire communities. The loss of a keystone species results in a range of dramatic cascading effects that alters trophic dynamics, other food-web connections and can cause the extinction of other species in the community

Living systems theory

Living systems theory is a general theory about the existence of all living systems, their structure, interaction, behavior and development. This work is created by James Grier Miller, which was intended to formalize the concept of life. According to Miller's original conception as spelled out in his magnum opus Living Systems, a "living system" must contain each of twenty "critical subsystems", which are defined by their functions and visible in numerous systems, from simple cells to organisms, countries, and societies. In Living Systems Miller provides a detailed look at a number of systems in order of increasing size, and identifies his subsystems in each.

Learning organization

A learning organization is the term given to a company that facilitates the learning of its members and continuously transforms itself. Learning organizations develop as a result of the pressures facing modern organizations and enables them to remain competitive in the business environment. A learning organization has five main features; systems thinking, personal mastery, mental models, shared vision and team learning

Lotka-Volterra Equations

In 1926, Volterra proposed a simple model for the predation of one species by another to explain the oscillatory level of certain fish in the Atlantic. If N(t) is the prey population and P(t) is the predator population at time t then Volterras's model is dN/dt = N (a -bP), dP/dt = P (cN -d), where a,b,c, and d are positive constants. The model assumes: (1) prey in absence of predation grows linearly with N (i.e. in Malthusian fashion); (2) predation reduces prey's growth rate by a term proportional to the prey and predation populations; (3) the predator's death rate, in the absence of prey, decays exponentially; (4) the prey's contribution to the predator's growth rate is proportional to the available prey as well as to the size of the predator population. The system of equations is known as the Lotka-Volterra equations because Lotka derived the same equations in 1920 for a chemical reaction he believed to exhibit periodic behavior.


Memetics is a theory of mental content based on an analogy with Darwinian evolution, originating from Richard Dawkins' 1976 book The Selfish Gene. It purports to be an approach to evolutionary models of cultural information transfer. A meme, analogous to a gene, is essentially a "unit of culture"—an idea, belief, pattern of behaviour, etc. which is "hosted" in one or more individual minds, and which can reproduce itself from mind to mind. Thus what would otherwise be regarded as one individual influencing another to adopt a belief is seen memetically as a meme reproducing itself. As with genetics, particularly under Dawkins's interpretation, a meme's success may be due to its contribution to the effectiveness of its host. Memetics is notable for sidestepping the traditional concern with the truth of ideas and beliefs.


Microcosms are artificial, simplified ecosystems that are used to simulate and predict the behaviour of natural ecosystems under controlled conditions. Open or closed microcosms provide an experimental area for ecologists to study natural ecological processes. Microcosm studies can be very useful to study the effects of disturbance or to determine the ecological role of key species. A Winogradsky column is an example of a microbial microcosm.


If f is a nonlinear function or an operator, and x is a system input (either a function or variable), then the effect of adding two inputs, x1 and x2, first and then operating on their sum is, in general, not equivalent to operating on two inputs separately and then adding the outputs together; i.e. . Popular form: the whole is not necessarily equal to the sum of its parts. Dissipative nonlinear dynamic systems are capable of exhibiting self-organization and chaos.

Prisoner's Dilemma

The prisoner's dilemma is a two person non-zerosum game that has been widely used in experimental and theoretical investigations of cooperative behavior. Two persons suspected of a crime are caught, but there is not enough evidence to sentence them unless one of them confesses. If they are both quiet (or cooperate, C), both will have to be released. If one confesses (defects, D) but the other does not, the one who confesses will be released but the other will be imprisoned for a long time. Finally, if both confess, both will be imprisoned, but for a shorter time. It is assumed that the prisoner's make their respective choices separately and independently of one another. If the game is "played" once, each player find defection to be the optimal behavior, regardless of what his opponent chooses to do. Finding the optimal strategy to follow over time, however, is considerably more difficult.

Punctuated Equilibrium

A theory introduced in 1972 to account for what the fossil record appears to suggest are a series of irregularly spaced periods of chaotic and rapid evolutionary change in what are otherwise long periods of evolutionary stasis. Some Artificial Life studies suggest that this kind of behavior may be generic for evolutionary processes in complex adaptive systems.


Resilience is an inherent ability of a system to absorb a significant negative change and recover then recover to an acceptable service level. Resilience is therefore a function of a system’s vulnerabilities and its ability to adapt.


One of the fastest-growing agile methods is Scrum. It was formalized over a decade ago by Ken Schwaber and Dr. Jeff Sutherland, and it’s now being used by companies large and small, including Yahoo!, Microsoft, Google, Lockheed Martin, Motorola, SAP, Cisco, GE, CapitalOne and the US Federal Reserve. Many teams using Scrum report significant improvements, and in some cases complete transformations, in both productivity and morale.  Scrum is simple, powerful, and rooted in common sense. Scrum is an iterative, incremental framework. Scrum structures product development in cycles of work called Sprints, iterations of work which are typically 1-4 weeks in length, and which take place one after the other. The Sprints are of fixed duration – they end on a specific date whether the work has been completed or not, and are never extended. At the beginning of each Sprint, a cross-functional team selects items from a prioritized list of requirements, and commits to complete them by the end of the Sprint; during the Sprint, the deliverable does not change. Each workday, the team gathers briefly to report to each other on progress, and update simple charts that orient them to the work remaining. At the end of the Sprint, the team demonstrates what they have built, and gets feedback which can then be incorporated in the next Sprint. Scrum emphasizes producing working product at the end of the Sprint is really “done”; in the case of software, this means code that is fully tested and potentially shippable.

 Scrum Roles

 In Scrum, there are three primary roles: The Product Owner, The Team, and The Scrum Master. The Product Owner is responsible for achieving maximum business value, by taking all the inputs into what should be produced – from the customer or end-user of the product, as well as from Team Members and stakeholders – and translating this into a prioritized list. In some cases, the Product Owner and the customer are the same person; in other cases, the customer might actually be millions of different people with a variety of needs.

 Product Owner

The Product Owner role maps to the Product Manager or Product Marketing Manager position in many organizations. The Team builds the product that the customer is going to consume: the software or website, for example. The team in Scrum is “cross-functional” – it includes all the expertise necessary to deliver the potentially shippable product each Sprint – and it is “self-managing”, with a very high degree of autonomy and accountability. The team decides what to commit to, and how best to accomplish that commitment;

Scrum Master

 The Scrum Master helps the team be successful using Scrum. The Scrum Master is not the manager of the team; the Scrum Master serves the team, protects the team from outside interference, and facilitates the team’s Scrum practices. Most teams will have someone dedicated fully to the role of Scrum Master, although a smaller team might have a team member play this role (carrying a lighter load of regular work when they do so). Great Scrum Masters have come from all backgrounds and disciplines: Project Management, Engineering, Design, and Testing. The ScrumMaster and the Product Owner probably shouldn’t be the same individual; at times, the ScrumMaster may be called upon to push back on the Product Owner (for example, if they try to introduce new deliverables in the middle of a Sprint). And unlike a Project Manager, the ScrumMaster doesn’t tell people what to do or assign tasks – they facilitate the process, supporting the team as it organizes and manages itself.

Second-order learning

Second-order learning involves active manipulation and change of the interpretive schema. One barrier toward such learning is that high skill in first-order learning—leading typically to rewarded performance and promotion to managerial ranks—can actually detract from the ability to perform second-order learning.


The essence of self-organization is that system structure often appears without explicit pressure or involvement from outside the system. In other words, the constraints on form (i.e. organization) of interest to us are internal to the system, resulting from the interactions among the components and usually independent of the physical nature of those components. The organization can evolve in either time or space, maintain a stable form or show transient phenomena. General resource flows within self-organized systems are expected (dissipation), although not critical to the concept itself.

The field of self-organization seeks general rules about the growth and evolution of systemic structure, the forms it might take, and finally methods that predict the future organization that will result from changes made to the underlying components. The results are expected to be applicable to all other systems exhibiting similar network characteristics.

Self-Organized Criticality

Self-organized criticality (SOC) describes a large body of both phenomenological and theoretical work having to do with a particular class of time-scale invariant and spatial-scale invariant phenomena. Fundamentally, SOC embodies the idea that dynamical systems with many degrees of freedom naturally self-organize into a critical state in which the same events that brought that critical state into being can occur in all sizes, with the sizes being distributed according to a power-law. The kinds of structures SOC seeks to describe the underlying mechanisms for look like equilibrium systems near critical points (see Criticality) but are not near equilibrium; instead, they continue interacting with their environment, "tuning themselves" to a point at which critical-like behavior appears. Introduced in 1988, SOC is arguably the only existing holistic mathematical theory of self-organization in complex systems, describing the behavior of many real systems in physics, biology and economics. It is also a universal theory in that it predicts that the global properties of complex systems are independent of the microscopic details of their structure, and is therefore consistent with the "the whole is greater than the sum of its parts" approach to complex systems. Put in the simplest possible terms, SOC asserts that complexity is criticality. That is to say, that SOC is nature's way of driving everything towards a state of maximum complexity.

Strange Attractors

Describes a form of long-term behavior in dissipative dynamical systems. A strange attractor is an Attractor that displays sensitivity to initial conditions. That it to say, an attractor such that initially close points become exponentially separated in time. This has the important consequence that while the behavior for each initial point may be accurately followed for short times, prediction of long time behavior of trajectories lying on strange attractors becomes effectively impossible. Strange attractors also frequently exhibit a self-similar or fractal structure.


Synergetics refers to what can loosely be called the "European" (vice US) approach to the study of complex systems. Consider a complex system (that is, a system composed of many individual parts) that is controlled from the outside in some manner by a control parameter (say, the system is driven by a constant influx of energy and/or matter). As the control parameter is changed, the system's state can become unstable and be replaced by a new state characterized by particular kinds of spatial, temporal or functional structures. Synergetics consists of strategies of describing what happens when the macroscopic state of systems undergoes a qualitative change. More colloquially, "synergy" is used to refer to how the action of two or more entities ("parts") can achieve an effect that cannot be achieved by any of the parts alone (see Emergence).

Social ecology

Social ecology integrates the study of human and natural ecosystems through understanding the interrelationships of culture and nature. It advances a critical, holistic world view and suggests that creative human enterprise can construct to the natural world by reharmonizing their relationship with each other.


Sustainability relates to the relationships between economic, social, institutional and environmental aspects of human existence. It organizes decisions to allow for current human needs to be met while preserving biodiversities and ecosystems to maintain the same quality of life for future generations.

Sustainability stewardship

Sustainability stewardship is the responsible caretaking and creative cultivation of resources — social, cultural, financial, and natural — to generate stakeholder value while contributing to the well-being of current and future generations of all beings.


The term symbiosis is also of Greek origin; it means, living together. It is use as a technical term in biology traces to the German mycologist Anton de Bary (1879), who employed it to denote the living together of dissimilar or differently named organisms in lasting and intimate relationships. His focus was on relationships and the paradigm examples both in de Bary's time and ever since, are the roughly 18,000 different species of lichen mutualistic partnerships between some 300 genera of fungi and various species of cyanobacteria (formerly known as blue-green algae) and green algae although de Bary also included in his definition what would now be called parasitic relationships.

Swarm Intelligence

Swarm Intelligence

#  is a mindset rather than a technology.

#  It is a bottom-up approach to controlling and optimizing distributed systems, using resilient, decentralized, self-organized techniques, initially inspired by how social insects operate

# Shaped by millions of years of evolution (but does it matter in the end).

Swarmware and Clockware:

Two terms coined by the editor of Wired Magazine Kevin Kelly for two antithetical management processes. "Clockware" are rational, standardized, controlled, measured processes; whereas "swarmware" are processes including experimentation, trial and error, risk-taking, autonomy of agents. Clockware processes are seen in linear systems whereas swarmware is what happens in complex systems undergoing self-organization as a result of the nonlinear interaction among components.


Trajectory (orbit). A sequence of positions (path) of a system in its phase space. The path from its starting point (initial condition) to and within its attractor.

A system of classifying scientific data in a heirarchy. Taxonomies are typically tree-like structures consisting of nodes called taxa (singular: taxon) and the relationships between parent and child tax

 Virtual community

Virtual communities are social aggregations that emerge from the Net when enough people carry on those public discussions long enough, with sufficient human feeling, to form webs of personal relationships in cyberspace