In this glossary each entry is an hypertext link that takes you to an introduction describing that concept in a wider context. Alternatively, to read all the introductions in sequence start with "Setting The Scene". This is a brief glossary, for a more detailed one see:
ISAAC's. Some of the terms included here are specific to the wider CALResCo viewpoint and may not be common in the work of other more specialised groups.
The ability of an organism to learn in response to changes in its environment over the course of its lifetime. This allows it to improve its fitness over that available from its initial phenotype.
The ability of a species to change in response to changes in its environment over many generations. This requires changes to the genotype in a way that increases an individuals' fitness.
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.
A collection of parts brought together without interactions, typical of the reductionist approach which ignores emergent effects and self-organisation.
Abbreviation for Artificial Life, the study of alternative forms of life to biological (BLife), also abbreviated to AL in contrast with AI (artificial intelligence) which concentrates on the emulation of psychological behaviours.
A point to which a system tends to move, a goal, either deliberate or constrained by system parameters (laws). The three standard attractor types are fixed point, cyclic and strange (or chaotic).
Two species changing in response to changes in the other, a typical predator - prey interaction. This is usually regarded as a negative-sum interaction, improvements cancel each other out.
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.
A form of system that can act independently, e.g. a robot. Used in complexity to refer
to active (teleological) agents rather than passive ones, i.e. agents with internal goals that can act differently in identical external circumstances.
Self-production or self-maintenance. The ability to maintain a bounded form despite a flow of material occurring. A non-equilibrium system, typically life or similar processes but in a wider sense also including natural phenomena like Jupiter's Red Spot. See also sympoietic.
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.
The study of values and their types. These can be of four types, systemic, extrinsic, intrinsic and holarchic. Complex systems are of the latter two types.
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.
A combination of interconnected logic gates often used to model complex phenomena and
demonstrating the emergence of multiple attractors in simple systems.
The possibility that a large change can occur from a minor shift in initial conditions. A butterfly flapping its wings in the Amazon leading to changes in the location of a typhoon elsewhere in the world. Sensitivity to initial conditions, a chaotic system.
The restriction of state space exploration by constraints imposed upon the system either from outside or self-generated, i.e. unavailable possibilities. This helps to preserve stability or the 'status-quo' but may also prevent better optima from being reached.
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.
Simple agents that have a limited number of states, arranged in a grid formation. The state occupied is solely determined by the agent states together with those of their immediate surroundings. The cells in the 'Game of Life' are of this type.
A system whose long term behaviour is unpredictable, tiny changes in the accuracy of the starting value rapidly diverge to anywhere in its possible state space. There can however be a finite number of available states, so statistical prediction can still be useful.
The formation of closed loops of cause and effect within the system, such that it is not possible to abstract a linear chain of explanation in the conventional manner. A feature of all complex systems, which typically incorporate many such loops and exhibit multiple interconnected causes and effects.
A set of production rules used to match environmental data and suggest an action to be
taken, usually incorporates a genetic algorithm. Each rule covers part of state space.
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.
The idea that to survive agents must fight each other and that only one of them can
be successful. This assumes that resources are limited (insufficient for both) and is often a negative-sum strategy, i.e. 'win-lose' or 'lose-lose'.
The interaction of many parts, giving rise to difficulties in linear or reductionist analysis due to the nonlinearity of the inherent circular causation and feedback effects.
The study of the rules governing emergence, the constraints affecting self-organisation and general system dynamics in nonlinear adaptive interacting systems. The study of the collective behaviour of macroscopic collections of interacting units that are endowed with the potential to evolve in time.
One not describable by a single rule. Structure exists on many scales whose characteristics are not reducible to only one level of description. Systems that exhibit unexpected features not contained within their specification. Systems with multiple objectives.
The study of how critically interacting components self-organize to form potentially evolving structures exhibiting a hierarchy of emergent system properties.
The relation of an agent to its neighbours, it can be sparsely connected (only affected by a few neighbours), fully connected (interfacing with every other agent in the system) or some intermediate arrangement. This parameter critically affects the dynamics of the system.
A force of some sort restricting the movement of a system. See selection. In Life studies the variations of form do not allow infinite variation, something constrains the options available. Complexity studies seek the laws that apply, if any, in these cases and similar areas.
The idea that we construct our reality mentally rather than seeing directly an objective world.
This idea is validated by research in neuropsychology and relates also to general semantics.
The idea that two agents can increase both their fitnesses by mutual help rather than by
competition. This assumes that resources adequate for both exist, or are created by the interaction, and relates to synergy (synergic coevolution) and 'compositional evolution'.
Sexual mating between two genotypes in which a portion of the genes of one is joined to part of the genes of the other, to create a hybrid creature. This recombination allows rapid searching of the possible phase space.
The study of control or homeostasis within a system, typically using combinations of feedback loops. This can be within machines or living structures. First order cybernetics relates to closed systems, second order includes the observer perspective and third order looks to how these coevolve.
Non-continuous. A step by step (countable) approach. Digital systems operate this way, with time steps being the controlling factor. A sufficiently large number of such steps can approximate to any continuous (analogue) system.
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.
The idea that issues can always be divided into either/or states, e.g. mind/matter, fact/value, right/wrong. A throwback to pre-complexity viewpoints and earlier bivalent logic and systemic valuation, replaced mostly in complex systems approaches by non-dualist (continuum) modes of thought that take into account the wider connectivity issues and the need to balance multiple objectives.
The mathematical study of the behaviour through time of systems. This studies the attractor structure, bifurcation behaviour and phase portraits of the system.
The behaviour of a system in time. Changes with time are the essence of complexity, a static system is merely a snapshot within an evolutionary continuum, however interesting it may be in its own right.
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.
System properties that are not evident from those of the parts. A higher level phenomena, that cannot be reduced to that of the simpler constituents and needs new concepts to be introduced. This property is neither simply an aggregate one, nor epiphenomenal, but often exhibits 'downward causation'. Modelling emergent dynamical hierarchies is central to future complexity research.
The tendency of systems to lose energy and order and to settle to more homogenous (similar) states. Often referred to as 'Heat Death' or the 2nd Law of Thermodynamics.
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).
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.
The study of evolution based upon neo-Darwinian ideas. Modern complexity science adds additional self-organizational concepts to this theory to better explain organizational emergence.
A set of techniques, using ideas from natural selection, within computer science. Includes genetic algorithms, genetic programming, classifiers, evolutionary programming and evolutionary strategies.
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.
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.
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.
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'.
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.
The phenomenon of bird flocking can be explained by simple rules telling an agent to stay a fixed distance from a neighbour. The apparently intelligent behaviour of a flock navigating an obstacle follows directly from the mindless application of these rules.
The movement of resources from a place of high concentration to a low (e.g. energy goes from hot to cold). By utilising such flows systems can perform work (including self-organization). When flows in opposite directions balance, the system can arrive at the steady state (dynamic equilibrium) that characterises dissipative systems.
A System having similar detail at all scales, leading to intricate patterns and unexpected features. Fractal geometry explores systems with non-integer dimensions.
A way of dealing with uncertain information and variables that do not permit simple
yes/no categorisations (e.g. colour). Can also be used to make decisions where uncertainty occurs (fuzzy control). This is a form of non-Aristotelian logic (see general semantics).
The study of how the way we use language constrains our thought patterns. It especially emphasises the need to adopt a non-Aristotelian viewpoint if we are to escape the errors of dualism. This relates to the new paradigm thinking behind complexity science and stresses that our 'maps' of reality are not equal to the 'territory' but are always only restricted viewpoints. See constructivism also.
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.
The use of evolutionary techniques to diversify, combine and select options in order to improve performance, following the methods of natural selection by coding options as genes.
The combination of genes that make up an organism. This has no form itself but directs the creation of the phenotype following the interaction of system, dynamics and environment. Usually regarded as comprising a number of alleles or bits (systems having two states, 0 or 1, off or on).
A form of judgement that takes into account all the values present within all the entities that form the hypersystem, plus their interactions, a 'whole systems' valuation or fitness measurement of the multi-level whole.
Iterated Function System. A mathematical method of applying affine transformations to a seed to obtain a fractal image. Fractal compression works in reverse to derive an appropriate seed and transformation from the original image.
A loop that uses the current value of a system to derive its future value by re-inserting it into the equations controlling the system dynamics. Feedback. The linking of effect back to cause.
Lindenmayer systems allow simple rules to serve as a way of generating complex images by iteration. This can create extremely natural forms, flowers, trees etc.
A game invented by John Conway, it uses cellular automata to evolve lifelike patterns. It is also a universal computer and can in theory execute any program imaginable, given a large enough pattern.
The mapping of the behaviour of a specific complex formula across space by colour coding the result of each starting point as convergent or divergent, generating a fractal boundary.
A prefix used to denote a higher level of thought about the subject, e.g. metascience (where we consider how we approach science), meta-ethics where we consider how we define normative behaviour. Each level in a complex system can be considered as a meta-viewpoint upon the previous level of emergence. Relates to category or type theory and higher-order logic.
The random change of any part of the genotype, typically by reversing the state of one bit. Natural systems often mutate by the action of radiation, cosmic rays or carcinogenic agents.
The manufacture of systems of molecular size that emulate the behaviour of larger systems. Any alife system is potentially creatable in these dimensions, using standard biological or even inorganic components.
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.
The idea, from game theory, that agents combine in such a way that both lose or
that the total change is a reduction in overall fitness, sometimes called dysergy or 'lose-lose'. Related to competition, where if the interactions repeat then we have escalating trajectories of fitness losses.
A new form of logic that goes beyond fuzzy logic by adding an axis for indeterminacy and thus
takes into account not only what is measured but also what is not, a more whole systems or intrinsic logic better suited to complex systems.
A form of philosophy that emphasises paradox and the complementary and contextual nature of truth. This fits in with the idea of balance, emphasised within complex systems in the notion of 'edge-of-chaos'.
A peak in the ecological fitness landscape occupied by one variety of creature, often unopposed. Niches, in coevolutionary thought, are created by the organism interactions, do not exist in isolation and are a way of maximising group fitness by minimising competition (see synergy).
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'.
A situation in which it is possible for all participants to win or lose
simultaneously, so that the fitness scores may total to a positive or negative sum overall.
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.
The search for the global optimum, or best overall compromise within a (typically) multivalued system. Where interactions occur many optima are typically present (the fitness landscape is 'rugged') and this situation has no analytical solution, generally requiring adaptive solutions.
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.
All the possibilities available to the system in theory. The sum total of possible states the system can occupy. In complex systems only a very small proportion of such states are found - the system is said to occupy only a minute proportion of state or phase space.
A movement between static, ordered or chaotic states or back again. Usually used in connection with a change of state in physics from solid, to liquid, to gas or the reverse, but of general applicability in complexity theory.
The form of the organism. A result of the combined influences of the genotype and the environment on the self-organizing internal processes during development.
The idea, from game theory, that when agents interact they can both benefit, the whole being greater than the sum of the parts, also called synergy or 'win-win'. When the interactions repeat we have escalating trajectories of positive fitness effects.
A problem whereby a prisoner gets freedom by giving evidence against a fellow villain, but only if the fellow prisoner does not do the same. If both keep quiet a better overall result will obtain than either if both confess, or if just one confesses; yet for an individual the best result is still to confess. An example of a non-zero sum game, where cooperation pays both parties.
The chance of obtaining a particular result, e.g. if a 10 sided die is thrown it will be 10%. For complex problems there can be many outcomes, some of which do not seem to be ever realised, even if they appear to be equally probable.
A change taking place in time, such that an input is transformed to an output. This can be cyclic if the sequence of changes is such that the output recreates the input (such as autocatalysis).
The treatment of reality as the evolution of processes rather than the behaviour of objects. In this methodology we recognise that 'things' are simply standing waves (attractors) in a continuous dynamical process and have no inherent absolute properties.
A choice between available options based on consideration of fitness within the current environmental context. A bias on movement in state space. See evolution.
Ability to create structure without any external pressures, an emergent property of the system.
Related to extropy or negentropy. Internal constraints.
Systems that generate their form by a process of self-organisation, either wholly or in part.
Modelling a system by implementing in a computer some relevant features. If all features are operational then the system is real not a simulation. Alife is sometimes said to be real life under this definition, unlike say a model of a volcano which cannot melt the computer - a feature of real volcanic lava, which is not included in the model.
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.
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.
The use of geometric ideas within a systems view to describe and understand reality. Closely associated with Buckminster Fuller who applied it also to human behaviour.
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.
A collection of interacting parts that forms an integrated and consistent whole, isolatable from its surroundings. The concept of dynamics or change over time is central to our treatment of complex systems.
System Dynamics
The study of how systems actually behave, using models to simulate the assumptions and rules being followed. Often the behaviour seen is very different than the behaviour people expect.
A form of judgement that allows only two possibilities, good or bad (present or absent, in or out). This corresponds to Boolean operations (based upon Aristotelian logic).
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.
The path through state space taken by a system. It is the sequence of states or path plotted against time. Two general forms affect fitness, positive-sum and negative-sum.
An temporary attractor formed within the transient behaviour of a system. This is a state (like a glider in the Game of Life) that only persists for a short time before dissipating with new perturbations (e.g. a smoke ring). Most attractors in evolving complex systems are of this type, due to the presence of continual perturbations.
A form of universal computer, assumed to take its instructions from an infinite paper punched tape and output results to the same medium before stopping upon completion of the program.
A computer able to perform any task if suitable programmed. Most personal computers are of this type (at least for a small range of tasks). Any system with sufficient flexibility of interaction may perform this function, for example some automata or neural networks.
The dimensions or objectives we choose with which to measure the system and those variables we attempt to optimise in deriving fitness. Due to neural associations, the often imagined dualism between 'fact' and 'value' is invalid, thus values (purposes) can and should form a part of our scientific worldview.
The inclusion in our definition of 'system' of all the issues involved, including all the nested
levels of interconnected smaller systems and how they relate to each other and work dynamically as a whole.
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).