Cybernetics principles | Bhomf

Cybernetics principles

Human organism is complex, adaptive, self-regulating cybernetic system (kybernâo = to guide, sýstema = orderly distribution) introduced by Norbert Wiener (1948).

System components interact in parallel, cooperatively, and in real time, creating multiple simultaneous interactions. Maintenance of system balance is by adaptive and interchangeable homeostasis known as allostasis, which adheres to 1st and 2nd cybernetic principles of positive and negative feedback.

Human System is autopoietic (self-sustaining), self-referencing and selfregulating system, whose complex behavior is unpredicatable. The systems approach explains a small variation which can produce a major change. Variety and constraint are determinant of such behaviour. Variety (V) represents the freedom the system has in choosing a particular state, which creates the uncertainty about its state. Variety is number of elements in the state space S, as the logarithm to the basis two of that number: V = log2 (|S|). A variety of one bit, V=1, means that the system has two possible states. In the simplest case of n binary variables, V = log2(2n) = n, is therefore equal to the minimal number of independent dimensions.

Due to multifactorial interferences, Langton (1990) has proposed that complex systems emerge and maintain on the edge of chaos, the narrow domain between frozen constancy and chaotic turbulence. A living organism has the “astonishing gift of concentrating a ‘stream of order’ on itself and thus escaping the decay into atomic chaos” (Erwin Schrodinger). Fractal nature of patterns allows order with flexibility and adaptability to continual change of internal and external environment.

The actual variety of states is smaller then the system can potentially conceive, due to the factors of constraint. Constraint increases probability of the system response, such as temperature of protein denaturing, vascular wall compliance or mineral concentration.

These restraining factors prohibit certain combinations of values for the variables: C = Vmax – V. The quantity of variety and the change in the variety (positive change is correlative with increased variety and negative is decreased variety ) creates uncertainty about the outcome. In cases when uncertainty is relieved but the occurrence of one of the possibilities, then we gain information about the system. There many possible ways to measure the quantity of variety, uncertainty, or information.