Glossaries – a work in process

Glossaries – a work in process

Arc – synonym for edge. Cliques – synonym for complete graph, subsets of vertices within which every possible edge exists [S2]. Cluster Clustering Complete Graph – A complete graph has one link between every node. Degree – The degree (or valence) of a vertex (node) is the number of edge ends at that vertex. In a digraph (directed graph) the degree is usually divided into the in-degree and the out-degree (whose sum is the degree of the vertex in the underlying undirected graph). [S1] Degree Distribution Digraph – synonym for directed graph. Directed Graph – A graph where links have a single direction, out from a given node and in to another node. Edge – Link between nodes Fitness Connectivity Product – In the scale-free model the probability that a new node connects to a nod with k links is given by k/Σiηi Graphs – Informally a finite set of nodes (vertices) connected by links (edges, arcs). More formally: a simple graph is a (usually finite) set of vertices V and set of unordered pairs of distinct elements of V called edges. Not all graphs are simple. Sometimes a pair of vertices are connected by multiple edges yielding a multigraph. At times vertices are even connected to themselves by an edge called a loop, yielding a pseudograph. Finally, edges can also be given a direction yielding a directed graph (or digraph). [S1] Growth Hierarchical Network Hub Lattice Link Mesh Node Path – see path length. Path Length – A path is a sequence of consecutive edges in a graph and the length of the path is the number of edges traversed. Power Law Random Graphs Random Networks Resilience Regular Graph – Each node has exactly the same number of links Scale Free Small World Model Valence – see degree. Vertex – synonym for node. Glossary: NetworksSources: S1=www.utm.edu/departments/math/graph/glossary.html, S2=Linked by Barábasi, S3=www.math.fau.edu/locke/GRAPHTHE.HTM

Glossary: CAS • Adaptation – 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 [www.cna.org/isaac/Glossb.htm]. • Agents – Individuals within an interacting population, each may have only limited freedom to react to their neighbours yet the behaviour of the whole (emergent) may be much more complex. Collections of agents are sometimes called 'swarms'. Agent-based models (ABMs) are central to complexity research [www.calresco.org/glossary]. • Anticipatory system – A system containing a predictive model of itself and/or its environment, which allows it to change state at an instant in accord with the model's predictions pertaining to a latter instant. To some extent, this applies to any system incorporating machine learning. At issue is how much of a system's behaviour should or indeed can be determined by reasoning over dedicated representations, how much by on-line planning, and how much must be provided by the system's designers [Robert Rosen] . • Anticipatory system, incursive – able to generate one anticipation [D. M. Dubois and G. Resconi, Hyperincursivity: A New Mathematical Theory. Liège: Presses Universitaires de Liège, 1992]. • Anticipatory system, hyperincursive – able to generate several anticipations [D. M. Dubois and G. Resconi, Hyperincursivity: A New Mathematical Theory. Liège: Presses Universitaires de Liège, 1992]. • Anticipatory system, autoincursive – able to generate successive anticipations as a function of its past, present, and anticipated states, to select among several anticipatiions, and to autonomously change its own learning parameters. Also called autoincursive memory networks [“AIM Networks: Autoincursive Memory Networks for Anticipation Toward Leaned Goals,” Frederic Lavigne, International Journal of Computing Anticipatory Systems, Vol 14, 2004, www.unice.fr/LPEQ/Membres/enseignants/Lavigne/Lavigne-IJCAS-2004.pdf]. • Attractor – An attractor is a set to which a dynamical system evolves after a long enough 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 [Wikipedia 09/30/07]. • Autocatalysis – A process that creates itself by catalytic action. A system of chemical reactions such that each reaction is aided (catalysed) by the product of another in a closed and self-perpetuating sequence [www.calresco.org/glossary].

Glossary: CAS • Autonomous 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 [www.cna.org/isaac/Glossb.htm]. • Autoplectic Systems – Consider a dynamical system whose behavior appears random or chaotic. There are two ways in which an apparent randomness can occur: (1) external noise, so that if the evolution of the system is unstable, external perturbations amplify exponentially with time -such systems are called homoplectic; (2) internal mechanisms, so that the randomness is generated purely by the dynamics itself and does not depend on any external sources or require that randomness be present in the initial conditions -such systems are called autoplectic systems. An example of an autoplectic system is the one-dimensional, two-state, two neighbor Cellular Automaton rule-30, starting from a single non-zero site. The temporal sequence of binary values starting from that single non-zero initial seed are completely random, despite the fact that the evolution is strictly deterministic and the initial state is ordered [www.cna.org/isaac/Glossb.htm]. • Autopoiesis – 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 [www.cna.org/isaac/Glossb.htm]. • Aware Systems – Systems that can respond to their environment in an autonomous way, detecting external conditions and reacting appropriately (a teleological drive). Systems that can plan ahead, also called anticipatory systems [www.calresco.org/glossary].

Glossary: CAS • Bifurcation – 1) A point at which a system splits into two alternative behaviours, either being possible, the one actually followed often being indeterminate (unpredictable). Related to catastrophes in Catastrophe Theory [www.calresco.org/glossary]. 2) 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 [www.cna.org/isaac/Glossb.htm]. • Bricolage – A construction made of whatever materials are at hand, something created from a variety of available things; in literature: a piece created from diverse resources; in art: a piece of makeshift handiwork; the use of multiple, diverse research methods [Dictionary.com 09/30/07)]. In design approach: building by trial and error, often contrasted to engineering theory-based construction [wikipedia 070930] • Catalysis – A reaction taking place due to the presence of an enabling agent, one that is not changed in the process. An essential part of autocatalytic processes [www.calresco.org/glossary]. • Cellular Automata – Cellular automata (CA) are a class of spatially and temporally discrete, deterministic mathematical systems characterized by local interaction and an inherently parallel form of evolution. First introduced by von Neumann in the early 1950s to act as simple models of biological self-reproduction, CA are prototypical models for complex systems and processes consisting of a large number of identical, simple, locally interacting components. The study of these systems has generated great interest over the years because of their ability to generate a rich spectrum of very complex patterns of behavior out of sets of relatively simple underlying rules. Moreover, they appear to capture many essential features of complex self-organizing cooperative behavior observed in real systems. Although much of the theoretical work with CA has been confined to mathematics and computer science, there have been numerous applications to physics, biology, chemistry, biochemistry, and geology, among other disciplines. Some specific examples of phenomena that have been modeled by CA include fluid and chemical turbulence, plant growth and the dendritic growth of crystals, ecological theory, DNA evolution, the propagation of infectious diseases, urban social dynamics, forest fires, and patterns of electrical activity in neural networks. CA have also been used as discrete versions of partial differential equations in one or more spatial variables [www.cna.org/isaac/Glossb.htm].

Chaos – The complexity of causality or the relationship between events. This means that any insignificant event in the universe has the potential to trigger a chain reaction that will change the whole system. A well known saying in connection with this issue is "A butterfly flapping its wings in one part of the world can cause a tornado on the other side of the earth." This is also known as the "butterfly effect". Mathematically, chaos means an aperiodic deterministic behavior which is very sensitive to its initial conditions, i.e., infinitesimal perturbations of boundary conditions for a chaotic dynamic system originate finite variations of the orbit in the phase space [Wikipedia 070930]. Chaos, Edge of – The phrase edge of chaos was coined by computer scientist Christopher Langton in 1990. The phrase originally refers to an area which was varied while examining the behavior of a cellular automaton (CA). As λ varied, the behavior of the CA went through a phase transition of behaviors. Langton found a small area conducive to produce CAs capable of universal computation. At around the same time physicist James P. Crutchfield and others used the phrase onset of chaos to describe more or less the same concept. In the sciences in general, the phrase has come to refer to a metaphor that some physical, biological, economic and social systems operate in a region between order and complete randomness or chaos, where the complexity is maximal. The generality and significance of the idea, however, has since been called into question by Melanie Mitchell and others. Stuart Kauffman has studied mathematical models of evolving systems in which the rate of evolution is maximized near the edge of chaos [Wikipedia 070930]. Chaos Theory – In mathematics and physics, chaos theory describes the behavior of certain nonlinear dynamical systems that under specific conditions exhibit dynamics that are sensitive to initial conditions (popularly referred to as the butterfly effect). As a result of this sensitivity, the behavior of chaotic systems appears to be random, because of an exponential growth of perturbations in the initial conditions. This happens even though these systems are deterministic in the sense that their future dynamics are well defined by their initial conditions, and with no random elements involved. This behavior is known as deterministic chaos, or simply chaos [Wikipedia 070930]. Coevolution – 1) Evolution of species, not only with respect to their environment, but also as to how they relate to other species. This is a more potent form of evolution to that normally considered, changing the shape of the fitness landscape dynamically [www.calresco.org/glossary]. 2) 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 affects the actions of all other species that occupy the same physical environment. In biology the terms "coadaptation" and "coevolution" are sometimes used to refer to the fact that all species simultaneously co-adapt and co-evolve in a given physical environment [www.cna.org/isaac/Glossb.htm]. Complex Adaptive Systems – 1) A form of system containing many autonomous agents who self-organize in a coevolutionary way to optimise their separate values [www.calresco.org/glossary]. 2) Macroscopic collections of simple (and typically nonlinearly) interacting units that are endowed with the ability to evolve and adapt to a changing environment [www.cna.org/isaac/Glossb.htm]. Glossary: CAS

Data Fusion – x Dissipative – Using a resource flow to constantly achieve a task, which may be work (e.g. movement) or more usually to maintain the system in a steady state (e.g. a living organism). Dissipative systems operate far-from-equilibrium [www.calresco.org/glossary]. Dissipative Structure – An organized state of a physical system whose integrity is maintained while the system is far from equilibrium. Example: the great Red-Spot on Jupiter [www.cna.org/isaac/Glossb.htm]. 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 [www.cna.org/isaac/Glossb.htm]. Downward Causation – Effect of higher level emergent properties on the lower level part behaviour. Constraints on the area of state space available [www.calresco.org/glossary]. Dynamical System – The dynamical system concept is a mathematical formalization for any fixed "rule" which describes the time dependence of a point's position in its ambient space. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, and the number of fish each spring in a lake. A dynamical system has a state determined by a collection of real numbers, or more generally by a set of points in an appropriate state space. Small changes in the state of the system correspond to small changes in the numbers. The numbers are also the coordinates of a geometrical space—a manifold. The evolution rule of the dynamical system is a fixed rule that describes what future states follow from the current state. The rule is deterministic: for a given time interval only one future state follows from the current state [Wikipedia 070930]. Ecosystem – The relatively stable balance of different species within a particular area. A food chain, usually cyclic and self-sustaining [www.calresco.org/glossary]. Edge of Chaos – The tendency of dynamic systems to self-organise to a state roughly midway between globally static (unchanging) and chaotic (random) states. This can also be regarded as the liquid phase, half way between solid (static) and gas (random) natural states. In information theory this is the state containing the maximum information [www.calresco.org/glossary]. Glossary: CAS

Emergence – 1) 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 [www.cna.org/isaac/Glossb.htm]. 2) A system exhibits emergence when there are coherent emergents [behaviors] at the macro-level that dynamically arise from the interactions between the parts at the micro-level. Such emergents are novel w.r.t. the individual parts of the system [De Wolf and Holvoet, Emergence Versus Self-Organisation: Different Concepts but Promising When Combined]. 3) Emergence is a behavior perceived as a difference between local purpose and global effect, arising from the interaction of local components – where “local” here means myopic examination of less than the total system in context: looking at individual agents/functions, looking at only some of the agents/functions, looking at all of the agents/functions but not in the context of their environment [Dove in SDOE683 class note]. Entropy – 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 [www.cna.org/isaac/Glossb.htm]. Equilibrium – The tendency of a system to settle down to a steady state that isn't easily disturbed, an attractor. Traditionally, equilibrium systems in physics have no energy input and maximise entropy, usually involving an ergodic attractor, but dissipative systems maintain steady states far-from-equilibrium (also non-equilibrium) [www.calresco.org/glossary]. Glossary: CAS

Evolution – This is a universal idea, generalised as 'general selection theory' to be the process of 'variation, selection, retention' underlying all systemic improvement over time (including 'trial and error' learning). The term is often specifically applied however to genetic evolution where some changes, by being more efficient in functional ways, are preferred by natural selection [www.calresco.org/glossary]. Evolution (Biology) – Evolution is the change in the inherited traits of a population from generation to generation. These traits are the expression of genes that are copied and passed on to offspring during reproduction. Mutations in these genes can produce new or altered traits, resulting in heritable differences (genetic variation) between organisms. New traits can also come from transfer of genes between populations, as in migration, or between species, in horizontal gene transfer. Evolution occurs when these heritable differences become more common or rare in a population, either non-randomly through natural selection or randomly through genetic drift. [Wikipedia 070930] Evolutionary Game Theory - Evolutionary game theory is the application of population genetics-inspired models of change in gene frequency in populations to game theory. It differs from classical game theory by focusing on the dynamics of strategy change more than the properties of strategy equilibria. Despite its name, evolutionary game theory is practiced more by economists than biologists. The common methodology to study the evolutionary dynamics in games is through replicator equations. Replicator equations assume infinite populations, continuous time, complete mixing and that strategies breed true. The attractors (stable fixed points) of the equations are equivalent with evolutionarily stable states. [Wikipedia] Extropy – A term used to denote the tendency of systems to grow more organised, in opposition to the entropy expectation. Also called 'ectropy', 'enformy', 'negentropy' or 'syntropy' (or more generally 'self-organization'). The reasons for this are partly the motivation behind Complexity Theory [www.calresco.org/glossary]. Far From Equilibrium – see Non-Equilibrium. Feedback – A linking of the output of a system back to the input. Traditionally this can be negative, tending to return the system to a wanted state, or positive tending to diverge from that state. Life employs both methods [www.calresco.org/glossary]. Finite State Automata – see finite state machine. Finite State Machine – A machine with a fixed number of internal options or possibilities. These could be as few as 2 (Yes/No) or any number of separate possibilities, each determined by some combination of input parameters [www.calresco.org/glossary]. Fitness – The ability of an organism to survive and flourish in its current environmental conditions, relative to the other creatures also there. A measure of 'quality of life' [www.calresco.org/glossary]. Fitness Landscape – The number of separate niches available within an organism's phase space, often regarded as peaks on a landscape. The higher the peak, the better the option, the steeper the slope the greater the selection pressure [www.calresco.org/glossary]. Fractal – 1) In colloquial usage, a fractal is "a rough or fragmented geometric shape that can be subdivided in parts, each of which is (at least approximately) a reduced-size copy of the whole". Because they appear similar at all levels of magnification, fractals are often considered to be infinitely complex (in informal terms). Natural objects that approximate fractals to a degree include clouds, mountain ranges, lightning bolts, coastlines, and snow flakes [Wikipedia]. 2) Fractals are geometric objects characterized by some form of self-similarity; that is, parts of a fractal, when magnified to an appropriate scale, appear similar to the whole. Coastlines of islands and continents and terrain features are approximate fractals. The Strange Attractors of nonlinear dynamical systems that exhibit deterministic Chaos typically are fractals [www.cna.org/isaac/Glossb.htm]. Glossary: CAS

Game Theory – A branch of applied mathematics that is often used in the context of economics. It studies strategic interactions between agents. In strategic games, agents choose strategies which will maximize their return, given the strategies the other agents choose. The essential feature is that it provides a formal modeling approach to social situations in which decision makers interact with other agents. Game theory has played, and continues to play a large role in the social sciences, and is now also used in many diverse academic fields. Many games, especially the prisoner's dilemma, are used to illustrate ideas in political science and ethics. [Wikipedia 070930] General Systems Theory – The interdisciplinary idea that systems of any type and in any specialism can all be described by a common set of ideas related to the holistic interaction of the components. This nonlinear theory rejects the idea that system descriptions can be reduced to linear properties of disjoint parts [www.calresco.org/glossary]. Genetic Algorithm – a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). Genetic algorithms are implemented as a computer simulation in which a population of abstract representations of candidate solutions to an optimization problem evolves toward better solutions [Wikipedia 070930]. Global Optimum – The very best possible fitness over the entirety of state space [www.calresco.org/glossary]. Heterarchical – A weblike branching structure where multiple owners are possible and loops may form. A N:M structure [www.calresco.org/glossary]. Hierarchical – A treelike branching structure where each component has only one owner or higher level component. A 1:N structure [www.calresco.org/glossary]. Hill Climbing – The ability of mutation to increase the fitness of a agent, such that it climbs to a higher position on the fitness landscape [www.calresco.org/glossary]. Limit Cycle – An Attractor describing regular (i.e. periodic or quasi-periodic) temporal behavior. Multi-Agent Systems – x Natural Selection – The three stage process of variation, selection, reproduction (or persistance) that underlies evolution in all areas (in biology the synthesis of Medelian genetics with natural selection is called neo-Darwinism). It is combined within complex systems thinking with self-organization [www.calresco.org/glossary]. Non-Equilibrium – A system driven by energy flows away from a steady state of maximum entropy. Also called far-from-equilibrium [www.calresco.org/glossary]. Non-Linear – Systems that behave in an unexpected way, not changing proportionally to a change in input. Sometimes going down when you expect them to go up, doing nothing instead, or changing drastically with only minor changes to the input. Nonlinear systems fail the mathematical principle of 'superposition‘ [www.calresco.org/glossary]. NP-Hard Problems – A class of problems, known as nondeterministic polynomial time -or class-NP -problems, that may not necessarily be solvable in polynomial time, but the actual solutions to which may be tested for correctness in polynomial time [www.cna.org/isaac/Glossb.htm]. NP-Complete – Just as there are universal computers that, given a particular input, can simulate any other computer, there are NP-complete problems that, with the appropriate input, are effectively equivalent to any NP-hard problem of a given size. For example, Boolean "satisfiability" -i.e. the problem of determining truth values of the variable's of a Boolean expression so that the expression is true -is known to be an NP-complete problem. Order Parameter An order parameter is a scalar or vector parameter associated with a continuous phase transition that determines the physical nature of the transition. It has the value zero in the random state (typically above the transition temperature) and takes on a nonzero value in the ordered state (typically below the transition). In the case of a fluid, for example, the order parameter is a scalar and is the difference in density between the liquid and vapor phases [www.cna.org/isaac/Glossb.htm]. Glossary: CAS

Open Systems – In the scientific sense: Allowing resources (e.g. material or information) to enter or leave the system, sucking in resources from outside or giving out more than they take in [www.calresco.org/glossary]. Organization – A non-random arrangement of parts, generally serving a purpose or function. The restriction of the system to a small area of its state space [www.calresco.org/glossary]. Perturbation – A forced change to a system. This can result in a sudden shift to a new state, an immediate return to the old state or a long transient resulting in one or the other [www.calresco.org/glossary]. Phase Space – In mathematics and physics, a space in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the phase space. For mechanical systems, the phase space usually consists of all possible values of position and momentum variables. A plot of position and momentum variables as a function of time is sometimes called a phase diagram. This term, however, is more usually reserved in the physical sciences for a diagram showing the various regions of stability of the thermodynamic phases of a chemical system, as a function of pressure, temperature, and composition. [Wikipedia 070930] Phase Transition – In thermodynamics, phase transition or phase change is the transformation of a thermodynamic system from one phase to another. The distinguishing characteristic of a phase transition is an abrupt change in one or more physical properties, in particular the heat capacity, with a small change in a thermodynamic variable such as the temperature. In the English vernacular, the term is most commonly used to describe transitions between solid, liquid and gaseous states of matter, in rare cases including plasma. [Wikipedia 070930] Prisoner's Dilemma – In game theory, the prisoner's dilemma is a type of non-zero-sum game in which two players may each "cooperate" with or "defect" (i.e. betray) the other player. In this game, as in all game theory, the only concern of each individual player ("prisoner") is maximizing his/her own payoff, without any concern for the other player's payoff. In the classic form of this game, cooperating is strictly dominated by defecting, so that the only possible equilibrium for the game is for all players to defect. In simpler terms, no matter what the other player does, one player will always gain a greater payoff by playing defect. Since in any situation playing defect is more beneficial than cooperating, all rational players will play defect, all things being equal. The unique equilibrium for this game is a Pareto-suboptimal solution—that is, rational choice leads the two players to both play defect even though each player's individual reward would be greater if they both played cooperate. In equilibrium, each prisoner chooses to defect even though both would be better off by cooperating, hence the dilemma. In the iterated prisoner's dilemma the game is played repeatedly. Thus each player has an opportunity to "punish" the other player for previous non-cooperative play. Cooperation may then arise as an equilibrium outcome. The incentive to defect is overcome by the threat of punishment, leading to the possibility of a cooperative outcome. [Wikipedia 070930] Proactive – Originally coined by the psychiatrist Victor Frankl in his 1946 book Man's Search for Meaning to describe a person who took responsibility for his or her life, rather than looking for causes in outside circumstances or other people. The term was popularized in the business press in Stephen Covey's 7 Habits of Highly Effective People. Though he used the word in Frankl's original sense, the word has come to mean "to act before a situation becomes a source of confrontation or crisis" vs. after the fact. It is frequently misused to mean simply "active" the opposite of passive. [Wikipedia – 070915] Regime – see phase space. Glossary: CAS

Self Organization – 1) The spontaneous emergence of macroscopic non-equilibrium organized structure due to the collective interactions among a large assemblage of simple microscopic objects [www.cna.org/isaac/Glossb.htm]. 2) A dynamical and adaptive process where systems acquire and maintain structure themselves, without external control (the ‘structure’ can be a spatial, temporal or functional structure – ‘no external control’ refers to the absence of direction, manipulation, interference, pressures or involvement from outside the system; which does not exclude data inputs from outside the system as long as these inputs are not control instructions) [De Wolf and Holvoet, Emergence Versus Self-Organisation: Different Concepts but Promising When Combined]. Self Organized Criticality (SOC) – The ability of a system to reach edge-of-chaos by self-organization [www.calresco.org/glossary]. Self-Organizing Systems – Systems that generate their form by a process of self-organisation, either wholly or in part [www.calresco.org/glossary]. Semiotics – A theoretical framework for the study of the meaning of language, signs and symbols, and is usually divided into three parts: Semantics - what was the meaning of the words or signs used; Pragmatics - who said it, to whom and in what circumstances and Syntactics - the formal rules of the language used. It includes the study of how meaning is constructed and understood. This discipline is frequently seen as having important anthropological dimensions. However, some semioticians focus on the logical dimensions of the science. They examine areas belonging also to the natural sciences - such as how organisms make predictions about, and adapt to, their semiotic niche in the world. In general, semiotic theories take signs or sign systems as their object of study: the communication of information in living organisms is covered in biosemiotics or zoosemiosis. [Wikipedia 070930] Simulated Annealing – A mathematical technique for general combinatorial optimization problems. The name comes from the physical process of annealing, during which a material is first heated and then slowly cooled. During annealing, the component atoms of a material are allowed to settle into a lower energy state so that a more stable arrangement of atoms is maintained throughout the cooling process [www.cna.org/isaac/Glossb.htm]. Stability – Unchanging with time. This can be a static state (nothing changes) or a steady state (resource flows occur). In complex non-equilibrium systems we have multistable states, i.e. many semi-stable positions possible within a single system [www.calresco.org/glossary]. Glossary: CAS

State Space – x Stigmergy – x Stochastic – Random or unpredictable effects, often associated with probabilistic or statistical treatments [www.cna.org/isaac/Glossb.htm]. Strange Attractor – 1) Chaotic motion gives rise to what are known as strange attractors, attractors that can have great detail and complexity. An attractor is informally described as strange if it has non-integer dimension or if the dynamics on it are chaotic. Examples of strange attractors include the Hénon attractor, Rössler attractor, Lorenz attractor, Tamari attractor. A simple three-dimensional model of the Lorenz weather system gives rise to the famous Lorenz attractor [Wikipedia]. 2) 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 [www.cna.org/isaac/Glossb.htm]. Sympoietic – A more open form of self-maintenance than autopoietic, more appropriate for social and ecological forms of organization. Exhibits more diffuse structures and fuzzy boundaries [www.calresco.org/glossary]. Synergy – The idea that combined parts have properties that are more or less that the sum of the parts (positive-sum or negative-sum rather than zero-sum). Related to emergence but much wider. The negative-sum version is sometimes called dysergy, leaving synergy to mean only beneficial effects also studied as symbiosis, 'holistic darwinism', 'synergistic selection', 'synergic evolution', 'cooperative coevolution' or 'compositional evolution' and many combinations thereof [www.calresco.org/glossary]. System – A collection of interacting parts that forms an integrated and consistent whole, isolatable from its surroundings. System of Systems – May be called a system-of-systems if…Component systems achieve well-substantiated purposes even if detached; component systems are managed for their own purposes; the system exhibits emergent behavior not achievable by the component systems acting independently; component systems, functions, and behaviors may be added or removed during its use [Levis, Maier, Sage and USAF report SAB TR-05-04]. Systems Thinking – The systems approach relates to considering wholes rather than parts, taking all the interactions into account, see also General Systems Theory. It considers processes rather than things to be primary [www.calresco.org/glossary]. Unstable Equilibrium – A stationary state of a dynamical system such that an arbitrarily small perturbation can cause a disturbance of arbitrarily large magnitude. Example: an egg poised on the vertex of a cone [www.cna.org/isaac/Glossb.htm]. Zero Sum – The idea, from game theory and economics, that agents swap resources, so that what one loses the other gains leaving a net no-change in fitness (contrast with non-zero, positive and negative sums) [www.calresco.org/glossary]. Glossary: CAS

Glossary: Bio-Life • Genotype – describes the genetic constitution of an individual, that is the specific allelic makeup of an individual, usually with reference to a specific character under consideration. For instance, there are three possible genotypes for the human albino gene, and it has two allelic forms, dominant A and recessive a. Thus the three possible genotypes are AA (homozygous dominant), Aa (heterozygous), and aa (homozygous recessive). It is a generally accepted theory that inherited genotype, transmitted epigenetic factors, and non-hereditary environmental variation contribute to the phenotype of an individual. • Genotype-phenotype distinction – must be drawn when trying to understand the inheritance of traits and their evolution. The genotype of an organism represents its exact genetic makeup, that is, the particular set of genes it possesses. Two organisms whose genes differ at even one locus (position in their genome) are said to have different genotypes. The transmission of genes from parents to offspring is under the control of precise molecular mechanisms. Genotype refers to the full hereditary information of an organism. The phenotype of an organism represents its actual physical properties, such as height, weight, hair color, and so on. It is the organism's physical properties that directly determine its chances of survival and reproductive output, • Phenotype – describes the total physical appearance of an organism, as opposed to its genotype. 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 concept of the phenotype has some hidden subtleties. In the first place, most of the molecules and structures coded by the genetic material are not visible in the appearance of an organism, yet is part of the phenotype. Human blood groups are an example. Therefore, by extension, the term phenotype must include characteristics that can be made visible by some technical procedure. A further, and more radical, extension would add inherited behavior to the phenotype. Secondly, the phenotype is not simply a product of the genotype, but is influenced by the environment to a greater or lesser extent. The phenotype is composed of traits or characteristics. Some phenotypes are controlled entirely by the individual's genes. Others are controlled by genes but are significantly affected by extragenetic or environmental factors. • Phenotypic plasticity – describes the degree to which an organism's phenotype is determined by its genotype. A high level of plasticity means that environmental factors have a strong influence on the particular phenotype that develops. If there is little plasticity, the phenotype of an organism can be reliably predicted from knowledge of the genotype, regardless of environmental peculiarities during development. An example of high plasticity can be observed in larval newts: when these larvae sense the presence of predators such as dragonflies, they develop larger heads and tails relative to their body size and display darker pigmentation. Larvae with these traits have a higher chance of survival when exposed to the predators, but grow more slowly than other phenotypes

Glossary: System of Systems • Component systems achieve well-substantiated purposes even if detached, • Component systems are managed for their own purposes, • The system exhibits emergent behavior not achievable by the component systems acting independently, • Component systems, functions, and behaviors may be added or removed during its use. • [ Levis, Maier, Sage and USAF report SAB TR-05-04]

Glossaries – a work in process