| 1. | Thermodynamics.
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| 2. | (in data transmission and information theory) a measure of the loss of information in a transmitted signal or message. |
| 3. | (in cosmology) a hypothetical tendency for the universe to attain a state of maximum homogeneity in which all matter is at a uniform temperature (heat death). |
| 4. | a doctrine of inevitable social decline and degeneration. |
A measure of the disorder of any system, or of the unavailability of its heat energy for work. One way of stating the second law of thermodynamics — the principle that heat will not flow from a cold to a hot object spontaneously — is to say that the entropy of an isolated system can, at best, remain the same and will increase for most systems. Thus, the overall disorder of an isolated system must increase.
Note: Entropy is often used loosely to refer to the breakdown or disorganization of any system: “The committee meeting did nothing but increase the entropy.”
Note: In the nineteenth century, a popular scientific notion suggested that entropy was gradually increasing, and therefore the universe was running down and eventually all motion would cease. When people realized that this would not happen for billions of years, if it happened at all, concern about this notion generally disappeared.
entropy en·tro·py (ěn'trə-pē)
n.
For a closed thermodynamic system, a quantitative measure of the amount of thermal energy not available to do work.
A measure of the disorder or randomness in a closed system.
entropy theory
A measure of the disorder of a system. Systems tend to go from a state of order (low entropy) to a state of maximum disorder (high entropy).
The entropy of a system is related to the amount of information it contains. A highly ordered system can be described using fewer bits of information than a disordered one. For example, a string containing one million "0"s can be described using run-length encoding as [("0", 1000000)] whereas a string of random symbols (e.g. bits, or characters) will be much harder, if not impossible, to compress in this way.
Shannon's formula gives the entropy H(M) of a message M in bits:
H(M) = -log2 p(M)
Where p(M) is the probability of message M.
(1998-11-23)