ESSAY ON A SYSTEMIC THEORY OF MEANING

Christophe Menant (crmenant[at]free.fr)

- Last update: July 2003 -

 

Presentations based on this paper:
- Foundations of Information Sciences -April 2002- http://crmenant.free.fr/FIScience/Index.htm
- Gathering in Biosemiotics 2 -May 2002 -
http://crmenant.free.fr/Tartu/Index.htm
- 5th European Science System Congress - Oct 2002-
http://www.afscet.asso.fr/resSystemica/index.html
- Publication in Entropy 2003, 5, 193-204 - http://www.mdpi.org/entropy/papers/e5020193.pdf
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ABSTRACT. Meaningful information is present everywhere in our environment, as well as within ourselves (language, signs, symbols, thoughts, ...). Theories have been proposed concerning meaning associated to language or to signs. But little has been done to address meaning as a concept, independently of the information carrying it.
We try here to fill this gap by proposing a theory of meaning that can take into account all cases where a meaning is present. The approach is systemic in order to cover the different cases involved.
This paper is the first chapter that introduces and defines basic elements with corresponding properties: meaningful information, meaning generator system, domain of efficiency of a meaning, relation with kowledge. Elementary notions of information and signal processing are used. Different cases of transmission of meaning are presented.
We plan in the next chapters to apply the basic elements introduced here to different cases of meaning that can be present in matter, life, human or machines.

KEYWORDS: Meaning, information, system, constraint, generation, efficiency, knowledge, Peirce, Shannon, Sharov, Varela, Maturana.

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TABLE OF CONTENT

Introduction


Presentation


Chapter I
Information and Meaning. Meaning Generator System. Efficiency of Meaning.

I.1 Information and Meaning. Definition of Meaning. Meaning Generator System .
I.2 Signal and Information. Definitions.

I.3
Transmission and Reception of Meaningful Information. Domain of Efficiency of a Meaning.
I.4 Information, Meaning, Knowledge.
I.5 Summary of the chapter and continuation.
Notes

Chapter II (under preparation)
Different levels of complexity of reality (matter, life, mankind, machines)

Chapter III (under preparation)
Meaningful information depending upon level of complexity


Chapter IV (under preparation)
Meaningful information in machines

Chapter V (under preparation)
Overall summary and possible additional studies


INTRODUCTION

Information and meanings are important part of the world surrounding us, as well as part of ourselves. It is quite obvious to consider that there are some relations between information and meaning.

When looking at newspapers, we pay attention to the articles that have some meaning versus our subjects of interests. These meanings may be different from the ones that have motivated the authors of the articles.
Depending if we are on the beach or under shelter, noise from thunderstorm will generate different meanings for us.
When we talk with other persons, the information we provide is often aimed at bringing others to share the meaning we attach to the information.
A frog has a visual system able to see object the size of an insect or worm, providing it moves like one. Visual information of food that is not moving has no meaning for a frog.
A hive colony will extract information on food location from a bee dance because this food has meaning relatively to the survival of the hive.
On the same token, it is generally agreed upon that information processing machine do not take into account the meaning attached to the information they process. Because the meaning related to the information comes from the user of the machine or from the designer. And it cannot be transferred to the machine.

These few examples show that questions relative to the nature and the content of meaning attached to information can come up in many circumstances of our everyday life. Be they relative to ourselves or to our environment.

Important work has been done by philosophers and scientists on questions relative to the meaning of words, sentences or signs.
On the side of the philosophers, Charles Sanders Peirce has elaborated at the end of the 19th century a theory of sign with categories involving meaning and reperesentation [1].
During the 20th century, several theories of meaning have been developed. They recognize that meaning does depend on us humans. And it is admitted that the term "meaning" is most of the time considered as a property of language [2].
Regarding the activity of scientists, Claude E. Shannon, while establishing the mathemathical foundations of communication theory, has elaborated in the 1940's a way to measure the content of information in a message. But the meaning of the information was not taken into account, as irrelevant to the engineering problem. A few years after, Donald MacKay proposed to look to the meaning of information as related to a target oriented communication [3].
More recently, A. Sharov has analyzed the problem of the sense of information with the help of Biosemiotics [4].
A lot has also been written in the field of psychology about meaningful information as feeding our motivations and our beliefs.

These studies are devoted to rather well defined types of information or systems managing information and meaning.
But not much has been done regarding the notion of meaning by itself, whatever the information associated to it or the system managing it.
It is this aspect of relations linking information and meaning we are interested in.
We believe there can be some common ground for most types of meanings, and that this common ground can be explicited by defining a basic system managing the information and the associated meaning.


PRESENTATION:

Information and meaning cover a huge amount of subjects in our environment as well as within ourselves.
In order to find some common ground for all those types of different elements, we need a tools that can provide a generic representation of different items. The systemic approach is a tool that allows this type of representation.
Indeed, a system is defined as an ensemble of elements linked by an ensemble of relations. This definition is general, and leaves open the nature of elements constituting the system. We will use the notion of system to define and characterize on a general basis the relations between information and meaning. This systemic approach will allow us to introduce the definitions and properties that characterize a Meaning Generator System without any prejudgement on the nature of the elements constituting the system, and so cover most of the cases of meaning associated to information. This will make available the basis of a theory of meaning that should allow us to interpret and explain in a new way a number of facts observed in the domain of matter, life, human and machines.

This essay is organized in five chapters, and present paper covers the first chapter:

I) In the first chapter, we introduce the basic elements of a systemic theory of meaning. (definitions and properties).
We first analyze the definition of the word "meaning" and evidence it's ambiguity as resulting of its usage centered on human. We propose to overcome this ambiguity by building up a definition for a meaningful information as based on the behavior of a simple living element processing information for the satisfaction of a survival constraint. This definition is formulated by presenting a meaningful information as generated by a system submitted to a constraint. We introduce the notion of Meaning Generator System (MGS) and the cases of transmission or reception of meaningful information. The notion of domain of efficiency of a meaning is correspondingly defined.
In this first chapter, we deal only with the systemic presentation of meaning generation, without applying the proposed modelization to specific cases. Corresponding applications will be developped in the following chapters.

II) As we are willing to analyse the nature and the manifestations of this "meaning" in the fields of matter, life, human and machines, we will have to characterize these four domains with their corresponding borders. It will be the content of the second chapter.
These four domains have taken place one after the other during the course of evolution of the universe. We will analyze the chaining of these different periods in order to make available a path on which it will be possible to work with our definition of meaning.

III) The third chapter will be about the application of the systemic modelization of meaning generation to the three first domains of evolution we are interested with (matter, life, mankind).

IV) In the fourth chapter, we will look for possible extensions of our concept of meaning to creations of mankind. We will analyze the possible connections between meaningful information and machines.

V) The last chapter will offer a summing up of what has been established as a systemic theory of meaning in the previous chapters. Additional studies will also be proposed.


CHAPTER I:

Information and Meaning. Meaning Generator System. Efficiency of Meaning.

The purpose of this essay being to build up a systemic theory of meaning, we need to begin by clear enough an understanding of the field covered by the notion of meaning. And this field is immense, as meaning can be linked to any type of information. Information carried by physical or chemical signals (noise, light, odour, presence of an object), or nervous information regulating the functioning of a living organism, or also psychic information populating our mental life (thoughts, ideas, reasoning, fears,...) [5].

This first chapter introduces and defines the basic elements supporting our systemic approach of meaning.
W
e begin by analyzing the content of the word "meaning" and underline its ambiguity as resulting from its centering on human. A definition of "meaning" that is cleared of these ambiguities is proposed by positioning the notion of meaning in the context of information processed by a system submitted to a constraint. A simple animal far enough of the performances of human is taken as an example: a paramecium submitted to a survival constraint.
This brings us to formalize the notion of meaning generation by introducing a Meaning Generator System (MGS). Some elements related to signal and information processing are used in order to address the case of meaningful information transmission and to introduce the notion of efficiency of an meaning.
This first chapter is limited to the introduction of a systemic approach of meaning, with definition and properties of basic elements.
Application to various levels of complexity (matter, life, human, machines) will take place in the following chapters with specific developments using the MGS.

 

I.1 lnformation and Meaning. Definition of Meaning. Meaning Generator System

The word "meaning" can be used in many different ways. Let's begin by analyzing what is generally put under this word.
When looking at dictionaries, it appears that the word "meaning" is heavily centered on human. The point is that definitions in dictionaries link the word "meaning" to a performance of human mind. In " Le Vocabulaire technique et critique de la philosophie" (A. Lalande), one can find for the word "meaning": "Function of signs. What a sign represents, sense of a word, of a sentence, etc." The same dictionary gives for the word "sense": "what "means to say", what communicates to the mind a word, a sentence or any other sign playing a similar role".
So, in it's common acceptance, the notion of meaning is linked to information as processed by human. And this link to mankind introduces well the complexity of the subject of meaning. We all know the difficulties we encounter about the simple fact of trying to correctly understand each other when we communicate. When an information is exchanged between two persons, several questions can arise about the meaning related to this information. Should it be considered that the meaning is defined by the person producing the information or by the one receiving it, or by both ? or by the context in which the information has been produced ? or even by the history of the subject addressed in the information ? Is the content of the meaning explicit or implicit ? should we consider several levels of meaning, and which ones ? relatively to what and for whom ? how can be formalized a possible difference of meaning that will be acceptable for the transmitter of the information as well as for the receiver ? etc. These few questions indicate that the analysis of the notion of meaning is complex, and this up to the point where it becomes rapidly unworkable.

What could be done in order to clarify and simplify this subject ?
First of all, we feel it is important to state again that the notion of meaning is centered on the performances of human. And we want to consider that this link to human is the source of the complexity of the subject.
More precisely, we think that the linkage of the notion of meaning to the performances of human attaches implicitly the understanding of meaning to the understanding of human mind.
In fact, most information processed by human involve human mind (intelligence, reason, emotions, thoughts, common sense, ...). So the notion of meaning is linked to the characteristics and performances of human mind. And this linkage, even if it is implicit, has important consequences on the analysis of the notion of meaning. This for the simple reason that we make the notion of meaning imprecise and indefinite by associating it to something we do not clearly understand. Because we do not really know what human mind is.

Human mind is something which is mysterious. At current level of the development of science, the nature of mind is still to be discoverd. Studies on mind are however numerous. Their main characteristic being their diversity (philosophy, neurology, artificial intelligence, psychology, science of knowledge, cognisciences, ...). The results achieved so far by these many fields of research are alas far from delivering an acceptable understanding of the nature of mind. The nature of mind is currently out the field of scientific knowledge. Consequently, we can say that the current linking of the notion of meaning to the nature of human imposes to the notion of meaning a lot of elements that are today out of the field of scientific knowledge.

So one of the reasons making difficult the study of the notion of meaning is that we closely relate it to the domain of human mind which is unknown.
If we want to proceed ahead with the hope of reaching some understanding of meaning on a general basis, we must look at the possibility of modelizing it in a domain less complex and better known than the domain of human.

In this context, we feel that the domain of non human living elements looks interesting as a starting point for the study of the notion of meaning. And this for several reasons.
First because the nature of living is better understood by science than is the nature of human. Biology is a science whose base ground is relatively firm. Psychology does not benefit of such firm bases, as lacking an understanding of the nature of mind. An analysis of the notion of meaning in the framework of life will be rooted in a better known domain.
Also, considering man as the result of evolution of life, we want to beleive that some elements to be introduced for the understanding of the notion of meaning at the level of life will be usable for the analysis of the notion of meaning at the level of human. And correspondingly make available new tools that could find some usage in the study the nature of mind.

We are going in this chapter to disconnect the notion of meaning from human and come down the ladder of complexity by linking the notion of meaning to information as processed by non human living elements. As the nature of life is better understood by science than the nature of human, we can legitimately hope that such an analysis of the notion of meaning will provide more factual elements, more acceptable on a scientific base.
A simple and well chosen example of behavior of a non human living element should allow us to introduce the notion of generation of meaningful information by a systemic approach.

But disconnecting "meaning" from human at this stage should not be looked at as puting "meaning" apart from human in our approach. Such a disconnection is temporary. It is done because meaning in human is too complex to be taken as a base on which a modelization of meaning generation can be built directly. Once this modelization established using simpler grounds, we will work on applying it to human and analyse how the meaning generation system can be used in such a case (chp III).

The choice of the living element that will be used to analyse the generation of meaning is important. Indeed, some animals display characteristics that are close to the behavior of human, and the simplification we are looking for may be an illusion if our choice is too close to human (we may introduce some characteristics of human mind that we precisely want to avoid). This would invalidate our reference based on well understood living elements. As an example, some pets can display and probably live emotive states that we cannot explain without reference to some components of human mind.

Consequently, in order to use a simple and non ambiguous case for the analysis of connections between information and meaning in the context of non human living being, we must choose a living creature that is far enough from human in terms of performances. In order to satisfy this criteria, we choose a unicellular animal, the paramecium. A paramecium is a unicellular animal that is able to move in water. The simplicity of this organism makes us sure about its limited performances. Paramecium has no mind to which could be communicated "a word, a sentence or any other sign playing a similar role". The characteristics of the paramecium we are going to take into account are the ones shared with all living organisms: satisfy it's vital constraints. Which means the ability to subsist as an individual in the surroundings, and subsist as a species (participate to the reproduction of the species members).

We are going to take as a starting point some behavior of this very simple animal in order to introduce a definition of meaning that will be generalized to a system. To this end, we choose a behavior of the paramecium that can be understood as a processing of information generating some meaning.
Many experiences have been implemented with paramecia. For instance, it has been shown experimentally that a drop of acid in the water at the vicinity of a paramecium makes it move away, looking for a location in water where there is less acid.

This reaction of the paramecium can be understood by noticing that acid water is a hostile environment for paramecia. This reaction of a paramecium moving away from a hostile environment allows us to introduce the notion of meaning for a non human living entity. The acid environment represents for the paramecium an information that participates to some generation of meaning within the paramecium. A meaning that "has sense of", that "wants to say": "the environment is becoming hostile versus the satisfaction of vital constraints". And this meaning is going to trigger in the paramecium an action aimed at putting it at distance from the acid environment. It is clear that the paramecium does not possess an information processing system that allows it to have access to an inner language. But it is true also that a paramecium has usage of sensors that can participate to some measurement of the environment acidity. The information made available with the help of these sensors is part of the process that generates the move of the paramecium in the direction of less acid water.

So we can say that the paramecium has created a meaning related to the acidity of it's environment, in connection with the satisfaction of its vital constraints.
This example of a paramecium reacting to an environment that impacts the satisfaction of it's vital constraints gives a picture of what we consider as being a meaning associated to an information. And this in the case of information processed by a non human living entity.

More precisely, this example brings up several characteristics relative to the notion of meaning we are trying to conceptualize.
Let's formulate these characteristics in order to bring up a "systemic aspect", more general than the living element that we have taken as example. These characteristics are five in number and
are explicited hereunder (the characteristics relative to the living element taken as example are in parenthesis).

1) A meaning (the environment is becoming hostile versus the satisfaction of vital constraints) is associated with an information (level of acidity in water) which is incident on a system capable of processing the information (the paramecium).

2) Meaning is generated because the information processing system possesses a constraint linked to its nature (vital constraint that is to be satisfied in order to maintain a living nature).

3) A meaning is generated because the incident information has a connection with the constraint of the system (too much acid in the water impacts the satisfaction of the vital constraint of the paramecium). The content of the meaning is this connection.

4) A meaning is a meaningful information relatively to the constraint of the system (information meaning that the environment becomes hostile versus the satisfaction of the vital constraints).

5) The meaningful information is going to participate to the determination of an action that the system is to implement (move towards a less acid location) in order to satisfy it's constraint (satisfy it's vital constraint).

These five characteristics bring us to propose a definition of "meaning" in the framework of a relation between an incident information and an information processing system submitted to a constraint.

A meaning is a meaningful information that is created by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is formed of the connection existing between the received information and the constraint of the system. The function of the meaningful information is to participate to the determination of an action that will be implemented in order to satisfy the constraint of the system.

Figure I.1 summarizes the relations that have been introduced between information processing system, incident information, constraint of the system, meaningful information creation, action determination.

In the following text, we will use indifferently the expressions "meaning" or "meaningful information"

The above definition of meaning calls for the following precision and complements:
- The meaningful information is created by the system in the purpose of having it's constraint satisfied. It is the existence of the constraint to be satisfied that is the cause of the creation of the meaningful information. This places the notion of constraint satisfaction at the base of meaning generation in our approach.
- The meaningful information is the information generated by the system submitted to a constraint. It is not the incident information, which is only a contributor to the meaningful information generation.
Information that has not been produced by a system submitted to a constraint is meaningless.
- The content of the meaningful information is the
connection existing between the received information and the constraint of the system. If there is no connection between the received information and the constraint, there is no creation of meaningful information.
- A given system can present several connections between it's constraint and a received information. The system will then generate several meanings as answers to the incident information.
- A meaningful information is meaningful relatively to the constraint of the system tha has generated it, and only relatively to that constraint. Calling S the constraint of the system, we will write "meaningful (S) information" or "meaning (S)" the meaning relative to the constraint S. (if several connections exist between the constraint and the received information, we can list the meanings: S1, S2, .. ).
- Some systems can have more than one constraint and/or receive several incident information at the same time. Analysis of meaningful information generation need to be done for each couple (received information, constraint).
- The participation of the meaningful information to the determination of the action can be indirect. Several operations can be combined or chained before the formal action in relation with the satisfaction of the constraint be implemented.
- The action that will be implemented to satisfy the constraint can be internal to the system or be external and involve other systems. (In our example with the paramecium, the action is internal as the paramecium is going to move some elements of it's body to displace itself).
- An external action can take place on an element outside the system (the paramecium can displace a small element obstructing its movement), or take place via another system in connection with the first system (see paragraph 3 on meaningful information transmission).
- To have identity of meanings, there need to be identity of received information, identity of constraints and identity of connections between received information and constraints.
- The system creating the meaningful information can be part of a bigger system containing other functions.
- A meaningful information is generated by an ensemble containing:
. A system submitted to a constraint and able to receive an incident information.
. An information incident on the system.
. An information processing element, internal to the system and capable of identifying a connection between the received incident information and the constraint.

We name "meaningful information generator system" (or "Meaning Generator System" - MGS -) such an ensemble. A meaningful information cannot appear or exist spontaneously, with no cause. Every meaning has an origin which is the MGS that has produced it.

The components of the MGS are drawn on the figure I.2 , where a system submitted to a constraint S generates a meaningful information.

The systemic definition of meaning proposed here above has been built up with an example comming from the non human living world that has been formalized up into a system. This approach is distinct from the Peircean theory of sign, although some elements may be functionnaly close [6].

We consider the MGS introduced here as a basic element that can be used as a building block for meaning generation.
The MGS is simple. It is simple because it has a precise and unique function which is to generate a meaningful information with one incident information and one constraint. Practical cases can be more complex. The MGS can interact with other functions, but without changing it's basic nature of meaning generation. Functions like memory, representation, simulation, other MGS can interact with the MGS, globally or with some internal elements. These relations with other functions do not change the nature of the MGS.
Such a general definition of meaning generation may look as an oversimplification and appear reductive relatively to the immense and complex subject of "meaning". But assuming we can get a precise identification of the elements constituting the MGS and of its relations with other functions, we consider that the MGS should cover all the cases of meaning generation. This is an hypothesis that will have to be verified in the next chapters for the different types of systems taken into account.
As it will be introduced later, the correct identification of the constraint of the system will be a critical point relatively to the usage of the MGS modelization.

I.2 Signal and Information. Definitions

When building up our definition of meaning, we have been using words of current usage without specificly precising their content. In order to be able to apply with sufficient rigour this definition of meaning to our various cases of interest, we need to specify the content of the vocabulary we are using. More precisely we have to specify what is an information, its transmission from a transmitter to a receiver, an information processing system, a constraint, and the connection that can exist between an information and the constraint of a system.

The first points to explicit are that information can be memorized or transmitted, and that information is always carried by a signal. A text in a closed book is memorized information (the printed characters). The text we are reading from a book is transmitted information (the photons reaching our eyes). In both cases, information is carried by a signal. We call signal any variation of energy (ex: sound vibration like noise or voice; electromagnetic field change like light; chemical diffusion like odour; presence of an element like ink, protein,..). The origin of the signal is the transmitter, which is the source of the energy variation. By associating an information to a signal, we define information as the content of the energy variations of the signal (ex: Amplitude and modulation of a vibration; variation of chemical concentration at a given point; ink density; molecules in the protein,...). This definition of information applies to all the components of the signal variation. Consequences are that a given signal can carry several different information, and that a signal always carries an information. The elementary information being the signal itself.
The signal carrying the information will be transmitted through the medium separating the transmitter from a possible receiver (it is also said that the signal is propagating from the transmitter to the receiver). During its transmission, the signal may be submitted to some disturbances (parasites, distortions) than can modify it. The information carried by the signal is also in a position to be altered with the signal during its transmission.

It is to be noted here that a signal is not a priori permanent. The variation of energy that has produced the signal can have a limited duration in time (thunder produced during a thunderstorm has a limited duration). But the signal will propagate, and so will continue to exist, even if the energy variation that has produced it does not exist any more (Three km away, the noise of thunder can be heard 10 seconds after its creation, which is terminated). By the same way, a meaningful information that has been produced by a system will continue to exist even if the system that has originated it does not exist any more (or is not any more a MGS). More specifically, a meaningful information as we have defined it will still be meaningful in the absence of the constraint of the system that has produced it. In other words, the meaningful information exists with the signal that carries it, and this even if the system that has created the meaningful information disappears. A meaning (S) stays meaningful in the absence of S.

These precisions regarding the connections between signal and information also show that two information can be carried by the same signal or by different signals (a singer and the orchestra accompanying him can figure on the same recording or on different ones). This remark applies to the incident information and to the meaningful information that exist relatively to the constraint of the information processing system. The meaningful information can be carried by the same signal that the one carrying the incident information, or be carried by another signal. Relatively to the example of the paramecium, the incident information (level of acid in the water) is beared by the signal that is the acid water, but the meaningful information (too much acid) is beared by a biological signal internal to the paramecium. In some other cases, the meaningful information will be carried by the signal carrying the incident information.

As we deal with a couple formed by a signal and the information carried by the signal, when reading the word "information", one should understand "information carried by the signal"

A receiver is an element capable of extracting an information from an incident signal and transferring it on another signal (ex: hearing, sight, sensitivity to a chemical/physical element, ...).

As the transmitted signal can carry several different information, it is the nature of the receiver that will fix the choice of the received information (our eyes can detect only a given segment of wavelengths among all the light signals emitted by the elements of our environment).

Regarding processing of information, it covers all types of actions applied to information (creation, modification, storage, integration in a signal, extraction from a signal, comparison with other signals, transformation, ...)

Figure I.3 summarizes the different elements introduced about transmission, propagation and reception of a signal carrying an information.

As we want to explicitly take into account a reception function heading the system that receives the incident information, we need to complement the drawings used to illustrate the definition of a meaning (Figures I.1 and I.2). Indeed, we have been considering that the incident information was feeding directly the function of identification of the connection between information and constraint. Strictly speaking, a reception function that will transform the incident information into a received information should be introduced. And it is the received information that will feed the function of identification of the connection. This reception function is placed at the boarder between the system and it's environment. Figure I.4 reproduces the figure I.1 and the figure I.2 with an explicit drawing of the reception function.
(We do not need to modify the definitions of meaning and of MGS, as the notion of reception was already included in these definitions).

Regarding the constraint of a system, we regroup under this term the ensemble of automatisms, rules, laws and finalities that the system must respect to satisfy its nature. For example, as summarized above, the constraints of a living system are to survive and to reproduce itself (vital constraints). If the living element is not able to survive, it will die and loose it's nature of living element. If the living element is not capable of reproducing itself, it's nature as a species will disappear [7].

This "constraint of the system" is one of the elements that will need the most development in the following chapters, depending upon the type of system taken into account. Our example with the paramecium's vital constraint corresponds to a simple case (keeping in mind that such a choice was a deliberate one, in order to modelize the generation of a meaning in a domain sufficiently well known and understood. Cf I.1). But for other systems, the contraints may be more complex. They may for instance evolve depending upon memorized experiences, or be modified by other information. But they still functionnaly keep their role as constraints in the MGS.
As an example, it is clear that the constraints for human are significantly more complex that the constraints for animal life (among other things, because of reflected consciousness performances and associated free will). We will have to identify the constraints associated to each system taken into account regarding meaning generation.

Finally, the word "connection" used in the expression "connection between incident information and constraint" is to be understood as "all the relations that can exist".

 

I.3 Transmission and Reception of Meaningful Information. Domain of Efficiency of a Meaning

We have seen in I.1 that the meaningful information's role is to participate to the determination of the action that will be implemented in order to satisfy the constraint of the system. It has also been said that this action can take place outside the system, and eventually implicate other systems. This last case corresponds to the transmission of a meaningful information to other systems: the formal action that will satisfy the constraint of the system will come after the transmission of the meaningful information and it's reception by other systems.
These other systems can also be submitted to specific constraints.

In order to take these cases into account, we introduce the notions of transmission and reception of meaningful information, as well as the notion of efficiency of a meaning that will characterize the possibility for a transmitted meaningful information to participate to the determination of an action.

The animal world offers many examples of transmission and reception of meaningful information. An example is the case of a male cicada calling female cicadas by producing a specific sound. This meaningful information is transmitted by the male cicada towards the female cicadas in order to have them come for copulation and reproduction. This takes place in order to make possible the satisfaction of the vital constraint of the species. Another example is given by a bee informing other members of the hive about the location of a new source of pollen by flying in precise patterns. This bee dance is a meaningful information produced by the bee to indicate to other members of the hive the direction and distance of the location where are the flowers containing the pollen. This meaningful information will bring the bees to fly to this new source of pollen to feed the hive, and so contribute to the satisfaction of the vital constraints of the species.
Also, pheromones left on the ground by an ant is a meningful information for the other ants to access food supply (overall indication of best path - the most used -)
. This information is meaningful vs the survival constraint of the anthill.

But beyond there simple examples, one finds the question about the becoming of the meaning attached to the transmitted information. Will this meaning always be taken into account by the receiver in order to satisfy the constraints of the transmitter ? And what if the receiver happens to be submitted to it's own constraint, different from the transmitter one ? Under which conditions will the receiver be in a position to satisfy the constraint of the transmitter ?

These questions are to be looked at in order to address the cases of transmission and reception of meaningful information.
To take into account such a subject, let's begin by reminding that a meaning is an information. It is a meaningful information that has been created by a system submitted to a constraint.

It has been stated above that a meaningful information keeps it's meaningful characteristics even if the system that has generated it disappears. In other words, a meaningful (S) information remains meaningful (S) in a location where the constraint S does not exist. But it is obvious that no action aimed to the satisfaction of the constraint S can take place in a location where the constraint S does not exist (the paramecium cannot try to move from a place where it is not). In other words, the meaning (S) will be able to participate to the determination of an action only in the locations where the constraint S exist.

In order to clarify these points, we need to introduce the notion of "efficiency of a meaning" that characterizes the possibility for a meaningful information to participate to the determination of an action aimed at satisfying the constraint of the system. We define the efficiency of a meaning as being the aptitude of the meaningful information to participate to the determination of an action aimed at the satisfaction of the constraint of the system. We note "efficiency (S)" the efficiency of a meaning relatively to the constraint of the system S.

On the same token, we define the "domain of efficiency of a meaning" as being the domain where the meaningful information is capable to participate to the determination of an action aimed at satisfying the constraint of the system. We note "domain of efficiency (S)" the domain of efficiency of a meaning relatively to the constraint of the system S. And we state that the domain of efficiency (S) is the location where the constraint S of the system is existing. In other words, The meaningful (S) information is efficient (S) in the domain of efficiency (S).

In it's domain of efficiency, a meaningful information can participate to the determination of an action aimed at the system's constraint satisfaction. Outside of it's domain of efficiency, the meaningful information will still be meaningful but this meaning will not be usable for determining an action related to the satisfaction of the system's constraint. The information is meaningful but the meaning is not efficient.

In order to integrate this notion of efficiency to the various elements relative to meaningful information already introduced, we are going to consider a MGS attached to a transmitter that will transmit the meaningful information. We will name " transmitter system T" the global system containing the MGS and the transmitter. The transmitter system is submitted to a constraint T. We will suppose that the constraint T does not exist outside of the transmitter system. In the transmitter system, a meaningful information generated by the MGS is meaningful (T) and efficient (T).

When leaving the transmitter system, the meaningful information remains meaningful (T) but looses it's efficiency (The meaningful information transmitted is meaningful (T) and not efficient (T)). Let's suppose that this meaningful (T) information reaches a receiver system that is able to receive it. And let's also suppose that this receiver system contains a MGS having a constraint R that applies to the receiver system. The received information will be meaningful (T) and non efficient (T) in the receiver system. But if the received information has some connection with the constraint R, then the receiver system will create a meaningful (R) information that will be efficient (R) in the receiver. Two meaningful information then exist in the receiver system: a meaningful information that is meaningful (T) and non efficient (T), and another meaningful information that is meaningful (R) and efficient (R). In the receiver system, the meaningful (R) information can participate to the determination of an action in the direction of the receiver's constraint satisfaction. But the meaningful (T) information cannot participate in the receiver to the determination of an action relative to the constraint T.
If the constraints T and R ar identical, then the transmitter and the receiver belong to the same domain of efficiency, and the receiver can participate to the implementation of an action satisfying the constraint of the transmitter.
Figure I.5 summarizes these points.

This formalization of meaningful information transmission shows that a given meaningful information can generate different meanings in different systems receiving it depending upon the constraints of the receivers. And the actions implemented will also depend upon the constraint of the systems receiving the information. Taking again the example of a male cicada calling female cicadas for reproduction, if we imagine that a “cicada eater” can hear the sound produced by the male cicada, he will find it and eat it. Cicadas and cicada eaters can have different constraints to satisfy.

There is also a possible variation where the meaningful (T) information generated in the transmitter system is not directly transmitted but used to trigger the transmission of another meaningful information (T'). The participation to the determination of the action that will satisfy the constraint (T) begins by the triggering of the transmission of the meaning T', and will continue by the reception of the meaningful (T') information by another system.
The results established for the transmission of the meaning (T) are valid for the transmission of the meaning (T') by replacing (T) by (T') in the receivers.
Figure.I.6
represents the transmission of the meaningful (T') information.

 

I.4 Information, Meaning, Knowledge

In the previous paragraphs, we have introduced definitions and properties linking information to meaning. Even if we want to limit this chapter to a systemic formalization of meaning generation, we feel there is some interest to look also at possible links with the notion of knowledge.
Information and meaning have obvious relations with knowledge: what is known is made of information. And the information is known because it has some meaning for the knower.
We will not try to modelize knowledge as we did for meaning, but just address some simple relations between the two notions in order to position our systemic approach of meaning in an epistemilogical field.
R
elations between meaning and knowledge can be highlighted by comparing the corresponding definitions.

Several definitions of knowledge have been proposed: (http://www.humanlinks.com/wwwboard/topic3/messages/2.htm).
Among these definitions, one seems to fit with the generality of the systemic background:
"knowledge is organized information applicable to problem solving" (Woolf).
Now, comming back to the definition and properties introduced for a meaningful information, we find terms that echo the definition of knowledge: "information created by a system", "is to participate to the determination of an action". These expressions show that the notions of meaning and knowledge are close. Both correspond to specifc information which has been created/organized, and both exist for some action/problem solving.

Moreover, looking to the nature of both notions can also bring up some interesting similarities. Nature of knowledge is generaly positioned on an epistemology spectrum between two distinct positions: knowledge as a true image of the outside world - objectivist position -, or knowledge as specific build up of reality by the knowing element - constructivist position -. (http://www.stemnet.nf.ca/~elmurphy/emurphy/cle2.html)
In the objectivist position (also called "representational paradigm"), the external world is considered as an existing reality. Knowledge is acquired from the outside reality. (This view has been supported by the classic "representational cognitivism", which has been at foundation of classic A.I.).
In the constructivist position, knowledge is constructed by the system (the knowing element), and reality is internaly created in the system.
(http://chemed.chem.purdue.edu/chemed/bodnergroup/archive/publications/kelley.html)
The constructivist position is more recent than the objectivist one and has several important developments. As an axample, F. Varela and H. Maturana have build up the "autopoietic" approach and the "enaction" paradigm that describe how the outside world can be better taken into account by a system as a construction that will serve ah hoc behaviours, rather than as a representation of an existing environment - (http://www.informatik.umu.se/~rwhit/AT.html#Tutorial)

The approach on meaning introduced with the MGS in the previous paragraphs is on the constructivist side: the meaning is built up by the system from it's constraint, which is internal to the system (Fig. I.4)



1.5 Summary of the chapter and continuation

In this first chapter, we have established the basic elements of a systemic theory of meaning. These elements can be summarized as follows:

- A meaningful information is generated by a system made of (Fig. I.4):
. A system submitted to a constraint and able to receive an incident information.
. An information incident on the system.
. An information processing element, internal to the system and able to identify a connection between the received incident information and the constraint of the system.

A meaningful information (a “meaning”) is defined as the connection existing between the received information and the constraint of the system.

We call such a system a Meaning Generator System (MGS). It is a building block for meaning generation. A MGS and its constituants can be related to other outside functions (memory, simulation, other MGS, ...) Assuming we can get a precise identification of the elements constituting the MGS and of its relations with other function, we consider that the MGS should cover all the cases of meaning generation.

- Key Properties of a meaning:
. A meaning is an information, it is a meaningful information whose function is to participate to the determination of an action aimed at the satisfaction of the constraint of the system.
. The meaningful information is the information generated by the system submitted to a constraint. It is not the incident information, which is only a contributor to the meaningful information generation.
. The participation of the meaningful information to the action determination can be direct or indirect. The action can be internal or external to the system.
. A meaning is meaningful only relatively to the constraint of the system.
. An information that has not been produced by a system submited to a constraint is meaningless.
. The meaningful information exists physically as part of the signal that carries the information, and this even if the system that has generated the meaning disappears.
. The efficiency of a meaning is its aptitude to participate to the determination of an action in connection with the satisfaction of the constraint of the system.
. The domain of efficiency of a meaning is the domain where the meaningful information is efficient.
. A meaningful information produced by a transmitter system in which exists a constraint (T) will be meaningful (T) and efficient (T) in the transmitter. Out of this domain of efficiency (T), the transmitted information is meaningful (T) and non efficient (T). If this information is received by a receiver in which exists a constraint (R) having a connection with the received information, then the receiver will generate an information that will be meaningful (R) and efficient (R) in the receiver. R can be identical to T. (Fig.I.5).
.
A meaningful information can be used to trigger the transmission of another meaningful information. (Fig.I.6).
. Relations with knowledge: The notion of meaning introduced in the present systemic approach is close to the notion of knowledge defined as: "organized information applicable to problem solving".
The MGS is on the constructivist position side.

The continuation of this first chapter will be about using the MGS approach for the different levels of comlexity (matter, life, human, machines). A key point will be the identification of the constraints of the systems at each level of complexity.
A first step has been done on this last item by introducing "anxiety limitation" as a new constraint coming through evolution in addition to the basic "stay alive " constraint existing for all living elements.
See http://crmenant.free.fr/Biosemiotics3/INDEX.HTM
where we show that group life with Self-Representation and Conspecific Representation can naturaly produce anxiety for a subject.

 

Notes

[1] C.S. Peirce (1839 - 1914) is considered as the source of the contemporary philosophical conception of semiotic - the science of signs -. The Peircean concept of sign contains three components: The Object of thought, the Sign representing the object, the Interpretant (that provides a mental representation). The Interpretant is also a sign that can be interpreted too. And the semiotic process goes on. These three components cannot be separated. The Peircean sign is qualified as «Triadic». The notion of meaning is sometime associated to the Interpretant in Peirce's writings.
Detailed information is available at Arisbe: http://members.door.net/arisbe/arisbe.htm

[2] P. Gelepithis (Survey of Theories of Meaning. Cognitive Systems, 2, No 2, pp 141-162, 1988) classifies the current theories of meaning in four categories (philosophical, linguistic, formal and biological). One conclusion is that these theories of meaning have recognized that meaning does depend on us humans.
C. Eliasmith (How Neurons Mean: http://www.sfu.ca/neurophilosophy/members/eliasm/project.htm) considers that "the term 'meaning' is most often taken to be a property of language or, more properly, of its constituent parts, words or sentences", and that questions concerning meaning or content have been asked by philosophers "almost exclusively in the context of cognitive or psychological states, focusing on high-level representations, on beleifs and concepts". C. Eliasmith is working on addressing the same questions from a neuro-biological perspective.

[3] A positioning of the notion of meaning in the history of Information Theory is available in J. Segal PhD Thesis 1998 (in French): Theorie de l’information: sciences, techniques et societe de la seconde guerre mondiale a l’aube du XXIe siècle. Text is in Chapt 11: La notion d’information dans l’emergence de l’unite du savoir. Internet version at: http://www.mpiwg-berlin.mpg.de/staff/segal/thesis/

[4] A. Sharov: Biosemiotics:functional-evolutionary approach to the analysis of the sense of information. In: Sebeok, T.A., J. Umiker-Sebeok (eds.): The Semiotic Web 1991: Biosemiotics. De Gruyter, Berlin-New York 1992. pp. 345-373. Also at: http://www.ento.vt.edu/~sharov/biosem/txt/biosem.html
A. Sharov has analyzed the problem of the sense of information with the help of biosemiotics. Two aspects of the sense of an information are introduced: the meaning and the value. Meaning being the semantic characteristic of the sense, and value being the pragmatic characteristic. Biosemiotics has part of it's origins in the work of J. von Uexkull (1864 - 1944) who studied the problem of how living being subjectively perceive their environment and how this perception determines their behavior (with the key notion of "Umwelt"). Umwelt has been reactualised as a support for several studies on artificial life. Biosemiotics homepage at http://www.zbi.ee/~uexkull/biosem.htm

[5] We will not analyze the question about the origin and the meaning of the universe. We will consider the universe as we can know it on a scientific basis, without asking question about it's origin or about the meaning of it's presence.

[6] The elements relative to the notion of meaning that we have introduced in this chapter are different from the content of the Peircean theory of sign:

- Peirce's theory is a theory of sign, and the present paper is centered on meaning. With Peirce, when meaning is explicitly taken into account, it is relatively to the Interpretant. In the present paper, meaning is presented explicitly as a meaningful information. Meaning is defined as the result of a defined information processing done by a system submitted to a constraint.

- The Peircean presentations of sign are done relatively to human (the Interpretant is a human mind, with some indication towards non human minds). As based on mankind, the Peircean theory is rich, powerful and complex. Many studies are devoted to Peirce's writings and ideas. Our introduction of meaning in the present paper is done relatively to an information processing system, with no reference to human for purpose of simplicity regarding the nature of the information processing system. This starting point will be progressively widened by analysing the different constraints that are to be associated with material systems, living systems and human ones.

- The element of this essay that could be related with the Peircean theory of sign is the Meaning Generator System. This MGS can be compared to a very simplified version of the Peircean Interpretant.

[7] The word constraint has been chosen on purpose to regroup the cases of causes to which a system can be submitted, be these causes from the type of "efficient causes" (gravity is the cause of the fall of this stone), or from the type of "final causes" (passengers willing to go to Paris are the cause of this plane flight). We do not feel pertinent in this first chapter to subdivide the approach of the concept of meaning in different parts depending if we think we are facing efficient causes or final causes. We will see in the next chapters if it is necessary to deal separately with these different types of causes.