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- Herausgeber: John Wiley & Sons
- Kategorie: Geisteswissenschaft
- Sprache: Englisch
"What is colour?", "What is the precise meaning of the statement 'the stock exchange closes at a 5% drop this evening'?", "How are TV viewers defined?", or "How can images produce meaning?" Such everyday questions are examined in this book. To make our analysis intuitive and understandable, numerous concrete examples illustrate our theoretical framework and concepts. The examples include gaming, fictional skits in leisure entertainment, and enigmas. The golden thread running through the text revisits the informational process and places the datum as its pivot. The epistemological perspective of our novel approach is that of "radical relativity". This is based on the precept that a perceptual trace carries with it the spectrum of the process that has engendered it. Given this, the informational tracking endeavour tracks the meaning-making process, notably through interpretive scaffoldings that leads to plausible realities.
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Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Introduction
1 The First Information Theories
1.1. Introduction
1.2. The mathematical theory of information by Shannon [SHA 48]
1.3. Kolmogorov’s algorithmic theory of information [KOL 65]
1.4. Delahaye’s further developments [DEL 94]
1.5. Final remarks
2 Understanding Shannon through Play
2.1. Introduction
2.2. The game of tele-balls
2.3. The teachings of the tele-ball game
2.4. The general diagram of communication/transmission
2.5. Conceptual confusion in the so-called information theories
2.6. Conclusion
3 “Tele” before Shannon
3.1. Introduction
3.2. The speaking African drums
3.3. The problems of long-distance communication
3.4. Conclusion
4 Some Revisions of the Concept of Information
4.1. Introduction
4.2. A double-faced concept: Capurro and Hjørland [CAP 03]
4.3. The Mathematical Theory of Information (MTI) as a starting point: Segal [SEG 03]
4.4. The Diaphoric Definition of Data (DDD): Floridi [FLO 05]
4.5. A pattern-oriented approach (POA): Bates [BAT 05]
4.6. Founding statements for a theory of information
4.7. Conclusion
5 Conceptualization and Representations
5.1. Introduction
5.2. Natural and artifactual devices for producing representations
5.3. Human conceptualization
5.4. About what “exists” in common thought, in natural language and in formal language
5.5. The resulting epistemological revolutions
5.6. Conclusion
6 From Captures to Data
6.1. Introduction
6.2. An illustrative sketch: a view of the human body
6.3. From the interactional bath to distinction
6.4. Diaphoric data and qualification?
6.5. Conclusion
7 From Data to Aggregates
7.1. Introduction
7.2. Data: raw material of the semantic chain
7.3. Aggregates: meaningful superstructures
7.4. Meaning: individual production or social construct?
7.5. Conclusion
8 Trace Deployment from Indexical Retention to Writing
8.1. Introduction
8.2. The trace as registered indexical retention
8.3. The search of the trace as evidence or proof
8.4. The trace as writing
8.5. Conclusion
9 Interpretive Scaffoldings in Context
9.1. Introduction
9.2. Information and trace
9.3. The horizon of expectation, by Hans Robert Jauss [JAU 78]
9.4. Relevance, according to Dan Sperber and Deirdre Wilson [SPE 89]
9.5. Weaving the horizon of expectation and the theory of relevance
9.6. Coalescence considered under the light of a horizon of relevance
9.7. Interpretive aggregate by means of example: visual sense-making
10 Realities under the Watch of Horizons of Relevance
10.1. Introduction
10.2. Back to the relation to the Real
10.3. Some examples
10.4. Conclusion: legalization of meaning in the age of Digital Humanities
Conclusion
Bibliography
Index
End User License Agreement
List of Tables
4 Some Revisions of the Concept of Information
Table 4.1. Four aspects of information according to Capurro & Hjørland [CAP 03]
Table 4.2. Three levels of application of DDD [FLO 05]
Table 4.3. From information to knowledge
5 Conceptualization and Representations
Table 5.1. Recapitulation of the five representational levels (M: machine, A: animal, H: human)
List of Illustrations
1 The First Information Theories
Figure 1.1. Relation between the amount of information of a message and the appearance probability of such a message
2 Understanding Shannon through Play
Figure 2.1. A bridged diagram of the transmitter–receiver model
Figure 2.2. Schematic diagram of a general communication system [SHA 48]
Figure 2.3. The linear pattern of communication [ESC 76]
Figure 2.4. Appearance of a media space at the top of the informational channel
3 “Tele” before Shannon
Figure 3.1. A Chappe telegraph station (tower and device)
Figure 3.2. Coding table for Chappe’s telegraph
Figure 3.3. Map of Chappe’s telegraph lines at their apogee in France
Figure 3.4. Coding table for Morse’s system
Figure 3.5. The alpha bravo coding table
5 Conceptualization and Representations
Figure 5.1. Distribution of the frequency spectrum. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 5.2. The two cubic spaces RGB and CMY for color coding. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 5.3. Spaces HSL and HSV for colors. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
6 From Captures to Data
Figure 6.1. Conceptualization process according to Mugur-Schächter [MUG 06]
7 From Data to Aggregates
Figure 7.1. Four different aspectual choices regarding the maps of France, among infinite possibilities. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 7.2. An initially heterogenous collection of objects
Figure 7.3. Categorization by shape
Figure 7.4. Categorization by texture
Figure 7.5. A meaningful aggregate
Figure 7.6. Interpretive scaffolding by coalescence. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 7.7. Diagram of the conceptual integration process
Figure 7.8. A face in the clouds, by means of pareidolia. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 7.9. Cut-out in meaningful aggregate zones. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 7.10. Dissociation of the aggregate in isolated zones. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 7.11. From the noumenon to aggregates via conceptualization. Summary. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
9 Interpretive Scaffoldings in Context
Figure 9.1. Appearance of the media space upstream of the information chain
Figure 9.2. A case of “neighboring” contextual influence
Figure 9.3. Personal pictures taken on December 30, 2016. Descending from Annecy to Geneve, from one side and the other of Mont-Sion tunnel. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 9.4. Annecy lake at dawn, personal picture. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 9.5. Duck or rabbit?
Figure 9.6. Emergence of the “duck’s head” aggregate
Figure 9.7. Emergence of the aggregate “rabbit’s head”
Figure 9.8. Entering or outgoing red or blue cubes? For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 9.9. Mathematical aberrations in the view with central cube entering For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 9.10. Mathematical aberrations in the view with outgoing central cube. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
Figure 9.11. Interpretation that does not respect the laws of perspective. For a color version of this figure, see www.iste.co.uk/leleu/tracking.zip
10 Realities under the Watch of Horizons of Relevance
Figure 10.1. Jeanne
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G1
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e1
Traces Set
coordinated bySylvie Leleu-Merviel
Volume 1
Informational Tracking
Sylvie Leleu-Merviel
First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2018
The rights of Sylvie Leleu-Merviel to be identified as the author of this work have been asserted by her in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2018932684
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-247-2
Introduction
Information officially entered the scientific world in 1948, thanks to the famous MTI, Mathematical Theory of Information, for which Shannon offered a measurement. As a matter of fact, it is remarkable that the first theory of information universally acknowledged as such appeared in a publication entitled A Mathematical Theory ofCommunication1 [SHA 48]. What might appear as a mere detail actually reinforces the idea of an inextricable weaving between information and communication, a concept embodied by the French discipline of Information and Communication Sciences, which finds no equivalent in the Anglo-Saxon structuring of knowledge fields.
At the heart of cybernetics and, a short time later, systemics, information quickly blossomed via the two concepts of feedback and homeostasis: these contributed to keeping the balance of complex systems and/or the possibility of controlling their dynamic evolution in order to satisfy a task set beforehand. Beyond its endless range of applications – from regulating the central heating system to controlling search-head missiles, not to mention the autopilot – (the most successful of which can be seen in space exploration). It was its high degree of applicability that favored the imposition of this theory, to the point that information was established by many authors as the third fundamental substance, after matter and energy.
Yet Shannon’s proposal was not devoid of flaws, as it was quickly pointed out by his detractors. In particular, Shannon assumed and recognized the absence of the semantic dimension. Various attempts to compensate for this lapse paved the way for an important amount of works devoted to broader views of information.
This book does not intend to draw up a list of the numerous conceptions of information in the sequence that they have successively appeared in since 1948. Other references already offer such a review: among others, we may quote, Le zéro et le un. Histoire de la notion scientifique d’information au XXe siècle by Jérôme Segal [SEG 03] for a historical analysis in French, and the Encyclopedia of Library and Information Sciences, 3rd Edition, in which the entry Information, written by Marcia J. Bates [BAT 10] which focuses on the Anglo-Saxon works of the field. The present proposal is more singular: it aspires to synthesize 20 years of personal research in the information sciences, from the accreditation to supervise research in November 1996 to the present day. These 20 years are a continuation of the 15 previous years devoted to automatics and systems analysis: the time needed to broaden a very systemic vision, in the mechanical sense of the term, in the direction of a human-centered approach. This book intends to function as a further milestone on this atypical journey, from engineering sciences towards social and human sciences.
To a large extent, this book constitutes a compendium of publications that have appeared over the years, and, that were redesigned to be updated and brought into coherence, while modeling a specific theoretical body. For this, some paradigms lay the essential foundations:
1) In this book, we will converge towards the idea that information is not a tangible thing, which we can see or touch here or there.
2) Besides, information does not exist as an entity by itself: it is impossible to provide a stable scientific definition that covers all the frequently accepted meanings of the term. As soon as we desire to grasp it firmly, it becomes slippery and escapes us like an oily soap between wet fingers.
3) A rapid evaluation of the first theories that were acknowledged as information theories reveals an important number of inaccuracies, abusive assimilations and confusions that seriously hamper their correct understanding.
4) Rigorously speaking, then, it is only the informational process that remains as an observable invariant capable of enduring a somewhat scientific approach.
5) While making a conceptual effort to identify and describe this process, the notion of trace becomes stained with a certain promiscuity.
6) In the same way that the trace carries within itself the specter of the process that engendered it, the quest for information ultimately constitutes a search for meaning.
In Chapter 1, the human and social sciences reader should not be afraid to come across some mathematical formulas. This entry in mathematical terrain is only of a short duration, and in the rest of the publication, there will be no other formal occurrences. A small moment of solitude that is quickly passed by, that we may swerve altogether, without any further consequences.
In the rest of the book, a game (Chapter 2), a recreational entertainment in the form of a fictional sketch (Chapter 6), puzzles (Chapter 7) and multiple examples help to illustrate the theoretical concepts and/or presentations, making the whole approach more intuitive and more digestible. “What is color?”, “What does it exactly mean that the stock market is closing down by 5% tonight?”, “How is the audience of a show defined?” or “How does an image make it possible to evoke meaning?”: the book answers these common or other famous questions. All in all, the aim is to revisit the informational process: therein, data constitutes the pivot.
1
Communication, not information, as is suggested by the highlighting in bold.
1The First Information Theories
1.1. Introduction
Information1 has been at the heart of scientific considerations for almost 70 years now. The importance given to information in our societies keeps growing, together with the versatility of the concept and the lack of rigor in its definition. From the texts of its forerunners to the most recent publications, all agree on this point. Jean-Louis Le Moigne [LE 73, p. 10] expresses it in the following words: “Information! Is there a more familiar, a more intuitive word? Is there even a more international term? Is it not central to every management conversation? At the heart of the act of Decision–making, do we not find the immediate answer: Information? Is it not the source of this mutation that human societies are currently experiencing – not without emotion – under the name of the computer revolution? And yet, is there not a more difficult, a more multifaceted, a more ambiguous word?”
Or Jean-Paul Delahaye [DEL 94, pp. 13–14]: “The word information is used in a variety of phrases and contexts. For example, we usually say things like: “The information contained in this book, the available information we have regarding a problem, the information encoded in the genome, the poor information that his long speech provided”. […] We sense that behind this word is hidden something complex, something changeable perhaps, something that, in any case, deserves our reflection. Thus, we are led to wonder: is it possible to make a general scientific theory of information? And if so, how to do it? It is not easy to answer seriously and it is very easy to answer badly, because some mathematical or physical theories already employ the term information, enunciate theorems and give the impression that the problem has been solved and that we can mathematically speak of information”.
The absence of scientific rigor in the characterization of the concept is absolute. We can clearly perceive this by taking a look at the definitions that appear in the dictionary:
– the action of informing, of giving information ACTION;
– the news, the information that is communicated about someone or something, STATE;
– the body of knowledge acquired regarding someone or something, A SET OF STATES;
– the actual contents of transmitted messages, CONTENT;
– a signal by which a system transmits a piece of knowledge, CONTAINER.
Unfortunately, there is no more rigor in the Sciences index of the term’s definition (reference: Petit Robert):
– an element or system capable of being transmitted by a signal or a combination of signals;
– what is transmitted, the object of knowledge or memory.
Let us observe that the word “system” frequently appears in most of the scientific definitions of the term.
The perspective of our work intends to be that of a scientific approach towards information. In this sense, we will only take into consideration the works devoted to:
– the modeling of the information processes;
– the study of the operational laws ruling the functioning of these processes;
– more or less formalized and more or less quantified proposals of abstract representations, associated with the corresponding phenomena.
We will purposefully exclude the principle of a study linked to a specific field of application or to a specific category of practices.
1.2. The mathematical theory of information by Shannon [SHA 48]
Research carried out by the pioneers in the field of “information theory” had a variety of destinies. There is no doubt that the works of Claude Elwood Shannon almost immediately found their application in the field of telecommunications: this contribution arrived at its own pace, particularly after the research conducted by other signal engineers from Bell Telephone Laboratories. Nowadays, these works constitute the core of the results unanimously recognized as scientific (in the Cartesian sense of the term, and even in its strictly mathematical sense) as regards a theory of information.
1.2.1.Beginnings of this theory
The starting point can be presented in almost naïve terms. The more improbable and uncertain an event, the more information concerning its advent will be significant: the amount of information in a message depends on the improbability of the event that the message informs us about.
To support this hypothesis, we will illustrate it with a simple intuitive example. Let us imagine that, for decades, all your family members have been without any news from uncle Sam, who left a long time ago to lead an adventurous life in unexplored territories. A message announcing uncle Sam’s arrival the next day contains a lot of information because, given his long absence and prolonged silence, the probability of a message announcing this specific event is extremely low; in fact, such an occurrence is close to zero. Besides, it is precisely due to the fact that the event itself is perceived as improbable that the probability of the message announcing it is feeble. Therefore, we can perceive that in this case there is confusion between the probability of the event taking place and the announcement of the event, a matter that we will discuss at further length. On the other hand, let us imagine that uncle Sam sent a letter that the post was unable to deliver promptly – which does sometimes happen. If this same message, by the same means, arrives one day after the reappearance of uncle Sam, it contains no more information, because the event has already taken place; so it is no longer improbable, it has become certain. As a matter of fact, there was a time when this kind of inconvenience was commonplace; for example, in Tristes Tropiques, Claude Lévi–Strauss observed: “Since the ‘official and urgent telegram’, sent from Lahore on the previous day in order to announce my arrival, reached the director only five days later, due to the floods that raged the Punjab, I might as well have come impromptu” [LÉV 55, p. 473]. Thus, we reckon that the informational estimate of the same message, in an identical form, can vary from one extreme to the other in a short time, depending on the probability of occurrence of the event to which the message makes reference.
Starting from this observation, Shannon’s theory establishes a biunivocal relation between the amount of I information and the probability of occurrence of a message or, more precisely, the number of N states that the expected message can eventually adopt.
This relation takes the following form: I = K.log N, K being constant.
being the appearance probability of one of the N possible states, these N states being equally likely, the above relation can also be expressed as:
Figure 1.1.Relation between the amount of information of a message and the appearance probability of such a message
This curve represents the relationship between the amount of information and the probability of occurrence of such a message.
1.2.2.Shannon’s generalization
The preceding definition of the measurement of the amount of information is filtered by the restrictive hypothesis of the equiprobability of possible states. One of Shannon’s key contributions lies precisely in the fact that he suggested such a generalization. In fact, all possible states no longer have the same probability of occurring, and then, when approaching the limit, the distribution of probabilities assumes a continuous form. By doing so, Shannon associates an amount of information with an amount of entropy.
In this way, we get a measure of the information of a message considered from the viewpoint of the appearance probabilities of the message, which assumes the very general form:
pi being the appearance probability of one of the n states: N1, N2, …, Ni, …, Nn.
Nevertheless, in its generality, this measure still has some limits, mainly associated with the notion of probability. In fact, who are these states likely or unlikely for? For an objective, statistical receiver or for a manager who often estimates a probability via a legitimately subjective judgment? The manager using this notion will often reach the limit. If he does so consciously, he will be right.
Therefore, we should always bear in mind that the distribution of probabilities assumes a continuous form when it approaches the limit.
1.2.3.Information and entropy
The theory of systems has given great importance to the notion of entropy, a measure for uncertainty, disorder, for diversity. We can briefly summarize the evidence of the negentropic equivalence of information as follows:
Given an initial situation, about which we know nothing (I0 = 0) and a priori characterized by N equally probable situations, I1 information (I1 > 0) makes it possible to reduce the number of equally probable alternatives from N0 to N1 (N1 < N0).
The evolution of the physical entropy of this system is measured by:
Hence, we have seen that the information about a situation is measured by:
with an adequate choice of units. Then:
And because I0 = 0:
If we define negentropy as the negative of physical entropy, we obtain the amount of information supplied to the system, which equals the corresponding increase in system negentropy.
1.3. Kolmogorov’s algorithmic theory of information [KOL 65]
A little less than twenty years later, appeared another theory of information, known as the “algorithmic theory of information” or “Kolmogorov’s theory of information” [KOL 65, LI 93]. It is sometimes introduced as a substitute for Shannon’s theory.
1.3.1.Succinct presentation
The initial idea is to define the complexity of an object according to the size of the smallest program capable of engendering it [CHA 77].
When Turing [TUR 36] established that certain mechanisms had a maximum calculating power, that is to say, as soon as the notion of a universal machine became available, the idea of using it as a universal measure of complexity naturally followed. This was almost simultaneously discovered by Minsky [MIN 62], Chaitin [CHA 66] and Kolmogorov [KOL 65], whereas it is to Solomonoff [SOL 64] that we owe the first technical formulation mentioning the notion of a universal Turing machine.
1.3.2.First algorithmic information theory
Kolmogorov’s notion of complexity is defined by the concept of information content of finite objects (for us, sequences of 0 and 1), itself formulated with the help of the notion of a universal Turing machine.
A universal Turing machine is a Turing machine capable of simulating all Turing machines. By definition, the information content of the s finite sequence, or Kolmogorov’s algorithmic complexity of s, written as K(s), corresponds to the size of the smallest program (for a universal Turing machine) capable of producing s.
We show that, by difference of an additive constant, this notion is independent of the universal Turing machine used in the definition. In other words, if KU(s) refers to the algorithmic complexity of s obtained by using the universal Turing machine U and KV(s) to the one obtained with V, there is a CUV constant, which solely depends on U and V, in such a way that for any s finite sequence:
This result receives the name of invariance theorem.
We immediately show that there exists a C’ constant, so that for every s finite sequence:
The idea this result proves is very simple: for every s sequence, there is a program producing s whose meaning is simply “to print s” and whose length is equal to the length of s plus what is needed so as to express the copy algorithm, which has a C’ length. This relation defines the size of the smallest program capable of producing a finite sequence.
1.3.3.Second algorithmic information theory
The initial expression of Kolmogorov’s information theory needed to be corrected in function of the expression of the end of the program.
In fact, the above definition is only possible in case the end of a program does not need to be indicated on the program itself. The end of the program is reached when all its digits have been read. The second theory of algorithmic complexity requires that the end of programs are specified on the programs themselves: we will then speak of self-delimiting programs. Nowadays, this is the case for virtually all computer programming languages.
The consequences of this requirement are:
– that no program can be extended to another one, and therefore, each program can be specifically weighed;
– that the preceding inequality becomes:
Indeed, the copy program must contain the information regarding the length of s, besides the description of s digit by digit.
In any case, the information content of a finite sequence is always expressed through the size of the smallest program capable of producing it.
1.4. Delahaye’s further developments [DEL 94]
Nevertheless, even if these information theories (respectively mathematical and algorithmic) are universally acknowledged and continue to be professed all over the world, they do not cover all the problems related to information.
1.4.1.Gaps
In his book Information, complexité et hasard – an essential work that we have already quoted and which constitutes the main source of the presentation above – Jean-Paul Delahaye is rather reticent concerning the merits of these multiple theories. “Neither Shannon’s theory, nor Kolmogorov’s theory, nor any other theory, states everything about what information is. What is more, while claiming to be the information theory, each of these prevents progress. Information must be conceived in general and diverse ways which are unlikely to be perused in haste and for which, until now, no mathematical theory provides the ultimate secret. Along the way, we will ponder the real interest of the theories mentioned” [DEL 94, p. 14].
Thus, the author suggests further extensions that address and go a step beyond the limitations he observed.
1.4.2.Information value
First, it is necessary to go beyond mere information contents and substitute this with the notion of information value.
Let us consider a sequence of S symbols, for example, a string of characters. The information content of the various objects of this nature has or had a certain variable value, at some point in the past. They were bought and sold accordingly; it was possible to invest in order to produce them or to continue spending large sums in order to keep them. Their assigned value at the exchange market was a testimony of their inherent value, in this case, expressed in a monetary equivalent. “A general theory of information which does not praise the informational value of one of these objects can only be incomplete or inadequate. An information theory which does not provide the means for comparing the informational content value of these objects, cannot claim to be recognized as the theory of information” [DEL 94, p. 16].
For this reason, Delahaye suggests considering information value as a complement to its sole “raw” contents.
1.4.3.Raw information content
The raw information content of each object can be understood as a weight estimate in a certain coding system. The corresponding measurement unit in a digital environment is the bit and its multiples (bytes, kilobytes, megabytes, gigabytes, terabytes, etc.). We can define this measure of raw content as the space that the stored object digitally occupies in the memory of a computer, when it is not subjected to any particular treatment other than the formatting process compatible with the computer’s operating system. Moreover, the identifier of any file recorded in a digital database is nowadays systematically accompanied by a weight measure – that is to say, its amount of information or raw content – expressed in bits.
“It is clear that the raw information content does not determine the value of the information. This is evident: the value of information is something more complicated and relative. And it is because the information value of a string is relative to a certain purpose and to a certain situation that there are several information theories and that not a single theory can really cover all the problems that the notion of information elicits” [DEL 94, p. 16].
Given a certain goal, that we will call goal B, this is written as Val(S,B), that is to say, the value of information contained in S regarding goal B.
In the particular case where we set the very specific goal of compressing the S character string as much as possible, assuming that we have an M machine for this, then the information value of S is the length of the smallest program available (expressed in binary terms), which enables it to reconstitute the S string when given to M. In this case, information value is the incompressible content of S (as regards M). Consequently, the notion of information content according to Kolmogorov corresponds to the general definition of information value Val(S, B) when goal B is to:
Thus, only the assignment of a goal makes it possible to measure the value relative to this goal. Evidently, there exists an infinity of purposes other than compressing for a universal machine. Bennett’s notion of logical depth [BEN 88] corresponds to the general definition of information value Val(S,B) when goal B is to:
This last value can be seen as a computational value. However, beyond the technical goals of compressing or calculating the S string itself, in everyday life a great number of goals are defined in a pragmatic way.
1.4.4.Pragmatic aspects related to information value
We still have to mention certain new limitations to the formulations that have been brought forward as information theories. “If we consider that the targeted aim is of a practical nature, for example, to survive in a certain environment or to make as much money as possible on a certain day at the Paris stock exchange, then the value of the information contained in a string of characters S will be measured according to the intention pursued. For example, precious information will be which place to attend to obtain certain types of food or the name of the stock market that must be bought because its price will increase. There is no general information theory that takes into account all the pragmatic aspects which determine the value of a string of characters” [DEL 94, p. 20].
Jean–Paul Delahaye concludes that: “The mathematical theories available or still in development which concern information claim a degree of universality and of applicability which we have to be wary of” [DEL 94, p. 27].
In the present state of scientific formalization, the assessment of the value of information – although more essential than measuring quantities or the raw contents of information – is still indefinite.
1.5. Final remarks
This section outlined the very first theories recognized as scientific approaches to information. The first theory, that of Shannon [SHA 48], is mathematical. The second one, by Kolmogorov [KOL 65], is algorithmic.
These theories share the feature that they disregard the semantic dimension of information in favor of a measurement of raw content or amounts of information. They do not contribute to estimating the value of this type of information. Regardless of their real interest, the two theories have often been misinterpreted, maybe due to misunderstanding, caricature, generalization or simplification beyond what is reasonable. In order to establish its fundamental notions and supporting paradigms, the following chapter will revisit Shannon’s theory, by revealing it in an intuitive and playful way so as to fully grasp its subtleties and nuances.
1
This chapter resumes, updates and enriches the ideas introduced in Chapter 5, pp. 69–83 of
La conceptionen communication. Méthodologie qualité
, published by Hermès Lavoisier in 1997 [LEL 97].
2Understanding Shannon through Playa
2.1. Introduction
Shannon’s theory was first praised and then criticized with equal vigor and perseverance, especially from the perspective of the Human and Social Sciences, which disregard mathematical formalization in terms of logarithmic functions. However, Shannon’s contribution is essential and deserves neither the oblivion nor the contempt that it has sometimes elicited. Rejection often derives from misunderstandings, in general due to excessive simplifications or impertinent shortcuts. What is more, despite its scope and incontestable popularity, the MTI should not be limited to the famous “Shannon’s formula”, introduced in the previous chapter. Like any scientific theory, it relies on a certain viewpoint that makes it coherent (we will define the notion of horizon of relevance later in the book). Neglecting this point of view has precisely been the source of all the erroneous approximations and interpretations that emerged as a result.
In order to rediscover Shannon’s theorem in all its original relevance, in a pragmatic way, but above all, with a playful and intuitive approach, I have borrowed a simple little game that Claude Baltz [BAL 09] previously suggested, and that can be useful as a stimulating material for workshops initiating all kinds of audiences, following an approach inspired by Main à la pâte or Savanturiers.
2.2. The game of tele-balls
The game of tele-balls is a variant of the “telephone” game, which was adapted so as to capture the point of view and the risks of digital communication. All of the interest of the game lies in the multiple trials and errors of which it is the object, which also stimulates the pleasure of learning by doing.
How is tele-balls played?
2.2.1.Layout and rules of the game
Tele-balls is played with six players. Understanding the imperative need for six players is precisely one of the game‘s contributions. On the other hand, it is important to explain to the players beforehand, that each of them will be entrusted a unique mission during the game. The purpose of this precaution is to unambiguously identify each of the functions necessary for the success of the challenge.
The players are divided into two groups of three. The groups are placed in two near spaces, contiguous but impeding direct communication of any kind, neither visual nor auditory nor of any other type. Considering the present circumstances and technological advances, players will certainly have to be deprived of all their remote communication devices, smartphones and other digital prostheses that could enable them to cheat. Nonetheless, both rooms are connected by a transparent tube which goes through the partition, and whose diameter is exactly adapted to make identical balls circulate inside of it.
The rule of the game is extremely simple: an event will take place in Room A and it is necessary to inform the players in Room B with only the means available.
2.2.2. Producing the source event
At the beginning of the game, an event takes place that functions as the initial impetus for the whole process. If there was no event, there would be nothing to transmit: although this is evident, it is so often forgotten! To begin with, the players in Room A are invited to produce an event.
It may be profitable to let the group reflect upon what constitutes or what makes an event, at least something they consider an event, to the point where they want to pass it on to subgroup B, who will ignore everything about it. Soon will appear the necessity that what group A considers an event here and now, is also deemed as such by the others, who are isolated on the other side of the wall. In this way, the notion of informational expectation and/or expectation horizon spontaneously emerges.
Without lingering on this reflection, we can make the group converge on a restrictive event device: for example, given a set of 32 cards, the event will be to randomly extract one of them. The infinite spectrum of potential events is suddenly and drastically reduced to one possibility among 32 pre-established equi-probable possibilities.
From this moment, the PRODUCER chooses a card among 32, takes it away from the game and shows it to his teammates: this is the source event. The goal is then communicated to the recipient group: it is a question of letting them know which card was drawn. By means of this uncertainty-generating choice, the PRODUCER creates the event (what justifies him being called the PRODUCER), of which he is charged of informing the RECIPIENT, located in the other room. Thus, two of the six players have been entrusted with a mission.
2.2.3.Channel and transmission
The second stage of the game is undoubtedly the longest and most arduous one. A card has been taken out from the pack: how to communicate this to the recipient? Knowing that we only have one tube and that balls can circulate (or not circulate) in this tube, it is necessary to find the means of “passing on the information”. We will now refer to this passage (this single tube that connects A and B) by its true name: TRANSMISSION, or more rigorously, TRANSMISSION CHANNEL.
We can quickly instruct the group for them to observe that only two types of actions can take place: either a ball passes (1) or it does not pass (0). Therefore, it is necessary to translate the designation of the drawn card into a sequence of 0 and 1 – a soft but very effective introduction to the binary code. Then, another serious need emerges: that of ENCODING – in the present case, we refer to the binary code. Later, we can tactfully make the group remark that a coding table is also available: beforehand, each of the 32 cards has been identified with a number, from 00000 to 11111 (in a later phase of the game, it will also be possible to analyze why five binary positions are necessary to encode 32 events, 32 obviously being equal to 25). Finally, it suffices to look up the number corresponding to the drawn card on the table, for example, 00111. It is the CODER who will be in charge of this. The 00111 sequence of binary positions concretizes the coding of the source event. From this moment on, it is no longer the event itself that will be emitted, but a series of signs referring to it.
2.2.4.Transmission
Then, the TRANSMITTER only has to inject the series: no ball, no ball, one ball, one ball, one ball. This series corresponds to the physical execution of the 00111 binary number. It is identified with the SIGNAL, which will be placed in the channel so that it carries the message to the other side of the wall. With the designation of the CODER and the TRANSMITTER, each player of the first group has been confided his mission.
This game is very pedagogical, because it has the advantage of the channel being visible, like the old pneumatic tubes of the very first message machines (those used by the Post office, for example). As a consequence, the very fact of visualizing the walls of the tube as well as the circulation of the ball signal in its interior makes it possible to grasp the general concept of transmission channel in very concrete terms and to transpose it to computer signals.
2.2.5.End of the process
If everything goes well, the RECEIVER will be able to recognize the 00111 signal. The DECODER, who must necessarily have the same conversion table as the CODER in room A, will translate the code into: “it is an ace of spades” (for example). Like the military, the RECEIVER will be able to say “Received 5 out of 5, the event is an ace of spades”. All the members of the game will then be able to compare the initial draw with the result at the end of the chain, and verify that the challenge was successfully met. The six members of the game will each play a unique role in the device, particularly emphasizing the distinction between coding/transmission, and reception/decoding.
In the end, the RECEIVER is free to decide what he is going to do with this “info”, in terms of the resulting action – for example, to play another shot in an asynchronous strategic game configuration. In order to give more empirical reality to the game of tele-balls, we can use 32 illustrated maps describing weather figures (rain, sun, wind, storm, heat wave and so on up to 32) or warrior actions, like the ones we find in abundance in today’s videogames.
2.3. The teachings of the tele-ball game
To say the least, the game of tele-balls makes it possible to understand that no communication is self-evident and that we can only achieve satisfactory results at the price of hard work.
Almost inadvertently, several fundamental needs, constraints or discoveries are revealed during the game of tele-balls. In fact, this game makes it possible to intuitively approach various aspects that the usual presentation of the Shannonian diagram neglects or completely passes over in silence.
2.3.1. The concept of “tele”
The first of these is precisely the concept of tele, as it is explicitly formulated in the name “tele-balls”. If the player is deprived of talking, it is certainly very simple to show the card drawn to the group. The communication diagram, as introduced by Shannon, is placed in a context of non-copresence. The difficulty to overcome is reporting the here and now of the initial event elsewhere and/or at another time. Indeed, Shannon mentioned this. The sentence that immediately precedes the famous phrase “Frequently the messages have meaning” is: “The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point”.
In Shannon’s perspective, the very foundation of communication is the absence of immediacy. It is precisely because the usual immediacy of copresence is broken by the rule of the game that we must invent one or more processes to overcome the space and/or time separating the PRODUCER from the RECEIVER.
