NEGENTROPY

In 1943 Erwin Schrödinger used the concept of '“negative entropy”' in his popular-science book ''What is life?''. Later, Léon Brillouin shortened the expression to a single word, 'negentropy.' Schrödinger introduced the concept when explaining that a living system exports entropy in order to maintain its own entropy at a low level (see entropy and life). By using the term ''negentropy,'' he could express this fact in a more "positive" way: a living system imports negentropy and stores it.
In a note to ''What is Life?'' Schrödinger explains his usage of this term.
In 1974, Albert Szent-Györgyi proposed replacing the term negentropy with ''syntropy,'' a term which may have originated in the 1940s with the Italian mathematician Luigi Fantappiè, who attempted to construct a unified theory of the biological and physical worlds. (This attempt has not gained renown or borne great fruit.) Buckminster Fuller attempted to popularize this usage, though ''negentropy'' still remains common.

Contents

Information theory

Organization theory

Notes

See also

Information theory

In information theory, "negentropy" is used as a measure of distance to normality. Consider a with a certain distribution. If the signal is Gaussian, the signal is said to have a normal distribution. Negentropy is always positive, is invariant by any linear invertible change of coordinates, and vanishes iff the signal is Gaussian.
Negentropy is defined as
:$J(p\_x)\; =\; S(phi\_x)\; -\; S(p\_x),$
where $S(phi\_x)$ stands for the Gaussian density with the same mean and variance as $p\_x$ and
$S(p\_x)$ is the differential entropy:
:$S(p\_x)\; =\; -\; int\; p\_x(u)\; log\; p\_x(u)\; du$
Negentropy is used in statistics and signal processing. It is related to network entropy, which is used in Independent Component Analysis.^[1].
Negentropy can be understood intuitively as the information that can be saved when representing $p\_x$ in an efficient way; if $p\_x$ where a random variable (with Gaussian distribution) with the same mean and variance, would need the maximum length of data to be represented, even in the most efficient way. Since $p\_x$ is less random, then something about it is know beforehand, it contain less unknown information, and need less length of data to be represented in an efficient way.

Organization theory

In risk management, negentropy is the force that seeks to achieve effective organizational behavior and lead to a steady predictable state.^[2]

Notes

1. P. Comon, ''Independent Component Analysis - a new concept?'', Signal Processing, 36:287-314, 1994.
2. Pedagogical Risk and Governmentality: Shantytowns in Argentina in the 21st Century (see p. 4).

See also

★ Exformation