A type of evolving computer program developed by the computer scientist John Holland whose strategy of arriving at solutions is based on principles taken from genetics. Basically, the genetic algorithm uses the mixing of genetic information in sexual reproduction, random mutations, and natural selection at arriving at solutions. In an analogous manner to the way a genetic algorithm learns better solutions through the mixing of patterns and an openness to random or chance events, a complex, adaptive system can adapt to a changing environment through a mixing of previous internal models of their environment. Thus, genetic algorithms can provide insight into the creative process of problem solving or decision making.

See: Complex, Adaptive System; Randomness

Bibliography: Eoyang & Olson (2001); Holland (1994).

A concept developed by complexity researchers David Lane and Robert Maxfield. They define a human relationship as generative if it “produces new sources of value that cannot be forseen in advance.” Their contention is that organizations, in times of turbulence and change, need to foster multiple generative relationships, within and outside the organization, as a means of discovering new strategies and directions. fostering such relationships as well as preconditions for their success, they suggest, is a key responsibility of leaders.

See: Edge of Chaos; Emergence; Genetic Algorithm; Self-Organization
Bibliography: Lane/ Maxfield (1996).

The mathematical theory that studies the properties of networks or webs of connections. A graph consists of edges (linkages) connecting nodes (what’s connected). Examples of networks studied by graph theory include the internet, the economy, and genetic landscapes. Although graph theory is a purely mathematical discipline, the term is being included here because it is providing a theoretical foundation for the very influential and growing field of social network theory. The latter is providing rich insights into the dynamics of complex systems in general. For example, social network theory using graph theory has been discovering the complex structures of the internet, communities, employees connected within and outside their work organizations and so forth.

See: Scale-free Network; Small World Network

Bibliography: Kilduff & Tsai (2003); Newman, Barabasi, Watts (2006); Trudeau (1993); Watts (1999).

Originally, information in the technical senses referred to the bits of a message, as opposed to “noise,” in a communication channel (formulated in Information Theory by the mathematician Claude Shannon building on earlier work done by Harry Nyquist and Ralph Hartley). Information has come to mean the bits of data that are the elements that are processed by the computer as information processor. “Noise” has a disorganizing effect in its way of disrupting redundant patterns so that novelty can come about in the emergent structures resulting from self-organizing processes. In terms of organizations, information is the cognate in social systems of what energy is in a physical system. According to Gregory Bateson, information is “a difference that makes a difference.” In terms of social systems this refers to the differences among group members’ perspectives on what is going on in the system. Information is not mere data: it is data that is meaningful to organizational members. An organization that is low in the flow of information is one in equilibrium or tending to maintain its status quo; whereas, an organization that is high in informational flow is in a far-from-equilibrium state in which dramatic changes can take place. Recent years have seen the birth of a new field entitled quantum information science which is playing an important role in the development of quantum computers and so-called quantum teleportation, both relying on the strange nature of quantum entanglement. These new fields reveal that information is increasingly seen as a basic constituent of the world around us or as the renowned physicist John Wheeler once put it, “It comes from bit!”

See: Redundancy

Bibliography: Goldstein (1994); Darling (2005).

The state of a system corresponding to the beginning of a period of observing or measuring it. The initial conditions are what is assessed at any particular time, and to which one can compare any later observation, measurement, or assessment of the system as it evolves over time. For example, chaotic systems demonstrate sensitive dependence on initial conditions, meaning that the nonlinearity strongly amplifies slight differences in initial conditions, thereby rendering impossible the predictability of later states of the system.

See: Chaos; Sensitive Dependence on Initial Conditions

Bibliography: Lorenz (1993); Ott (2003).

The condition of a system when it is more easily disturbed by internal or external forces or events, in contrast to a stable system that will return to its previous condition when disturbed. A pencil resting vertically on its eraser or a coin resting on its edge are examples of systems that have the property of instability because they easily fall over at the slightest breeze or movement of the surface they are resting on. An unstable system is one whose attractors can change, thus, instability is a characteristic of a system near or at bifurcation (or far-from-equilibrium).

See: Bifurcation; Equilibrium: Far-from-equilibrium

Bibliography: Nicolis (1989)

In complex, adaptive systems theory, a system functions according to its internal representation or model of its environment. This internal model is encoded in a set of internal mechanisms or processes (for example, memory structures). For a system to adapt to a changing environment, the internal models must have a means for changing as well. Thus, one of the most important functions of “change agents” in a business or institution is to expedite reconsiderations of an organization’s internal model of its environment.

See: Complex, Adaptive Systems
Bibliography: Gell-Mann (1994); Holland (1995)