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Heinz von Foerster argued that humanity's abilities to construct societies, civilizations and technologies do not result in self-inhibition. Rather, societies' success varies directly with population size. Von Foerster found that this model fits some 25 data points from the birth of Jesus to 1958, with only 7% of the variance left
Heinz von Foerster (né von Förster; November 13, 1911 – October 2, 2002) was an Austrian-American scientist combining physics and philosophy, and widely attributed as the originator of second-order cybernetics.
The McKendrick–von Foerster equation is a linear first-order partial differential equation encountered in several areas of mathematical biology – for example, demography [1] and cell proliferation modeling; it is applied when age structure is an important feature in the mathematical model. [2]
Second-order cybernetics is closely identified with Heinz von Foerster and the work of the Biological Computer Laboratory (BCL) at the University of Illinois Urbana–Champaign. Foerster attributes the origin of second-order cybernetics to the attempts by cyberneticians to construct a model of the mind:
The foundations of cybernetics were then developed through a series of transdisciplinary conferences funded by the Josiah Macy, Jr. Foundation, between 1946 and 1953. The conferences were chaired by McCulloch and had participants included Ross Ashby, Gregory Bateson, Heinz von Foerster, Margaret Mead, John von Neumann, and Norbert Wiener.
Heinz von Foerster proposed Redundancy, R=1 − H/H max, where H is entropy. [60] [61] In essence this states that unused potential communication bandwidth is a measure of self-organization. In the 1970s Stafford Beer considered self-organization necessary for autonomy in persisting and living systems. He applied his viable system model to
The cybernetician Heinz von Foerster formulated the principle of "order from noise" in 1960. [3] [4] It notes that self-organization is facilitated by random perturbations ("noise") that let the system explore a variety of states in its state space. This increases the chance that the system will arrive into the basin of a "strong" or "deep ...
The dynamics of the model are determined by the Markov process, which in this case, expresses the probability of each possible state in the system upon time in a form of differential equations. The equations are difficult to solve analytically, so simulations on the computer are performed as kinetic Monte Carlo schemes.