Sunday, July 24, 2005
complexity
A complex, nonlinear, interactive system which has the ability to adapt to a changing environment. Such systems are characterized by the potential for self-organization, existing in a nonequilibrium environment. CAS’s evolve by random mutation, self-organization, the transformation of their internal models of the environment, and natural selection. Examples include living organisms, the nervous system, the immune system, the economy, corporations, societies, and so on. In a CAS, semi-autonomous agents interact according to certain rules of interaction, evolving to maximize some measure like fitness. The agents are diverse in both form and capability and they adapt by changing their rules and, hence, behavior, as they gain experience. Complex, adaptive systems evolve historically, meaning their past or history, i.e., their experience, is added onto them and determines their future trajectory. Their adaptability can either be increased or decreased by the rules shaping their interaction. Moreover, unanticipated, emergent structures can play a determining role in the evolution of such systems, which is why such systems show a great deal of unpredictability. However, it is also the case that a CAS has the potential of a great deal of creativity that was not programmed-into them from the beginning. Considering an organization, e.g., a hospital, as a CAS shifts how change is enacted. For example, change can be understood as a kind of self-organization resulting from enhanced interconnectivity as well as connectivity to the environment, the cultivation of diversity of viewpoint of organizational members, and experimenting with alternative "rules" and structures.
See: Adaptation; Emergence; Genetic Algorithm; Self-organization
Bibliography: Dooley (1997); Gell-mann (1994); Holland (1995); Kauffman (1995)
In the theory of Darwinian Evolution, adaptation is the ongoing process by which an organism becomes "fit" to a changing environment. Adaptation occurs when modifications of an organism prove helpful to the continuation of the species in a changed environment. These modifications result from both random mutations and recombination of genetic material (e.g., by means of sexual reproduction). In general, through the mechanism of natural selection, those modifications that aid in the survival of species survival are maintained. However, insights from the study of complex, adaptive systems are suggesting that natural selection operates on systems which already contain a great deal of order simply as a result of self- organizing processes following the internal dynamics of a system (Kauffman’s "order for free"). A fundamental characteristic of complex, adaptive systems is their capacity to adapt by changing the rules of interaction among their component agents. In that way, adaptation consists of "learning" new rules through accumulating new experiences.
See: Complex, Adaptive Systems; Genetic Algorithm; N/K Model
Bibliography: Holland (1995); Kauffman (1995)
The arising of new, unexpected structures, patterns, or processes in a self-organizing system. These emergents can be understood as existing on a higher level than the lower level components from which the emergents emerged. Emergents seem to have a life of their own with their own rules, laws, and possibilities unlike the lower level components. The term was first used by the nineteenth century philosopher G.H.Lewes and came into greater currency in the scientific and philosophical movement known as Emergent Evolutionism in the 1920’s and 1930’s. In an important respect the work connected with the Santa Fe Institute and similar facilities represents a more powerful way of investigating emergent phenomena. In organizations, emergent phenomena are happening ubiquitously yet their significance can be downplayed by control mechanisms grounded in the officially sanctioned corporate hierarchy. One of the keys for leaders from complex systems theory is how to facilitate emergent structures and take advantage of the ones that occur spontaneously.
See: Self-organization
Bibliography: Cohen and Stewart (1994); Goldstein in Sulis and Combs (1996)
Images, representations, or thought schemes of how we perceive and cognize the world around us. We follow our mental models in getting about in the world, but can become trapped in limiting behaviors by being overly attached to certain mental models. That is why we need occasionally to be jogged out of the ruts of our dominant mental models by investigating new ways of looking at things. Complexity science has the promise of being a powerful tool to get us to look at our work and organizations in a new way, thereby changing our mental models of how to go about our business in the most effective manner.
See: Complex, Adaptive Systems; Internal Model
Bibliography: Senge (1990), Stacey (1996)
One of the defining characteristics of emergent patterns arising from self-organizing processes is their novelty or innovative character. Indeed, that is why such phenomena are termed "emergent" Ñ they introduce new qualities into the system that were not pre-existing in the system. An example are the novel nature of the "dissipative structures" that arise in nonlinear systems at far-from-equilibrium conditions. This novelty is neither expected, predictable, nor deducible from the pre-existing components. Moreover, this novelty is not reducible to the lower level components without loosing its essential characteristics. An issue, therefore, for practitioners working with complex systems, is to determine what system processes (i.e., "anacoluthian") are necessary for the emergence of novelty. That is, novel outcomes demand novel processes that prompt a system to the production of novel structures and practices.
See: Anacoluthian; Bifurcation; Emergence; Far-from-equilibrium; Self-organization
Bibliography: Goldstein in Sulis and Combs (1996); Kauffman and Macready (1995); Van de Ven & GarudThe management/ complexity theorist Ralph Stacey’s term for the set of informal relationships or networks among people in an organization which exists in tandem with the official and "legitimate" network or hierarchy. The shadow organization is not focussed on the same stabilizing objective as the official organization, so it is a ripe ground for the instability required for self-organization and the emergence of more adaptable organizational structures and processes. Effective leaders take into consideration both the mainstream and the shadow systems, even capitalizing, according to Stacey on the potential friction between them.
See: Edge of Chaos; Far-from-equilibrium
Bibliography: Stacey (1996)
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.
See: Cellular Automata; Complex, Adaptive System; Self-organization
Bibliography: Kelly (1994)
