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| Far-from-equilibrium:
The term used by the Prigogine School for those conditions leading to self- organization and the emergence of dissipative structures. Far-from-equilibrium conditions move the system away from its equilibrium state, activating the nonlinearity inherent in the system. Far-from-equilibrium conditions are another way of talking about the changes in the values of parameters leading-up to a bifurcation and the emergence of new attractor(s) in a dynamical system. Furthermore, to some extent, far-from-equilibrium conditions are similar to "edge of chaos" in cellular automata and random boolean networks. See: Difference Questioning; Equilibrium; Purpose Contrasting; Self-organization Bibliography: Goldstein (1994); Nicolis in Davies (1989); Prigogine and Stengers (1984) The mutually reciprocal effect of one system or subsystem on another. Negative feedback is when two subsystems act to dampen the output of the other. For example, the relation of predators and prey can be described by a negative feedback loop since the more predators there are leads to a decline in the population of prey, but when prey decrease too much so does the population of predators since they don’t have enough food. Positive feedback means that two subsystems are amplifying each other’s outputs, e.g., the screech heard in a public address system when the mike is too close to the speaker. The microphone amplifies the sound from speaker which in turn amplifies the signal from the microphone and around and around. Feedback is a way of talking about the nonlinear interaction among the elements or components in a system and can be modeled by nonlinear differential or difference equations as well as by the activity of cells in a cellular automata array. The idea of feedback forms the basis of System Dynamics, a way of diagramming the flow of work in an organization founded by Jay Forrester and made popular by Peter Senge. See: Interactive, Nonlinear Bibliography: Eoyang (1997) A "graphical" way to measure and explore the adaptive (fitness) value of different configurations of some elements in a system. Each configuration and its neighbor configurations (i.e., slight modifications of it) are graphed as lower or higher peaks on a landscape-like surface, i.e., high fitness is portrayed as mountainous-like peaks, and low fitness is depicted as lower peaks or valleys Such a display provides an indication of the degree to which various combinations add or detract from the system s survivability or sustainability. The use of fitness landscapes in understanding the behavior of complex, adaptive systems has been pioneered by Stuart Kauffman in his study of random boolean networks. An important implication from studying fitness landscapes is that there may be many local peaks or "okay" solutions instead of one, perfect, optimal solution. Thinking in terms of fitness landscapes can point to foolish adaptation, i.e., a downward trend on the slopes of the peaks. Moreover, studies of N/K models using fitness landscapes demonstrates that there is a decreasing rate of finding fitter adaptable configurations as one travels uphill on a fitness landscape. The use of fitness landscapes can be applied to gain insight into various organizational issues including which innovative organizational designs, processes, or strategies promise greater potential. See: N/K Model; Random Boolean Networks Bibliography: Kauffman (1995); Kauffman and Macready (1995); Maguire (1997). 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. A noninteger measure of the irregularity or complexity of a system. Knowing the fractal dimension helps one determine the degree of irregularity and pinpoint the number of variables that are key to determining the dynamics of the system. See: Chaos; Correlation Dimension; Scale Bibliography: Peak and Frame (1994) |
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