From: Michel Petitjean <ptitjean@itodys.jussieu.fr>

Date: Mon 05 Apr 2004 - 09:14:30 CEST

Date: Mon 05 Apr 2004 - 09:14:30 CEST

2004 FIS session introductory text.

Dear FISers,

I would like to thank Pedro Marijuan for his kind invitation

to chair the 2004 FIS session. The session is focussed on

"Entropy and Information". It is vast, that I am afraid

to be able only to evoke some general aspects, discarding

specific technical developments.

Entropy and Information: two polymorphic concepts.

Although these two concepts are undoubtly related, they have

different stories.

Let us consider first the Information concept.

There was many discussions in the FIS list about the meaning

of Information. Clearly, there are several definitions.

The information concept that most people have in mind is

outside the scope of this text: is it born with Computer Sciences,

or is it born with Press, or does it exist since a so long time

that nobody could date it? Neglecting the definitions from

the dictionnaries (for each language and culture), I would say

that anybody has his own concept. Philosophers and historians

have to look. The content of the FIS archives suggests that

the field is vast.

Now let us look to scientific definitions. Those arising from

mathematics are rigorous, but have different meanings. An example

is the information concept emerging from information theory (Hartley,

Wiener, Shannon, Renyi,...). This concept, which arises from probability

theory, has little connections with the Fisher information, which

arises also from probability theory. The same word is used, but two

rigorous concepts are defined. One is mostly related to coding theory,

and the other is related to estimation theory. One deals mainly

with non numerical finite discrete distributions, and the other

is based on statistics from samples of parametrized family of

distributions. Even within the framework of information theory,

there are several definitions of information (e.g. see the last

chapter of Renyi's book on Probability Theory). This situation

arises often in mathematics: e.g., there are several concepts of

"distance", and, despite the basic axioms they all satisfy, nobody

would say that they have the same meaning, even when they are defined

on a common space.

Then, mathematical tools are potential (and sometimes demonstrated)

simplified models for physical phenomenons. On the other hand,

scientists may publish various definitions of information for physical

situations. It does not mean that any of these definitions should be

confused between themselves and confused with the mathematical ones.

In many papers, the authors insist on the analogies between their

own concepts and those previously published by other authors: this

attitude may convince the reviewers of the manuscript that the work

has interest, but contribute to the general confusion, particularly

when the confusing terms are recorded in the bibliographic databases.

Searching in databases with the keyword "information" would lead to

a considerable number of hits: nobody would try it without constraining

the search with other terms (did some of you tried?).

We consider now the Entropy concepts. The two main ones are the

informational entropy and the thermodynamical entropy. The first

one has non ambiguous relations with information (in the sense of

information theory), since both are defined within the framework of

a common theory. Let us look now to the thermodynamical entropy,

which was defined by Rudolf Clausius in 1865.

It is a physical concept, usually introduced from the Carnot Cycle.

The existence of entropy is postulated, and it is a state function

of the system. Usual variables are temperature and pressure. Entropy

calculations are sometimes made discarding the implicit assumptions

done for an idealized Carnot Cycle. Here come difficulties. E.g., the

whole universe is sometimes considered as a system for which the

the entropy is assumed to have sense. Does the equilibrium of

such a system has sense? Does thermodynamical state functions

make sense here? And what about "the" temperature? These latter

variable, even when viewed as a function of coordinates and/or

time, has sense only for a restricted number of situations.

These difficulties appear for many other systems. At other scales,

they may appear for microscopic systems, and for macroscopic

systems unrelated to thermochemistry.

In fact, what is often implicitly postulated is that the

thermodynamical entropy theory could work outside thermodynamics.

Statistical mechanics creates a bridge between microscopic and

macroscopic models, as evidenced from the work of Boltzmann.

These two models are different. One is a mathematical model

for an idealized physical situation (punctual balls, elastic

collisions, distribution of states, etc..), and the other is

a simplified physical model, working upon a restricted number

of conditions. The expression of the entropy, calculated via

statistical mechanics methods, is formally similar to the

informational entropy. This latter has appeared many decades after

the former. Thus, the pioneers of information theory (Shannon,

von Neumann) who retain the term "entropy", are undoubtly responsible

of the historical link between <<Entropy>> and <<Information>>

(e.g. see http://www.bartleby.com/64/C004/024.html).

Although "entropy" is a well known term in information theory, and used

coherently with the term "information" in this area, the situation

is different in science. I do not know what is "information" in

theermodynamics (does anybody know?). However, "chemical information"

is a well known area of chemistry, which covers many topics, including

data mining in chemical data bases. In fact, chemical information

was reognized as a major field when the ACS decided in 1975 to rename

one of its journals "Journal of Chemical Information and Computer Sciences":

it was previously named the "Journal of Chemical Documentation".

There are little papers in this journal which are connected with

entropy (thermodunamical of informational). An example is the

1996 paper of Shu-Kun Lin, relating entropy with similarity and

symmetry. Similarity is itself a major area in chemical information,

but I consider that the main area of chemical information is related

to chemical databases, such that the chemical information is represented

by the nodes and edges graph associated to a structural formula.

Actually, mathematical tools able to work on this kind of chemical

information are lacking, particulary for statistics (did anyone

performed statistics on graphs?).

In 1999, the links between information sciences and entropy were again

recognized, when Shu-Kun lin created the open access journal "Entropy":

<<An International and Interdisciplinary Journal of Entropy and

Information Studies>>. Although most pluridisciplinary journals

are at the intersection of two areas, Shu-Kun Lin is a pionneer

in the field of transdisciplinarity, permitting the publication

in a single journal of works related to entropy and/or information

theory, originating from mathematics, physics, chemistry, biology,

economy, and philosophy.

The concept of information exists in other sciences for which the

term entropy is used. Bioinformation is a major concept in bioinformatics,

for which I am not specialist. Thus I hope that Pedro Marijuan would like

to help us to understand what are the links between bioinformation and

entropy. Entropy and information are known from economists and philosophers.

I also hope they add their voice to those of scientists and mathematicians,

to enlight our discussions during the session.

Now I would like to draw some provocative conclusions. Analogies between

concepts or between formal expressions of quantities are useful

for the spirit, for the quality of the papers, and sometimes they

are used by modellers to demonstrate why their work merit funds

(does anybody never do that?). The number of new concepts in

sciences (includes mathematics, economy, humanities, and so on)

is increasing, and new terms are picked in our natural language:

the task of the teachers becomes harder and harder. Entropy

and Information are like the "fourth dimension", one century ago:

they offer in common the ability to provide exciting topics

to discuss. Unfortunately, Entropy and Information are much more

difficult to handle.

Michel Petitjean Email: petitjean@itodys.jussieu.fr

Editor-in-Chief of Entropy entropy@mdpi.org

ITODYS (CNRS, UMR 7086) ptitjean@ccr.jussieu.fr

1 rue Guy de la Brosse Phone: +33 (0)1 44 27 48 57

75005 Paris, France. FAX : +33 (0)1 44 27 68 14

http://www.mdpi.net http://www.mdpi.org

http://petitjeanmichel.free.fr/itoweb.petitjean.html

http://petitjeanmichel.free.fr/itoweb.petitjean.freeware.html

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Received on Mon Apr 5 09:22:34 2004

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