Sung, List,

>From a mathematical point of view, an “entropy” or “uncertainty” measure is 
>simply a measure on distributions that achieves its maximum when the 
>distribution is uniform. It is thus a measure of dispersion or uniformity.

Measures like these can be applied to distributions that arise in any given 
domain of phenomena, in which case they have various specialized meanings and 
implications.

When it comes to applications in communication and inquiry, the information of 
a sign or message is measured by its power to reduce uncertainty. 

The following essay may be useful to some listers:

http://intersci.ss.uci.edu/wiki/index.php/Semiotic_Information

Regards,

Jon

http://inquiryintoinquiry.com

> On Apr 4, 2015, at 1:22 AM, Sungchul Ji <s...@rci.rutgers.edu> wrote:
> 
> Jerry, Steven, John, Bob, lists,
> 
> I want to thank Jerry for bringing to my attention Miller's impressive book, 
> "Living Systems" [1], which I thought I had "thumbed through" once but did 
> not: I simply conflated it with another book.    
> Miller's book is the first biology book that I have seen so far that provides 
> an extensive discussion on the meaning of "information", which I have 
> collected below as Items (1) through (17). I agree with most of these items 
> except a few. 
> 
> 
> (A)  In Item (9), Miller indicates that Schroedinger coined the term 
> "negentropy" but it was Brillouin who coined the word as an abbreviation of 
> Schroedinger's expression "negative entropy".  As I pointed out in [2], the 
> concept of "negative entropy" violates the Third Law of Thermodynamics (but 
> that of "negative entropy change" does not).  
> 
> (B)  In Item (9), Miller assumes that the negentropy is the same as 
> information, which assumption being referred to as the Negentropy Principle 
> of Information (NPI) by Brillouin (1951, 1953, 1956).  I refuted NPI in [2], 
> in agreement with Wicken [3] based on the  thought experiment called the 
> "Bible test", which was designed to demonstrate the fundamental difference 
> between Shannon entropy, S_S (also called informational entropy, S_I) and 
> thermodynamic entropy, S_T:  The S_T of the Bible increases with temperature 
> but S_I does not.  See Item (28). Wicken's argument against NPI is summarized 
> in Items (18) through (27) extracted from [3]. 
> 
> (C) In Item (10), Miller assumes that the Second Law of Thermodynamics stated 
> as "a system tends to increase in entropy over time" applies to all physical 
> systems but it does not: It applies only to isolated systems which cannot 
> exchange any energy or matter with their environment and not to closed (only 
> energy can be exchanged; e.g., a refrigerator) or open systems (both energy 
> and matter can be exchanged; e.g., living cells). For example, the 
> thermodynamic entropy content of a living cell can decrease while its Shannon 
> entropy can increase when growing and differentiating. 
> 
> (D)  My conclusion would be that it is impossible to define the relation 
> between information and thermodynamic entropy without knowing whether the 
> thermodynamic system under consideration is open, closed or isolated.  In 
> other words, the relation between Shannon entropy (also called information) 
> and thermodynamic entropy depends on the nature of thermodynamic system 
> involved.
> 
> (E)  As I indicated in my previous email of today, I believe that 
> "information" is an irreducibly triadic relation (as shown in Figure 1 below) 
> but "entropy" is a pat of the "free energy" that drives semiosis in which 
> "information" is processed.  Free energy is a function of both energy and 
> thermodynamic entropy.  In other words, "information" is the whole system of 
> 3 nodes and 3 arrows whereas entropy is a part of the "energy" that drives 
> the processes indicated by the 3 arrows.
> 
> 
>   
>                      f                                 g
> VARIETY ------------>  MESSAGE -------------> FUNCTION
>      |                                                                  ^
>      |                                                                  |
>      |______________________________________| 
>                                      h
> 
> f = selection
> g = action
> h = information flow
> 
> Figure 1.  An "irreducibly triadic definition of information", an instance of 
> the irreducibly triadic semiosis and the sign. 
> 
> 
> 
> 
> (1) The technical sense of  "information (H) . . . . is not the same thing as 
> meaning or quite the same as information as we usually understand it." [1, p. 
> 11].
> 
> (2) "Meaning is the significance of information to a system which possesses 
> it: it constitutes a change in that system's processes elicited by the 
> information, often resulting from associates made to it on previous 
> experience with it."  [1, p. 11].
> 
> (3)  Information is ". . . .the degree of freedom that exists in a given 
> situation to choose among signals, symbols, messages, or patterns to be 
> transmitted." [1, p. 11].
> 
> (4) The amount of information is measured as the logarithm to the base 2 of 
> the number of alternative patterns, forms, organizations, or messages."  [1, 
> p. 11].
> 
> (5) The unit of information, bit, ". . .is the amount of information which 
> relieves the uncertainty when the outcomes of a situation with two equally 
> likely alternatives is known." [1, p. 11].
> 
> (6)  "The term marker was used by von Neumann to refer to those observable 
> bundles, units, or changes of matter-energy whose patterning bears or conveys 
> the informational symbols from the ensemble or repertoire.  These might be 
> the stones of Hammurabi's day which bore cuneiform writings, parchments, 
> writing paper, Indian,s smoke signals, a door key with notches, punched 
> cards, paper or magnetic tape, a computer's magnetized ferrite core memory, 
> an arrangement of nucleotides in a DNA molecule, the molecular structure of a 
> hormone,  pulses on a telegraph wire, or waves emanating from  a radio 
> station.  If a marker can assume n different states of which only one is 
> present at any given time, it can represent at most log_2(n) bits of 
> information."  [1, p. 11].
> 
> (7)  "Communication of almost every sort requires that the marker move in 
> space, from the transmitting system to the receiving system, and this 
> movement follows the same physical laws as the movement of nay other sort of 
> matter-energy." [1, pp. 11].
> 
> (8)  "The disorder, disorganization, lack of patterning, or randomness of 
> organization of a system is known as its entropy (S).  This is the amount of 
> progress of a system from improbable to probable states.  The unit in which 
> it is measured empirically is ergs or joules per degree absolute." [1, p. 13].
> 
> (9) "It was noted by Wiener and by Shannon that the statistical measure for 
> the negative of entropy is the same as that for information, which 
> Schroedinger has called negentropy."  [1, p. 13].
> 
> (10) "Since, according to the second law, a system tends to increase in 
> entropy over time, it must tend to decrease in negentropy or information." 
> [1, p. 13].
> 
> (11)  "There is therefore no principle of the conservation of information as 
> there are principles of the conservation of matter and energy." [1, p. 13].
> 
> (12)  "The total information can be decreased in any system without 
> increasing it elsewhere, but it cannot be increased without decreasing it 
> elsewhere."  [1, p. 13].
> 
> (13)   "Making one or more copies of a given information pattern does not 
> increase information overall, though it may increase the information
>  in the system which receives the copied information."  [1, p. 13].
> 
> (14)  "Matter-energy and information always flow together.  Information is 
> always borne on a marker." [1, p. 13]
> 
> 
> (15)  "Conversely there is no regular movement in a system unless there is a 
> difference in potential between two points, which is negative entropy or 
> information."  [1, p. 13]
> 
> (16)  "Which aspect of the transmission is most important depends upon how it 
> is handled by the receiver.  If the receiver responds primarily to the 
> material energetic aspect, I shall call it, for brevity, a matter-energy 
> transmission; it the response  is primarily to the information, I shall call 
> it an information transmission.  For example, the banana eaten by a monkey is 
> a nonradndom arrangement of specific molecules, and thus has its 
> informational aspect, but its use to the monkey is chiefly to increase the 
> energy available to him." [1, p. 13].
> 
> (17)  Referring to Table 2-1 in his book, James Miller states "It indicates 
> that there are several pairs of antonyms used in the section, one member of 
> which is associated with the concept of information (H) and the other member 
> of which is associated with its negative, entropy (S)." [1, p. 13].
> 
> (18) "The concept of entropy has had a long and interesting history, 
> beginning with its implicit introduction by Carnot to its explicit 
> formalization as a state function by Clausius to its statistical treatment by 
> Boltzmann and Gibbs to its application to communications theory by Shannon 
> (Shannon and Weaver 1949). The latter achievement has seemed to several 
> scientists a true generalization of the entropy conception, its freeing from 
> the particular disciplinary framework of thermodynamics for application to 
> probability distributions generally (Gatlin 1972; Yockey 1977). This mistaken 
> belief is a major impediment to productive communication between 
> thermodynamics and information theory, and we will examine it carefully." [3, 
> p. 177].
> 
> (19)  "entropy is defined to express constraints on the directions natural 
> processes can take, according to the equation dS = dQ/T where dS is the 
> differential change in entropy resulting from an infinitesimal flow of heat 
> dQ at temperature T. In any spontaneous or irreversible process, entropy 
> always increases. Any concept claiming to be a generalization of classical 
> entropy would have to share this essential property." [3, p. 177].
> 
> (20) "Boltzmann gave thermodynamics its first, rough statistical 
> interpretation, introducing the microstate-macrostate distinction on which 
> the explanation of irreversibility hangs. Boltzmann used as a model a gas 
> with N particles and having a total kinetic energy of E (see Brush 1983). 
> This energy was divided into J discrete pieces and assigned to these N 
> particles in all possible combinations. Each combination constituted one of W 
> equiprobable microstates of the system. Given these assumptions, Boltzmann 
> was able to define his entropy function by the equation 
> 
> H = k ln W or 
> 
> H = -k ln P 
> 
> where k is Boltzmann's constant and P = 1/W." [3, p. 177].
> 
> (21)  "Natural, entropy producing processes are those that increase the 
> microscopic possibilities of the system-environment supersystem. A 
> nonequilibrium system can therefore be regarded as compressed in probability 
> space  Wicken 1981), accessing only a small fraction of the microstates 
> available to it. Irreversible processes are expansions in probability space, 
> from macrostates having relatively few microscopic complexions to macrostates 
> having relatively many such complexions." [3, p. 178].
> 
> (22) "Microstates are only equiprobable if they have the same energy. For 
> systems that are open to energy exchanges with their environments, this is 
> not usually the case. A more general formula for the entropy of a system, 
> derived from Gibbs' systematic development of statistical thermodynamics, is 
> 
> H = - k E Pi ln Pi 
> 
> where Pi refers to the energy-dependent probabilities of the various i 
> microstates. Under isolated conditions, or under conditions where kinetic 
> barriers to reaction keep the system in a single, nonequilibrium macrostate, 
> this reduces to Boltzmann's equation." [3, 178].
> 
> (23)  "The Shannon equation is 
> 
> H = - K E Pi log2 Pi, 
> 
> where K is generally taken as unity. Since proportionality constants and 
> logarithm bases are more matters of convenience and scaling than of 
> substance, the relationships among the variables in the two equations are 
> identical. Gibbs circumspectly referred to his statistical formulations as 
> "entropy analogues" rather than "entropies" (Denbigh 1982). The question is 
> then whether the Shannon equation generalizes the entropy analogues of 
> statistical mechanics." [3, 178].
> 
> (24)  "The answer to this question depends on whether Shannon entropies have 
> properties consistent with thermodynamic entropies. Issues relevant to this 
> question are: (a) Do both entropies behave the same way? and (b) are they 
> both based on the same kinds of probabilistic assumptions? Neither is the 
> case." [2, 178].
> 
> (25)  "The starting point for the Shannon entropy is an alphabet of symbols 
> which have the capacity to convey information because they can be transmitted 
> in alternative sequences. The entire ensemble of possible sequences can be 
> abstractly assigned an "entropy", which measures the uncertainty connected 
> with knowing a priori the sequence of elements in any given message. Shannon 
> initially suggests (Shannon and Weaver 1949, p. 49) that information is 
> carved from that entropic space. This much can at least be borne in a spirit 
> of suspended judgment. But he slips immediately (p. 50) to assigning 
> entropies to the symbols and messages themselves. Here he permanently parts 
> company with statistical entropy." [2, p. 180-181].
> 
> (26)  "What allows us to assign a thermodynamic system an entropy is that any 
> measurable macrostate in which it resides can be expressed in a variety of 
> alternative microstates. Since these are all accessible by the system, there 
> is an essential uncertainty in knowing its microstate at any instant. I would 
> concur with Denbigh (1982) that there is nothing "subjective" about this 
> uncertainty, that it belongs to the macrostate by virtue of its ensemble of 
> microstates. A message, in contrast, cannot possess entropy. It is what has 
> been said, a fait accompli." [3, p. 181].
> 
> (27)  "To appreciate the importance of restricting entropy to thermodynamic 
> applications -- or, more broadly to applications in which a 
> macrostate-microstate relationship obtains, one need only reflect on Weaver's 
> remarks about the Shannon formulation making contact with a universal law. It 
> does no such thing. Yet, as long as the term "entropy" buttresses the Shannon 
> formula, the second law remains a steady source of justification for ideas 
> that must find their own grounds of support. If there is a single generalized 
> entropy concept manifoldly expressing itself, one might expect all 
> exemplifications of it to have the property of increasing in time " [3, p. 
> 187].
> 
> (28)  "When one heats up a book such as the Bible, the thermodynamic entropy 
> associated with molecular motions of the paper constituting the pages of the 
> Bible will increase but the informational entropy associated with the 
> arrangement of letters in the Bible will not be altered until the temperature 
> increases high enough to burn the Bible.  This thought experiment may be 
> conveniently referred to as the Bible test." [4, Footnote c on p. 100]. 
> 
> 
>  All the best.
> 
> Sung
> 
> -- 
> Sungchul Ji, Ph.D.
> 
> Associate Professor of Pharmacology and Toxicology
> Department of Pharmacology and Toxicology
> Ernest Mario School of Pharmacy
> Rutgers University
> Piscataway, N.J. 08855
> 732-445-4701
> 
> www.conformon.net
> 
> 
> 
> References:
>    [1]  Miller, J. G. (1978).  Living Systems,  McGraw-Hill Book Company, New 
> York.
>    [2] Ji, S. (2012). The Third Law of Thermodynamics and “Schroedinger’s 
> Paradox”.  In:Molecular Theory of the Living Cell: Concepts, Molecular 
> Mechanisms, and Biomedical Applications.  Springer, New York.  pp. 12-15.   
> PDF available at http://www.conformon.net under Publications > Book Chapters.
>    [3]  Wicken, J. S. (1985).  Entropy and Information: Suggestions for 
> Common Language.  Phil. Sci. 54: 176-193.
>    [4]  Ji, S. (2012).  The Information-Entropy Relations.  In:Molecular 
> Theory of the Living Cell: Concepts, Molecular Mechanisms, and Biomedical 
> Applications.  Springer, New York.  pp. 97-101.  
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