Download Mario Ludovico Evolving Systems. Recognition and Description  Attachment ATTACHMENT SYNTROPY...
Mario Ludovico – Evolving Systems. Recognition and Description  Attachment
ATTACHMENT
SYNTROPY This attachment is a synthesis of reviewed excerpts from an article of mine published in the Internet by online magazine SyntropyJournal.1 in 2008: It may be considered as both a smoother introduction to and a summary of the theory expounded in the previous part of this book. Actually, the innovative concept that works as a cardinal pivot of my theory of evolving systems is just the new definition I have proposed for the concept of “syntropy”, used to express the degree of internal organization that characterizes complex systems, especially systems with an intrinsic capability to adapt themselves to changes in the internal and external conditions. In the theory referred to, syntropy is a quantity complementary to entropy, the latter being the degree of internal “disorder” (or lack of detectable internal organization) that inevitably affects any physical system. Where it seems necessary for the sake of a more precise language, the text of this attachment includes also quite elementary algebra, by use of a few simple formulas, which  besides recalling the definition of the concept of “logarithm”  do not involve other concepts that are more difficult than the basic arithmetical operations. The reader endowed with a little larger mathematical background can obviously access more detailed explanations, as these are provided by the preceding chapters of this book.
1
See Syntropy: Definition and Use, www.syntropy.org/journalenglish, Dec.2008
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SYNTROPY – DEFINITION AND USE The aim of this article is to provide a larger number of potential readers with an idea about the practical use of the concept of “syntropy”, particularly in the field of social and economic studies. The word “syntropy” is nowadays often used in a competition with “negentropy” (the other term adopted – perhaps in a less correct way) to express the concept of processes or states that oppose entropy. Both words are since the past century used to mean that quite special mode by which energies of various kind concur in promoting either biological phenomena or other natural spontaneous processes, which are in an apparent disagreement with the overall tendency of matter to degrade. The overall tendency to degrade was given evidence and defined in the 19th century through the formulation of the concept of “growing entropy”. It seems to me that the diffusion of the term “syntropy” is not associated with a commonly accepted meaning for this word, because there is no univocal definition of the concept. There is also a formal statistical definition of “syntropy”, according to which “syntropy” is seldom used as “deviation from the norm”. In my view, and allowing for the historical origins of the word, the appropriate meaning of “syntropy” is the “degree of internal organisation” that characterises any system of events. This is basically the sense the word was given by Italian mathematician Luigi Fantappiè (19011956),2 who did also coin the word. I deem it impossible to grasp the concept of syntropy without having assimilated the concept of entropy, since – as I’ll try to show – not only are the two concepts in a strict mutual connection but entropy and syntropy are also complementary concepts: In other words, where it is
2
Luigi Fantappiè (19011956) , Principi di una teoria unitaria del mondo fisico e biologico, Humanitas Nova Editrice, Rome 1944. In the same year, Austrian physicist Erwin Schrödinger (18871961) published an essay, What is Life? The Physical Aspect of the Living Cell, Cambridge University Press (U.K.), in which he used the expression “negative entropy”, also giving its formal definition (Chapter 6, Para. 7). The concept was later referred to by other scholars with the word “negentropy”.
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possible to measure a level of entropy there is a complementary level of syntropy. Therefore, I wish to apologize for my long introduction, in which I linger on the meaning of “entropy”, whose use is often inaccurate and source of misunderstanding. 1. Entropy in Thermodynamics Thermodynamics is rather a complex branch of physics, and it seems difficult to many to get familiar with the concept of “entropy”. This concept was formulated by Rudolf Clausius (18221988), and its implications have been largely developed since the second half of the 19th century. In the 20th century the concept of “entropy” has also been adopted in other technical disciplines, following the reshaping of the concept in probabilistic terms as provided by Ludwig Boltzmann (18441906). Boltzamnn’s formulation led to interpret “entropy” as a measurement of intrinsic disorder in the state of any physical system. In thermodynamics, a state of disorder is viewed as the chaotic uniformity that establishes when an isolated physical system comes to a condition of thermal equilibrium. I deem it useful to start discussing first this aspect of “entropy”, prior to defining the concept of “system”, with a view to introducing later the concept of “syntropy” in a smoother way. Thermodynamics does essentially address principles and techniques aimed at utilizing heat (thermal energy): the possibility of utilizing heat decreases with the diffusion of its uniform chaotic distribution. Instead, thermal energy becomes more and more utilisable (for instance, to produce mechanical work) with the gradient of its concentration inside any physical system. Adjective “chaotic” is important in specifying the degree of uniformity in the heat distribution, as I’m going to explain ahead. Thermal energy coincides with the summation of the individual kinetic energies of the molecules of which any matter consists. Material molecules are in a permanent agitation, with an average agitation speed that depends on the matter’s average temperature. Actually, as per a fundamental definition of dynamics, the square agitation speed times half the mass of the molecule is the kinetic energy of each molecule, and the temperature of matter is the average kinetic energy of its molecules. Thus, summarising the concepts: “Heat” is the summation of the kinetic energies of all the molecules of which any material body consists. “Temperature” is the average kinetic energy of any molecule belonging to the material body. 134
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There is no counter indication if one adopts the concept of temperature as a density of thermal energy, i.e., as “amount of heat per mass unit”.3 In thermodynamics, entropy is a quantity that depends on the physical state of the system considered: For instance, if it is a fluid, entropy depends on how density, pressure, temperature, volume, gravity potential, etc., are distributed inside the fluid. These quantities, which determine the level of entropy, are generally variable quantities, usually mentioned as “state parameters”. Classic thermodynamics doesn’t deal with absolute values of entropy, since it focuses instead on the entropy variations that occur in spontaneous transformation processes undergone by material systems. In thermodynamics, entropy variation is defined by the formula (a very simple ratio between two quantities) used by Clausius to introduce the concept. The two quantities relate to a spontaneous transformation process and are: (i) the amount q of thermal energy that transfers from any section of a material system to any other section of the same system, the first section being at a higher temperature with respect to the other section: (ii) the temperature T at which the system establishes its thermal equilibrium at the conclusion of the process. The ratio that defines entropy variation is expressed by VE = q./.T . Any interpretation of the concept of entropy must allow for this simple formula.4 In the light of the preceding definition, it’s perhaps easier to understand why entropy does continuously tend to grow when
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There is no conceptual difference in considering temperature either as the average kinetic energy of any individual molecule or as the density of thermal energy per mass unit. Concerning temperature, there is to remark that the term has never been given a univocal and precise definition. The scientific definition of “temperature” is actually an operational one, in that it is a quantity measured with various kinds of thermometers, according to the specific technical context regarded. As to its physical dimension (see also subsequent Footnote 4), temperature is in some cases quantified in energy units.
4
The physical nature of the quantities that define ratio VE may lead to the following interpretation, amongst other possible ones: Considering that no energy is conceivable without the vehicle of the relevant mass, the inexorable increase in the entropy level is a measurement of the increasing amount of matter in the system that exits the transformation process and becomes the system’s “deposit” of idle energy.
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spontaneous transformation processes are involved. Indeed, during any transformation, there is a continuous transfer q of heat (or of other forms of energy) from some sections to other sections of the system. Therefore, quantity q can never be nil until the process ends with an equilibrium state. 1.1 – Entropy, Age and Time The unceasing increase in the amount of entropy during any physical transformation process has led to consider entropy as the transformation’s degree of irreversibility. Thus, one may associate the concept of “age” with that degree of transformation irreversibility. The age of any material system, as measured in terms of entropy generated by its transformation processes, has substantially nothing to share with the concept of “time” used in physics, and with the time currently used for common practical purposes in our everyday life. In simple words, thermodynamic age, which inheres in the ageing of any material system, cannot be properly measured with clocks. The physical dimension of the age regarded by thermodynamics is energy divided by temperature, whereas the physical dimension of time is distance divided by speed.5 This is an important remark, which points out the different physical nature of age with respect to time: Age involves energy and/or mass, time neither mass nor energy. Moreover, entropy, or age, is always a positive parameter, never less than zero, whereas time of physics is a parameter that may be given either positive or negative values. Time is a parameter associated with reversible processes, whose description is symmetrical with respect to any reference frame, whereas
A useful note for whom has lost familiarity with the language of physics. Sometimes, specialist languages can be misleading, because of terms that – in a correct English – might be replaced by other more appropriate ones. It’s the case of “physical dimension”, which could suitably be substituted with “physical nature”, or “physical character”, or the like. Almost all of the quantities addressed by physics can be characterised by numerical powers of three basic “physical dimensions” (or “physical characters”): These are “mass”, symbolised with [M], “length” (or “distance”) symbolised with [L], and “time”, symbolised with [T]. For example, the physical dimension of quantities that express “volumes” is the “third power of a length” [L3], the dimension of “speed” is “length divided by time” [L / T], the dimension of “force” is “mass multiplied by acceleration” [ML / T2], the dimension of “energy” is “force multiplied by length” [ML2/ T2], etc. Moreover, there are dimensionless quantities, usually expressed by ratios between two hom*ogeneous quantities, like – for example – ratios between two masses, or between two forces, etc. In physics, dimensionless quantities are symbolised with number “1” in square brackets, i.e., by [1]. 5
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entropy (or age) is a parameter inherent in irreversible processes, which are not symmetrical with respect to that same reference frame. However, according to practical needs and in most cases, there is no serious inconvenience if one uses time as a parameter of age, since the use of clocks and calendars is a noticeable simplification also in describing irreversible processes. Provided that one keeps oneself well aware that time taken for age (i.e., the use of clocks to measure entropy) becomes a oneway and irreversible quantity too. The opposite, i.e., the use of entropy to measure time of mechanics, would make no sense in almost all cases. The practical use of time for age establishes a relationship between the positive trend of the former with the natural trend of the latter, but one should never assume that there is a direct and regular proportionality between these two quantities. For example: The age of a rock, which is exposed only to gravity and to meteorological phenomena, grows very slowly in a comparison to the stone’s time duration. At variance with this example, the process of generation, life and disappearance of some subatomic particles is considered as a longlasting event, though it is measured in nanoseconds. Thus, also the age of a 120 year old person is considered as an enormously longlasting life, though its time duration is negligible if compared with cosmological times. The graph here below shows how the growth of age should be viewed in a comparison to time. Time can linearly vary according to two opposite directions, whereas age grows constantly in a quite irregular way, though never dropping its varying quantity below levels already achieved.
+ age/entropy (valuemeter) 
_
(trend )
watch time
(calendar time)
+

_ An intense and complicated debate has developed since decades about what time is or how it should be understood. Ilya Prigogine (19172003) and his school of thought have devoted many mental energies to
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the issue, and several interesting aspects of the question have usefully been put into evidence. It seems to me, however, that part of the complexity attached to this issue is artful and may be bypassed. Prigogine bets on a unique kind of oneway time, which should be considered as inherent in any event, irrespective of its mechanical or biological nature. 6 In my opinion, as partly expressed above, there is an initial selfdeception, which consists of using one same word to define two different concepts. The use of clocks does only provide conventionally “regular” reference motions. These reference motions, which are expressed in length units or in arc degrees gone by the clock’s hands, are compared with the lengths gone by other objects in motion. Instead of keeping the route of the clock’s hands as a reference length, one might follow the suggestion that comes from Relativity: In describing motion, the fourth reference coordinate, which measures the motion duration, can be the length (in meters or yards) travelled by a beam of light while the observed object shifts its position from one position to another.7 Furthermore, time  at variance with age  can also compare distances traversed by an object in motion with volumes of sand or water dropped by a clepsydra, which means that time can also be measured in litres or gallons, and makes it clear that time is only a way to measure any
Prigogine addresses this issue in almost all publications of his. Substantially, he states that no phenomenon can be considered as a reversible event. Every event is a process characterised by state fluctuations, and these can be properly addressed only through a probabilistic approach, which takes them into the paradigm of statistical mechanics, i.e., into the field of evolution and irreversible processes. Which inevitably leads to the oneway nature of time. (In this connection, see also Paragraph 2 ahead). 6
In Relativity, the fourth dimension is expressed by product ct, in which c is the constant speed of light, and t is a measurement of time whatever. The dimension of ct is “length”, in symbols [ct] = [L]. This means that the relativistic fourth dimension is not “time” but “length”, expressed in meters (or yards or kilometres), just like the other three space dimensions known as length, width and height. (It’s common experience, for example, to use a clock to calculate distances while travelling on a highway at a constant speed). Therefore, all the coordinates necessary to describe the motion of any object can be made hom*ogeneous with each other; whereas in classical mechanics time was a heterogeneous parameter, an “intruder”, which didn’t allow one to describe physical events in a mere geometrical way, i.e., in terms of length coordinates only. I deem this is an important clarification brought by Relativity as to the meaning of time in physics. 7
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motion by means of any other “regular” reference motion. Quite a different approach to the use of clock is considering any clock as a process. But the ageing of the clock has no conceptual relation with its function of timemeter. There is an innate language conditioning, which  since our birthday  makes us believe that the pace of our growing age is measured in astronomic cycles as well as in number of laps run by the clock’s hands; which in the end persuades us that age and time mean substantially the same thing. However, if we pose ourselves in front of a mirror keeping a clockface beside our face we could see that the reflected clockhands can easily invert the direction of their motion, but we could never see our face rejuvenating, as long as the clockhands may go. Time is symmetrical with respect to any reference frame in space, the age of our face is not. I have repeatedly used “spontaneous” as an adjective of “process”, but the meaning of this adjective – as far as the subject is thermodynamics – must be confined within the bounds of the processes that are fully controlled by the laws of physics, which includes thermodynamics. The expression “spontaneous process” takes a different meaning if used in dealing with biological events. Whence the need to analyse that other kind of “spontaneity” whose nature seems to conflict with the inexorable law of entropy, while this law remains instead at work in all physical phenomena. Any physical transformation involves transformation of energy. Energy, in material systems, is generally present in various forms, each form being characterised by a different level of quality. Thermal energy (heat) is at the lowest level of quality. The irreversibility of physical processes consists in the degradation of the involved energy, whose forms decay through the tendency of all energies to turn into thermal energy. If heat is the only form of energy involved, then the irreversibility of the process is closely related to the degree of heat diffusion inside the material system. This enters its maximum entropy state when the whole energy has become heat and every element of the system is at the same temperature. In a thermal equilibrium state, or maximum entropy state, flows of energy from higher quality or concentration levels to lower levels are no more possible; this lack of energy gradients is just the reason why further spontaneous transformations are (theoretically) impossible for the system. Therefore, for any system that is perfectly isolated in the universe, thermodynamics establishes that no spontaneous physical transformation is possible in a state of maximum entropy.
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2. Entropy as “most likely state” It’s interesting to observe that for thermodynamics entropy grows with the temperature of a system in thermal equilibrium, whereas – for the Third Principle – entropy is nil when the system’s equilibrium temperature is absolute zero (0° K). However, at this extreme temperature, the system should also be void of energy and therefore incapable of any spontaneous transformation; which would contradict the expectation that “nil entropy” means maximum potential of spontaneous transformation. This apparent contradiction sheds light on the importance of the disorder that is intrinsic to thermal equilibrium states. The state of matter at high and very high temperature is characterised by more and more chaotic diffusion of molecular kinetic energy, which implies the tendency to a uniform probability distribution of the molecules physical states inside the relevant volume of matter. A persuading example of such a tendency in the molecular behaviour may be guessed thinking of the possible state and position of individual molecules in a volume of gas at high temperature. An important theoretical confirmation of entropy as “state of disorder” came from the work carried out by Ludwig Boltzmann, whose statistical version of thermodynamics proved that the entropy of isolated systems tends to grow because “state of higher entropy” means state of higher disorder, and states of the kind are statistically the most probable ones. Whence also the proof that the level of entropy is a function of the probability of disorder that develops inside the systems. (A short break for terminological clarification seems here advisable, to remind the average reader of the lexicon proper to mathematical language. The word “function”, in mathematics, summarizes an expression composed at least by eight other words, and is used to mean any “quantity that is determined by other variable quantities”. For example, saying that z is a function of x and y is a way to say that any value for z depends on how the values for x and y may vary. There are infinite ways in which any quantity may depend on other quantities, but these ways are always mathematically specified, according to case). Thanks to Boltzmann we can now avail ourselves of a more significant definition of entropy. The macrostate that characterises the overall physical state of a system (as this depends, for instance, on the internal distribution of density, pressure, temperature, gravity potential, etc.) can be determined by many different combinations of many different microstates. These are
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the individual physical states of the molecules of which the system consists. When the system’s macrostate is of nonequilibrium, the possible molecular microstates have different probability of occurrence for different molecules, though various combinations of different microstates may result in the same macrostate for the system as a whole. By a coarse similarity used only for exemplification purposes: Five balls which are equal to each other in weight, but distinguished by five different colours, can variously be distributed over the two pans of a balance, in order to obtain – for example – the particular position of the balance pointer caused by three balls placed on the left pan and two balls on the right pan. This particular position of the pointer in the balance is taken as the analogue of the system’s macrostate. One can obtain the same result changing repeatedly the position of the colours (the analogue of the microstates) while keeping always three balls on the left pan and two balls on the right pan of the balance. Instead, in a state of thermal equilibrium, all the possible microstates, which can determine the same macrostate, have an equal probability to occur. This common probability value is expressed, for each microstate, by P = 1/W, where W is the total number of possible microstates. Boltzmann has proved that the entropy level in a state of thermal equilibrium is expressed by E = – k lnP = – k ln(1/W ). In this formula, “E “ represents the maximum level of entropy for a system in a thermal equilibrium state, “ln” means “natural logarithm”, and “k ” is a constant positive value, referred to as “Boltzmann constant”. The physical dimension of k is energy divided by temperature, while probabilities are always pure numbers. It has previously been observed that entropy can in no case be less than zero. In the formula above there is the logarithm of a probability. In all cases, probabilities are positive values ranging between zero and 1. Probability is zero when the relevant event is impossible; probability is 1 when the relevant event is unique and certain.8 To remind the reader of his/her high school learning, the concept of “logarithm” expresses the inverse of “exponential”. For example, a.= b.c is a formula where b.c is the “exponential”, of which b is the “base” and c is the “exponent”. One can express c as ln.b.a to say that c is the “exponent of base b “ in the exponential that gives the value for a. Most frequently, and when the base of the logarithms is not mentioned, Euler number e = 2.7182818… is used as a base for both exponentials and logarithms, which are in such a case 8
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As known, logarithms of numbers greater than zero 0 but less than 1 are negative numbers. For example: ln(0.6) = – 0.5108256. The sign “– “ (“minus”) before constant k in the formula for entropy E is used to turn the negative value of the logarithm into a positive value. Using properties of logarithms,9 the same formula can be written also in this way: E = – k ln(1/W) = – k (ln 1 – lnW ), whence, accounting for ln 1 = 0, it’s immediately seen that E = k lnW . The reformulation, due to Boltzmann, of deterministic concepts of classical physics through a probabilistic approach has led to consider the not negligible role played by the observer that tries to describe nature in an objective way.10 Statistical mechanics, as promoted by Boltzmann’s theoretical work, dubbed “natural logarithms” and symbolized with “ln”. Remember also that the logarithm of zero gives minus infinite ( ln 0 = – ∞). The logarithm of 1 is zero (ln 1 = 0). The logarithm of negative numbers gives imaginary values, which cannot be used in this context. The logarithm of the ratio between two numbers a and b, is given by the difference between the logarithm of a and the logarithm of b, i.e., ln(a./.b) = lna – lnb . As to the logarithm of numerical powers, remember that ln(a.c ) = c.lna, and ln(1/ a.c ) = ln(a.– c ) = – c.lna. 9
The system of concepts of classical physics, in which Relativity shall also be included (at variance with the opinion of philosopher Karl Popper on the subject) represents nature by means of a mechanistic paradigm, in that the interactions between components of matter are considered as chains of causes and effects, which – at least in principle – can be identified and calculated, provided that all the involved initial and/or final conditions (the socalled “border conditions”) of the events to describe are known. The manyyear controversy between Einstein and the “indeterminist school” of Bohr and Heisenberg (often referred to as “.Copenhagen School.”) focused on the idea, maintained by Einstein, that the “indetermination” about the state of atomic/subatomic particles was only due to the constraints imposed by the inadequacy of the investigation, with no reason for doubting the substantial deterministic character of the physical events at atomic and subatomic scales. On the contrary, for Bohr and the indeterminist school, the indetermination concerning the state of atomic and subatomic particles must be considered as intrinsic to the scale of those events, which escape in principle (i.e., not because of technological constraints) from any deterministic description and representation.
10
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has primed quantum mechanics, perhaps the most important scientific and philosophic revolution of the 20th century. Actually, the probabilistic version of the concept of entropy is an assessment of the system’s state as made by the observer, rather than a description of the system’s state in itself. The many billions of individual microstates, relevant to the many billions of molecules that form the system, are neither random states in themselves nor states intrinsically equivalent to each other. Each microstate is in any case the result of physical interactions that obey the laws of classical mechanics, though such interactions – as a fact of matter – cannot be described individually. The statistical problem doesn’t inhere in the molecules individually considered, but in the impossibility for the observer to follow and describe their behaviour in detail. Even macroscopic operations, such as the measurement of the temperature of any given volume of fluid in thermal equilibrium, pose problems of a statistical nature. In spite of any sophisticated technology, each measurement operation is affected by a different degree of precision, which in practice cannot exceed the capacity of the measurement instrument; so that the final measurement result is actually a subjective decision rather than the true temperature of the fluid. This means that it is the observer who establishes the significant degree of approximation for his measurements, and decides that the temperature is everywhere uniform within the given volume of fluid. It is the start of a subversive change in the scientists’ attitude. It is perhaps the first time in history in which scientists feel impelled to recognise that the real world in itself is substantially different from what technical instruments and specialist languages can investigate and represent. In scientific activity, the recourse to the use of concepts and techniques of the theory of probability means the awareness of impassable limits to our knowledge of nature. These limits inhere in the observer, and impose an inevitable amount of subjective uncertainty in all “objective” descriptions of the real world. Therefore, one thing is the shared consensus on the objectivity of a theory, quite different thing is to assume that any objective theory can describe and represent how the real world does objectively work. Later, quantum mechanics had even to introduce the issue of the unavoidable interference brought by the observer into the behaviour of the events under investigation; which  in 1927  led Werner Heisenberg (19011976) to formulate the “indetermination principle” that – on the one hand – laid the basis of quantum mechanics and – on the other hand – subverted the philosophy of scientific activity at every scale of investigation.
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As Heisenberg explains, at the scale of subatomic physics, any investigated system includes both the observed particles and the observer with his laboratory equipment, in a whole made of interacting components. In a sphere like that, observations and analyses can only be carried out by a statistical approach, while deductions, calculations and predictions can only be of a probabilistic nature. 3. Probability, Subjectivity, Information Thanks to mathematician Andrei Nikolaevic Kolmogorov (19031987), theory of probability has undergone in the 20th century a complete axiomatization, which makes this theory a formidable instrument for analysis and prediction in many different fields of research. “Axiomatization” means that “probability” is addressed as an abstract mathematical quantity, with neither physical dimension nor reference to any physical reality. Probability is a concept that lends itself to be treated in mere logical processes, because it expresses a simple criterion to assess the degree of truth either concerning statements formulated in abstract languages or concerning the occurrence of expected real events. However, in the application of the theory, there is often the problem of translating the abstract concept of probability into a number that is useful for practical purposes. This problem gave rise to a school of thought of which mathematician Bruno De Finetti (19061985) was a major representative. According to that school, degrees of subjectivity are always associated with assessments of probability, which implies an intrinsic degree of unreliability in any reasoning that develops by means of probabilistic criteria. De Finetti, in particular, has extensively discussed the use of the concept of probability, with an accurate argumentation to prove that any assessment of probability is based on the amount and on the quality of the information that is available about expected or guessed events. Usually, the assessment of probability values regards a socalled “probability distribution” among a set of interconnected possible events. It is a “probability distribution” if the sum of the values of the assessed probabilities is equal to one. Typical example (the study of which, by the way, has historically originated the theory of probability) is the probability of show distributed among the sides of a dice in the relevant game of chance. If the dice is not rigged, the probability of show during the game is the same for all the six sides of the dice, and is equal to 1/6 (one sixth). Therefore, the summation of the six probabilities of show is equal to 6/6 (six sixths), i.e., equal to one. If the dice is rigged, the distribution of probabilities among the six sides of the dice is uneven, and changes in the values of the probabilities 144
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depend on how the dice has been rigged. Nevertheless, the sum of the six different probabilities remains still equal to one, whence a probability distribution is still associated with the use of the dice in the game. Obviously, in gambling, the cheat  who knows how the dice has been rigged  enjoys a greater amount of information with respect to other players that are unaware of the trick. Thus, the cheat’s bet has a comparably greater possibility to succeed. Such a situation may also be described by stating that the cheat, in making his stakes, is affected by less uncertainty with respect to the other players. Mathematician Claude Shannon (19162001) must be acknowledged for having provided in 1949 the theorem that proves that there is a unique and quantifiable amount of uncertainty associated with every probability distribution. 4. Entropy as Statistical Uncertainty Shannon’s theorem, which univocally assigns a welldefined amount of uncertainty to any probability distribution, has provided science with an important instrument, perhaps still to be appreciated to the appropriate extent. He has introduced a new concept in the theory of probability, formally perfect, which noticeably enhances the potentials of this very fertile branch of mathematics. It’s worth showing in mathematical symbols the simple formula that defines the statistical uncertainty associated with a probability distribution. Let’s go back to the example of the dice. Six numbers, ranging from 1 to 6, one number per side, distinguish the six sides of a regular dice from each other. Tossing the dice in the relevant gambling, the probability of show, when the dice stops on the floor, is 1/6, the same for each side of the dice. Therefore, it is possible to write p1 = p2 = p3 = p4 = p5 = p6 = 1/6 , where p1 , p2 , … , p6 are the probabilities of show for side 1, side 2, …, side 6, respectively. As seen, these probabilities form – by definition – a “probability distribution”, because their sum is equal to 1. According to Shannon’s theorem, the statistical uncertainty “ U “ associated with a probability distribution relevant to six possible events (like those concerning the dice) is expressed by the following relation: U = ─ h (p1 lnp1 + p2 lnp2 + ... + p6 lnp6) , in which h is a constant value that depends on the base of the logarithm used.11 In some applications of this definition of uncertainty, constant h
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has no special significance and is assumed equal to 1. In gambling with a regular (nonrigged) dice, expression “plnp” has the same value for all the probabilities of the distribution, for these probabilities are equal to each other, i.e., plnp = p1lnp1 = p2lnp2 = ... p6 lnp6 = (1/6)ln(1/6) = ─ 0.298626. Therefore, assuming h = 1, uncertainty U, as expressed according to the above definition is U = ─ 6 x (plnp) = ─ 6 x (─ 0.298626) = + 1.791759. Instead, if the dice is rigged, for instance with number 3 printed on two sides of the dice, the probability of show for 3 in the game is 2 x (1/6) = 2/6 = 1/3, while the probability of show for the remaining four sides of the dice remains equal to 1/6, which is obviously less than 1/3. So modified,12 the distribution is now characterised by a value of uncertainty that is less than U calculated above and becomes U’ = ─[(1/3)ln(1/3) + 4x(1/6)ln(1/6)] = ─ (─0.366204 ─ 1.194506) = + 1.560710, which is clearly less than U = 1.791759. It is important to remark that there is less uncertainty only for the cheat, who knows how the dice is rigged. For the players who are unaware of the trick the uncertainty remains as before, i.e., equal to U. Then, the shape of the dice imposes constraints on the probabilities assessed by the players, which entails that the evaluation of probability is subjective, for it depends on the information available to different players. The possible events relevant to dicing are finite in number (6 events) and strictly interconnected, so that – as to gambling – one may consider dice as a system of events, in which the occurrence of one event excludes the possibility of occurrence for the other ones. If the dice is quite a regular one, uncertainty U = 1.791759 is the maximum uncertainty it is possible to associate with the six
Operator “logarithm” may have whatever base. As previously recalled, Euler number e = 2.7182818… is the base of natural logarithms; number 10 is the most common base for logarithms used in engineering, and number 2 is the base for logarithms used in information theory; but no theoretical constraint limits the choice.
11
Note that also in the modified distribution the sum of the relevant probabilities equals 1.
12
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probabilities, because these are equal to each other. This is the general case, in that the uncertainty associated with a probability distribution achieves its maximum value when all the probabilities are equal to each other. Obviously, such a maximum value depends on the number of the probabilities belonging to the distribution. If N is the number of the possible events, these are equiprobable events if all of them have the same probability of occurrence, which is expressed by P = 1/N. Then, the relevant maximum uncertainty is given by Umax = ─ N [(1/N) ln(1/N)] = ─ ln(1/N) = lnN , considering the property of logarithms, by which ─ln(1/N) = ─lnN –1 = ─ (─lnN). It can immediately be observed that the formula for this maximum uncertainty is formally identical to that one given by Boltzmann for the entropy of a system in thermal equilibrium (remember E = k lnW ), including the presence of a constant factor, “h “, not shown in the above formula for Umax only because I have assumed h = 1 for the sake of writing simplicity. In the formula for Umax , the number N of the possible events regarded by the relevant probability distribution corresponds to the number W of the possible microstates of maximum entropy in thermodynamics. It should not be difficult to guess that the similarity between the two different situations is not only formal but also conceptual. Actually, the mathematicians that deal with the theory of probability deemed it obvious to adopt the term “entropy” as a synonymous of “statistical uncertainty”. The interesting consequence of this fact is that the concept of entropy has entered the practice of many different disciplines, with the possibility of a direct measurement of entropy in all the cases in which the “behaviour” of a system can be described through probability distributions. The only substantial difference between entropy of thermodynamics and statistical entropy might be seen in the physical dimension: the dimension of the former is energy divided by temperature, whereas the latter has no physical dimension, it being a pure number.13 Entropy as a pure number offers a limitless sphere of applicability of the concept. As a pure number, in fact, statistical entropy becomes a significant qualitative measurement of the state of the system, irrespective of the physical nature of the system considered. This point can be questioned if temperature is expressed in energy units: In which case, also the entropy of thermodynamics becomes dimensionless, like a pure number.
13
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In the light of the preceding notes, it’s worth going one step back to the principles of thermodynamics, according to which the entropy of any material system grows with its temperature and vanishes when the system’s temperature drops to the value of zero degrees Kelvin (273.15° C). One can give an explanation for these principles using the concept of entropy as statistical uncertainty, which clarifies the relationship between the objective state of the system observed and the subjective instruments that are at the observer’s disposal. For clarification purposes, let’s avail ourselves of schematic but not inappropriate examples. Suppose that the observer, through a sophisticated microscope, can observe two molecules, each of a different type of gas. The two molecules are confined inside a small transparent spherical box. The task is to record in every given instant the state of the molecules in terms of the respective position and momentum. The two molecules enter the small box with given initial directions and momentums. All the physical characteristics of the two molecules and of the box are also known. The observer can use, along with the microscope, also a special videocamera for recording – instantbyinstant – what is going on inside the small transparent container, in order to couple the observation times with the respective positions of the two molecules. If the initial speed of the two molecules is not too high, not only both the observer and the camera have no difficulty in recording how the motion of the two molecules develops inside the small container, but the observer can also use the available basic data and the whole conceptual outfit of classical mechanics, with the relevant mathematical instruments, to calculate the momentums and the positions of the two molecules with a satisfactory precision. If additional molecules of other different gases are subsequently introduced into the small transparent box, and the speed of the newly introduced molecules increases with the number of the molecules introduced, the situation becomes more and more complicated for both the observer and the camera, not only because of the increasing speeds but also because of the rapidly increasing number of collisions between the molecules. Not only becomes it more and more complicated to determine the position and speed of each molecule but also the respective identification. The increasing agitation of the molecules inside the small transparent box coincides with the growth of the temperature of the gas mixture. Whatever the observation instruments, there will be a certain temperature level at which the information from the gas mixture becomes confused to such a point not to be any more utilisable to the observer for his initial purposes. Should he be requested, for example, to
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assess the probability that, at a given instant, one particular molecule passes the geometrical centre of the box, the only possible answer would be that such a probability is the same for all the molecules in the box. At that observation point, the observer’s capacity to describe in detail the situation created by the molecules inside the small container is affected by the maximum degree of uncertainty. Nevertheless, it is not licit to make the objective individual state of the molecules correspond to the subjective condition at which the observer is arrived, to mean that it’s not licit to suppose that the laws of mechanics have disappeared in correspondence with the disappearance of the observer’s capability to follow the behaviour of each molecule inside the box. The maximum entropy that the gas establishes at that point is actually the entropy concerning the state of information at which – against his will – the observer has come to find himself. For a confirmation of this interpretation of the concept of entropy, let’s see now what happens if the temperature of the gas mixture inside the box is brought down to zero degrees Kelvin. As the temperature decreases, the molecular agitation decreases too, until it stops completely at the absolute zero temperature. At this extreme point every molecule would keep its own steady position inside the volume of the box, so as to allow the observer to determine the position, the state and the identity of each molecule with an almost absolute precision. The information needed by the observer would then be complete, no uncertainty would affect his observation, which means that the statistical entropy associated with the system would vanish together with the system’s thermodynamic entropy, according to the Third Principle, which was postulated in 1906 by Walther Ernst (18641941), before the proof provided by Boltzmann’s statistical thermodynamics. In this connection, it’s worth reconsidering Boltzmann’s statistical definition of entropy: At zero degree temperature, every molecular microstate is no more in a probable state, for its state is certain for the observer. In probabilistic terms, any certain possibility is measured by number 1; therefore, at zero degrees Kelvin, symbol “ln(1/W)” for uncertainty in Boltzmann formula becomes “ln1 = 0”, since the state is certain for every molecule, and the system’s entropy is nil. Probabilistic uniformity, when it describes the maximum entropy state for a system at a high temperature, cannot be considered as corresponding to an objective uniformity in the temperature of the system, because this uniformity cannot be proved true for each individual molecule that participates in the thermal agitation. The concept of maximum entropy as maximum disorder, as previously seen, comes from this practical impossibility. Therefore, maximum entropy is
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equivalent to maximum confusion in the state of the information about each element of the system observed. The uniformity may be more or less chaotic, according to the utilisable information the observer can get from his observation until the system shows any degree of describable order. This leads to understand why the probabilistic uniformity at zero degrees Kelvin definitively ceases to be “chaotic”, and is instead a description of a perfect order, the distribution of the system’s microstates becoming detectable in its unique steady configuration. The thought experiment discussed above, however, is actually impossible to the extent to which zero degree Kelvin is an unattainable temperature. The reasons for this impossibility are both in the energy intrinsic to every molecule14 (which, in turn, is in itself a rather complex system) and in the intrinsic instability of the physical space in which every material component is immersed. The cosmic microwave background of common knowledge would be sufficient alone to prevent material particles from keeping perfectly still, i.e., void of kinetic energy; without considering the inevitable impact of the radiation energy that would necessarily be sent against hypothetically still particles to detect and record their state. That is why the concept of null entropy must be considered as a theoretical limit only, with no corresponding physical reality. The residual content of energy in matter at any low temperature is sufficient to make the start of spontaneous transformation processes likely; moreover, it may be argued that the overall physical condition determined by a lowtemperature environment does actually favour the formation of complex material systems, including biological systems.15 In the opposite direction, there is no theoretical idea of a temperature that is high enough to determine an impassable maximum for entropy. Mathematical developments of chaos theory, along with some sophisticated experiments that followed, proves that matter, even at the “maximum” level of its “apparent” disorder (which means, at the highest level of confusion for the observer), can always establish internal orderly structures that condition the behaviour of its components.
According to quantum mechanics, even at zero degrees Kelvin any material component detains an intrinsic kinetic energy, which is referred to as “zero point energy”. In this connection, note that quantum mechanics considers all “elementary particles” of matter as also consisting of the respective associate waves.
14
It’s one of the theses sustained by Schrödinger in his essay “What is life? The Physical Aspect of the Living Cell”, previously cited. 15
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In the preceding sections of this article I’ve drafted the main arguments that have led to assume “degree of disorder” as the substantial meaning of the concept of entropy. It seems now licit to ask whether it is possible to identify different degrees of order left – in any material system – by the different degrees of disorder associated with the system’s states. Before trying an answer to the question, it is worth pausing at the meaning of the word “system”. 5. System Any subject of study that engages human intellect is casebycase defined through different modes of concentration and distribution of mental attention. The attention first “delimits” the subject and then “configures” it according to “components” or “elements”. In accomplishing these mental operations, the observer uses identification and analytical instruments provided both by his mother tongue and by specialist languages and techniques. In other words, the perception of any object or set of objects occurs both through a physical contact (i.e., through senses and instruments) and through languages that can represent and describe the object perceived. It’s just through the language that one can determine the modes of concentration and distribution of his attention. The linguistic institutions, which preexist individuals and generations, not only determine a shared communication medium between different observers, but also  to a very large extent  a shared way in which the world is perceived. It’s a physiologic datum that transcends individual mental attitudes and induces many to believe naturally that each of the terms and concepts, which belong to the languages used, are objectively corresponding to things, these being therefore perceived as objects that preexist per se. The above premise intends to introduce the assumption that the identification, the definition and the description of whatever “system” is substantially a linguistic operation of a subjective nature. Any “obvious” distinction, like that between a system defined as “refrigerator” and another one defined as “gasoline pump”, becomes perhaps an impossible operation within the Neolithic culture of tribes recently discovered in previously unexplored recesses of New Guinea. But, beyond this extreme example, any educated member of our civilisation, should he suddenly be dropped onto a quite unknown and never imagined environment, would find it problematic or impossible to identify “different objects” or “systems of different objects”. In this
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connection, it seems appropriate remarking that in almost all cases the identification of any object is strictly associated with the identification of its possible or actual function (or differentiated role or use or behaviour with respect to other objects), be its function clearly known or simply supposed. Let’s go back to the example concerning a “gasoline pump”. The image, as provided by these two simple words, is perceived with small differences among people who are used to see gasoline pumps and to employ them to fill car tanks. The mental image can isolate this kind of object from any possible context and enables any person to represent it graphically. The images of a gasoline pump drawn by a sample of various persons would be little different from the sketch made by a tenyear child, but all the sketches would easily be interpreted by anyone as “image of a gasoline pump”. Nevertheless, the “gasoline pump” perceived and represented in that way is a drastic simplification of the reality regarded. Not only is the object “gasoline pump” something intrinsically different, in its mechanical consistence, from the commonly perceived image, but it is also an inseparable component of a much larger and complicated system constructed to carry on a specific function. The pump is only one of the many “outlets” of a system that includes underground tanks, tank trucks, electric power generation and distribution network, oil wells and refineries, road network, car users, plus an ample range of environmental components, such as ground, atmosphere, sunlight etc. To understand what all this means, it would be sufficient to think of our surprise in finding an object like the one commonly perceived as a “gasoline pump” in a hotel bedroom. Nevertheless, there is no reason for disappointment if “gasoline pump” remains a common daily locution to express the concept, as it is familiar to the man in the street and to tenyear old children. The true point is – in general – the use of the meaning of words that one intends to do. After having pointed out that impending snares affect the language on which our knowledge is based, it is time to try a definition of “system” apt to discourage dissension. The “system” meant in this article is a set of material components, each of which is identifiable and definable by means of whatever language; these components are perceived by the observer as connected with each other through detectable and measurable interactions. All the components of the “system” can be represented as elements included in one of two distinct groups: one group is referred to as “main system”, the other group is mentioned as “external universe”. The components
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of the “external universe” are not individually identified, but only mentioned as a whole. Actually, all that which is not identified as a component belonging to the “main system” shall be considered as inherent in a unique “external component” connected with components of the “main system”. Once made the meaning of this distinction clear, nothing prohibits considering also the “external universe” as one special component of the “main system”. The role of the “external universe” has a fundamental importance for any identified “main system”. This is a point to be borne in mind constantly, with a view to avoiding conceptual and logical errors in addressing “isolated systems”, especially when attention is drawn to events that seem in disagreement with the Second Principle of thermodynamics. It’s again convenient referring to a concrete example, in an attempt to clarify how entropy in involved in biological processes as well as to point out how a “biological system” should be identified and defined. Let’s consider a “system” consisting of a glass container (which shall later be closed hermetically), in which a vegetal seed is put together with a handful of wet soil and an atmosphere of oxygen and carbon dioxide in appropriate proportions. If the container is kept at a constant temperature, the seed develops exploiting the surrounding materials and following the “biological programme” of the seed’s genetic code. It will be observed that those materials will organise in the differentiated forms of a small plant, giving so evidence to a process characterised by a decrease in the system’s entropy. The process seems showing that phenomena contrasting the Second Principle are possible. But it’s a misleading impression, for the “system” has been defined in a wrong way. In fact, if the “system” were completely isolated, i.e., if the “system” had no exchange of energy and materials with the relevant environment, the seed would develop its biological programme until its own resources, along with the resources initially provided by the materials inside the container, are available, through the process that on the one hand  produces organised matter and – on the other hand – dissipates heat within the isolated container. However, once all the resources of the “system” had been expended, the implementation of the seed’s biological programme would come to a stop, and a decay process would inevitably start for the “system”, showing a decomposition of the forms of the organised matter in association with a relevant rapid increment in the amount of entropy, up to a final condition of disorderly equilibrium. The development process could instead continue – beyond the utilisation of the resources initially available inside the container – suitably providing the “system” with energy, mainly light, necessary to
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photosynthesis, together with soil, water, oxygen and carbon dioxide, i.e., with all the constituents indispensable to the growth of the plant. In this case the “system” (which so becomes a “main system” as per the relative concept previously defined) is no more isolated: It’s immediately understood that the “system” to consider cannot be only the one inside the glass container. That “system” is only the section of the universe on which our attention did initially focus, in the belief that it’s actually possible to isolate parts of the world from the relevant external universe. There is also to remark that most of the energy and of the materials spent to feed the development of the seed turns into dissipated heat, which corresponds to a noticeable production of entropy. Thus, the diminishing entropy in the “main system” (as shown by the observed vegetal development) is largely compensated by the overall increment in the entropy of the real system to consider, which includes the “external universe”. As a conclusion, it is immediately realised that there is always an “external universe” that acts as an inseparable component of any possible “main system”, and that local drops in the entropy level do not invalidate the Second Principle, as also proved by a large number of experimental tests and calculations carried out about studyexamples of the kind.16 6. Syntropy From now on, it’s convenient to use the definition of statistical entropy as given by Shannon’s formula (see Paragraph 4), adopting symbol “E ” for entropy in whatever form expressed. Shannon’s formula can be written in a simpler and more practical way by use of mathematical symbol “Σ” (capital “sigma” of Greek alphabet), which – in mathematical notation – means “sum”. Example: The addition of 3 different quantities a1, a2, a3 (i.e., the mathematical
One might ask whether it is possible to consider the Sun, or any other star, as an isolated system. The answer is “no”. Star formation depends on the activity of the cores of galaxies, and the formation of matter is basically due to the stars’ activity, while also the cores of the galaxies are states of the socalled “empty space”, which is instead anything but “the void”. The cosmic space must be considered as the unlimited reservoir of a basic essence, call it “energy” or “ether”, “by which all events are born and fed, and into which everything will turn at the end of its own cycle, according to necessity”, as per the cosmic image of the ά̟ειρου (“the Indeterminate”) conceived by Greek philosopher Anaximander in the VI Century b. C. 16
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3
expression for a1 + a2 + a3) , can be written as
∑a
i
, which is read “sum
i =1
of the ai, with index i varying from 1 to 3”. Therefore, Shannon’s statistical entropy can also be written in the following way: N
E = ─ h ∑ ( pi ln pi ) i =1
in which N represents the [usually very great] number of the possible events relative to the probability distribution considered. If number N is known and fixed, and if it’s also assumed h = 1 for the sake of writing simplicity, then the above expression becomes simplified as follows: E = ─ ∑(pi ln pi). By use of symbol “Σ”, also the concept of “probability distribution” can simply be expressed as ∑ pi = 1 . The above formula for entropy E can be applied to any system, to any probability distribution pi , and for any number N of respective possible events. As previously seen in Paragraph 4, maximum entropy (Emax) characterises the state of a system when the internal distribution of the interactions between the system’s components is represented by a uniform probability distribution, i.e., when all the interactions have the same probability p = 1/N to occur, where N is the number of possible events (i.e., the number of possible interactions). Then, in that case, the system’s maximum entropy is expressed by
1 1 Emax = ─ N (p lnp) = ─ N ln = ─ 1(ln1─ lnN) = ─1(0.─lnN) = lnN. N N It has also been shown that any non uniform probability distribution, which is characterised by interaction probabilities that do not have the same value (it’s sufficient that one probability only differs from the remaining ones), brings the system’s state to a level of entropy lower than Emax . Thus, given any value of entropy E which is less than Emax , we can associate a new quantity with the state of the system: It’s a positive quantity expressed by a difference indicated with symbol “S.“ and defined as follows: S = Emax ─ E = lnN ─ E .
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We can call this quantity “syntropy”, because number S measures what of the system’s state has been taken away from disorder. In other words, if the system’s entropy E is not the possible relevant maximum entropy, it means that the disorder does not affect the system completely, and that a certain degree of order – as expressed by S – qualifies the system’s state. Then, given the above definition for “syntropy”, it’s possible to see that – whatever the state of the system – the sum of its entropy and syntropy is a constant value, which is proper to each system and depends on the number of the possible events that characterise the system. This is immediately visible after moving entropy E from the right hand side of the preceding equation to the left hand side, to write S + E = lnN , constant. The clear meaning of this relation is that any increment in the disorder of the system corresponds to an equivalent decrease in the system’s order, and viceversa. Constant quantity “lnN” is referred to as “entropy potential” or “transformation potential” inherent in the system, and is in general symbolised with “H “ when the value for N is not specified. It’s important to note that “entropy potential” H shall not be considered as the theoretical maximum value for the system’s entropy only, since it also represents the theoretical maximum limit for the syntropy in the same system; as it can immediately be seen if in the equation H = S + E entropy E is nil. 17 6.1 – Negentropy: What Does It Mean? By the way, and briefly, it’s worth mentioning and commenting on the term “negentropy”, which is also commonly used to mean something analogous to what is here meant by “syntropy”. I don’t know how the concept of negentropy could be defined in a way that is – for analogous purposes – differentfrom and alternative to the concept of syntropy. Those who use the word “negentropy” – as far as I know – do not indicate any precise formulation of the concept. Sticking to the word, “negentropy” should mean a quantity whose significance is expressed by a numerical value opposite to that of “entropy”, and in mathematics “opposite value” means “quantity
It is proved that both maximum entropy and maximum syntropy (and therefore  also zero entropy and zero syntropy) are only theoretical bordervalues that cannot be achieved by any system. 17
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qualified by opposite algebraic sign”. That is actually what Schrödinger suggested in 1944, upon a harried and questionable interpretation of Boltzmann’s statistical formulation of “entropy”. Assuming that “negative entropy” can explain or describe natural phenomena of order formation and selforganisation seems to me a misleading idea, because such a definition of order leads to contradictory implications. According to the definition of entropy, as it is so far accepted and recalled, “disorder” means amount of unusable energy or chaotic uniformity in the state of a system. This disorder tends to vanish when entropy, which is always a positive quantity, tends to zero; correspondingly, the “order” in the system tends to its maximum level. The order internal to the system is reflected by a highly differentiated distribution of the probabilities of interaction between the system components (if N is the number of the components, N 2 is the relevant number of possible interactions). If entropy is multiplied by “–1” with a view to defining “negentropy” as a measurement of “order”, it happens that the “order” in the system tends to zero, i.e., it tends to disappear, when the internal organisation of the system components achieves its highest degrees of complexity; while, in the other direction, the system’s order tends also to decrease towards a minimum expressed by a quite uniform distribution of interaction probabilities. Consider that “negentropy” is a negative quantity, and negative quantities decrease with the increment of the respective absolute numerical value.18 Summarising: Given a system of N.2 interactions between the N components of a system, “negentropy” – meant as “negative entropy” – implies that the system’s internal degree of organisation is both nonexistent when negentropy is nil, while it is at a minimum level when negentropy equals “–2lnN ”. Meanwhile, the system’s entropy, which expresses disorder, is also nil when the same entropy (positive quantity, normally) is nil. The logical disagreement between such statements concerning states of the system is evident and makes “negentropy” a fuzzy concept. In current languages, concepts of order and disorder do normally hint at “relations between things”, at configurations of objects of a set that can potentially be in alternative states, i.e., in possible states viewed as For example, –5 is less than –2; –100 is much less than – 7, etc.; whence “zero” is the greatest value in the whole infinite set of negative numbers (i.e., “0” is greater than –2, –5, –100, etc). 18
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“opposed” (to mean in mutual contrast) only because of different degrees of recognisable structure in the relations between the objects. Any state of order in the set doesn’t deny the state of disorder, but – to the contrary – uses the idea of “disorder” as “lower degree of order” with respect to the same objects put in alternative states that can exhibit any greater amount of information to the observer. Substantially, with no change in the number and quality of the objects of the set, order and disorder are only ways to compare different combinations of relations between the objects. Any process of transformation of matter may be viewed either as an activity that moves equal or similar objects toward systems that consist of “synthesized and differentiated things”, or – to the contrary – as an activity of demolition and hom*ogenisation of “different things” in a set of objects that are equal or similar to each other.19 On the basis of the above premise it is possible to develop a general theory of the systems that are formed by interacting components. In analysing the evolution of a system, the proposed theory shows a way to describe “progressive” and “regressive” transformations of the system in terms of increasing or decreasing internal organisation, respectively.
7. Description of the Behaviour of a System Aware of the caution that is necessary in defining any observed system, we can now focus our attention on those processes, both of a biological and of a social nature, which develop forms of matter and energy organization. Sticking to the definition of system introduced in the beginning of Paragraph 6, any system can be described as a set of different components (identified as such by the observer), which depend on each other through recognisable and measurable interactions. Within any conventional time unit, each component of the identified system is both source and destination of interactions whose intensity
A rough analogy is useful: Building up houses, factories, churches, theatres, schools, hospitals, etc., of a town means to use bricks – which are all equal to each other – for transforming equal objects into a system (synthesis) of things that are different from each other by shape and function. Demolishing and hom*ogenising those buildings means to reduce them to undistinguishable sets of bricks steadily equal to each other and void of any recognisable function. 19
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varies in general from pair to pair of components. This can be observed between biological as well as nonbiological components. The behaviour of nonbiological systems is dominated by the determinism of a relatively simple mechanics (proper to the mechanism of physics), also when the observation, because of practical conditions imposed by the levels of the observation scales, must renounce deterministic descriptions and resort to statistical and probabilistic methods of analysis. Some sort of constrained determinism is instead observed in biological and social systems, so that the system behaviour – though utilising the “laws” of nonbiological determinism – is subject to a framework of constraints. These constraints work as a programme because of their capacity of conveying the effects of deterministic rules toward a hierarchical selection and grouping of interactions between components of the systems. In this way, the system components come to form a structure of “organised” sections differentiated from each other by character and function, up to the composition of complex organisms, which – from a mechanistic standpoint – are intrinsically unlikely. Moreover, some of such complex organisms have the amazing property to evolve toward forms of higher and higher complexity. However, also the formation and the evolution of the most complex organisms undergo the impact of chance, whose effects may partly be eluded by the “program” and partly modify it. Accidental modifications in the “programme” may either corrupt the “programme”, making it no more effective (with subsequent decomposition of the organism), or mutate the “programme” in a way that allows the organism to resist the accident and to continue its evolution through adaptive processes. (Nevertheless, as so far experienced, chance does sooner or later prevail over “programmed” processes of any kind, and adaptation – though showing an increasing chanceresistance in a growing number of cases – is in the end overwhelmed by chance, i.e., by the domain of entropy. In a partial attenuation of this image of fate, it seems possible to affirm that “improbable processes” that lead to complex systems can rise from “chaotic” states of matter and energy through major stochastic deviations from states of equilibrium, which are never stable. Thus, the triumph of chance should never be considered as a general and definitive end of the story, but only as a local and cyclic event. Proliferation, as a property of living species, might be taken as a tentative example that corroborates the thesis). The capacity of organising matter and energy, which is proper to the evolution processes that are characterised by a behaviour programme, is always associated with the dissipation of most of the involved energy;
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so, the amount of order achieved is over counterbalanced by the production of a corresponding amount of disorder. Summarising: a “system”, like that defined  for example  by a complex organism, must always be considered as formed by the specific components of the complex organism and by the respective external environment, here referred to as “external universe”. The salient property of any system is that all its components are active. The interactions inherent in any system are both those that occur between different components of the system and those of each component with itself. In this connection, it’s worth remarking that also the “external universe”, which is one of the system’s components, develops inside itself an amount of activity that is caused by (or depends on) its relationship with the “main system”. It’s also important to remark that the “external universe” relevant to the identified system is only that part of the universe that undergoes the influence of the particular “main system” addressed. Upon the assumption that the interactions between the system’s components are all identifiable and measurable, the description of the system’s behaviour becomes simpler than one could expect. In analysing any system, the observer is used to focus his attention only on those interactions that are deemed to be significant. In describing the behaviour of the system, it is supposed that the interaction flows (as observed in a given time unit) are methodically measured by use of a measurement system that makes the interactions hom*ogeneous quantities, in order to make any one of them comparable with the other ones. Which also implies the possibility of calculating the aggregate amounts of interactions produced in the system per time unit. Subsequently, all the interaction flows can be converted into interaction probabilities, to exploit the analytical advantages provided by the mathematical properties of probability distributions. Besides any possible discussion on the meaning of this kind of probability sets, the percent values expressed by probabilities are significant enough to justify the relative use in the analysis. Actually, interactions expressed in the form of “probabilities” are particularly useful to the purpose of associating quantities like entropy or syntropy with the system’s states. 7.1 – Shape of the System and “Constrained Uncertainty” The description of the behaviour of a system depends principally on the criteria adopted for identifying its components.
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The identification of the components is naturally associated with the distribution of the interactions within the system. For example, if the task is to analyse the behaviour of a human social system, the shape of the system depends on whether this is considered as formed by individuals or, instead, by different groups identified by class of characteristics shared by individuals. Suppose that the interactions are expressed in terms of exchange of information in any form. Though the total amount of interaction, per any given time unit, depends only on the number of persons involved, it is clear that the distribution of the interaction flows between components varies from a system whose components are individuals to a system whose components are groups of people because of the changes in the number and size of the system’s components. The importance of the preceding remarks is in that – just because of the identification of the components that form the system – it’s the observer to determine a first basic level of order in the system observed. Therefore, it’s the observer’s attention and decision that establishes the shape of the system, on which the measurement of the interaction flows will depend. I am now trying to clarify the meaning of that I have affirmed above, starting with a matrix of unitflow distribution within a hypothetical system consisting of 6 components. The matrix is shown by the table below, in which letters a, b, c, d, e, f represent the system’s components. The series of “1” in the horizontal lines (the rows) of the matrix indicate the intensity of the flow (for the sake of simplicity, every interactions flow is made equal to 1) sent by each component to the other components of the system, so that the series of “1” in the vertical lines (the columns) of the matrix represent the flows received by each component. (The unitflows that are disposed along the matrix diagonal  on lightyellow colour background  show the set of “selfinteractions”, i.e., the interactions of each component with itself).
1 1 1 1 1 1 → 1 1 1 1 1 1 → 1 1 1 1 1 1 →
6 6
a
6
c
1 1 1 1 1 1 → 1 1 1 1 1 1 → 1 1 1 1 1 1 → ↓ ↓ ↓ ↓ ↓ ↓
6 6
d e
6 ↓
f
b
6 6 6 6 6 6 → 36 Tot. a b c d e f Tot.
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Such a hypothetical “system” presents quite a uniform distribution of interaction flows, as it must also be considered as typical of any “wouldbe system” about which the observer has no information. Apart from the exemplification choice of a uniform interaction distribution characterized by unitflows, any other uniform distribution of flow intensities would be converted into a unique identical probability distribution, which – for any sixcomponent system – would consist of interaction probabilities all equal to 1/36. At this point a clarification is indispensable. The identifications of active components, which differ from one another and interact with each other, depends on the cultural formation of the observer. The aptitude for recognizing any system is a shaping activity of human mind, which can seize the existence of relationships between the identified components. Such relationships entail interaction flows whose intents are nonnull in principle, but – as in the case of the hypothetical system represented by the matrix above – they are instead unknown to the observer because of a total lack of information about them. Uniform interaction distribution, like that in the example, means only that the observer cannot at once distinguish the interactions’ intents from one another. The entropy associated with a wouldbe system of events like that described by the example matrix is the maximum entropy that can be associated with the system, and is expressed by the system’s “entropy potential”, whose value – using Boltzmann’s equation – is in this case H6 = ln(62) = 2 ln6 = 2 x 1.79176 = 3.58352. (Instead of “maximum entropy“, it would in general be more correct to speak of higher extreme of entropy level, which establishes the border condition in which any “system” can no more exist). If Shannon’s formula is applied to the probability distribution relative to the same system, all the probabilities being equal to 1/36, the entropy calculated as “statistical uncertainty” results in E6 = ─ 36 x (1/36)ln(1/36) = 3.58352 , which verifies that entropy and uncertainty do actually coincide, i.e., that H6 ≡ E6 . At variance with the previous situation, suppose now that some kind of available information allows the observer to group the same components of the system in 3 – instead of 6 – new different components identified as A, B, C, in the way that will be shown by the next matrix. In the new matrix, which modifies the preceding one, component A groups
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previous components a, b, c; component B groups previous components d, e, whereas previous component f remains alone to form the new component C.
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1 1 1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
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1
↓
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↓
18
12
6
A
B
C
→
18
A
→
12
B
→
6
C
↓ →
36
Tot.
Tot.
In the system so redefined, the interaction flows between A, B and C are no more equal to each other: A sends 6 flow units to B, 3 flow units to C, and retains 9 flow units for itself. Component B sends 6 flow units to A, 2 flow units to C, and retains 4 flow units for itself. In turn, component C sends 3 flow units to A, 2 to B, and retains 1 unit flow only for itself. The account of the flows received by each component is symmetrical to the former. The probability distribution that corresponds to the new flow distribution is given by the following series: 6/36, 3/36, 9/36 (relative to A); 6/36, 2/36, 4/36 (relative to B); and 3/36, 2/36, 1/36 (relative to C). The sum of these probabilities is obviously equal to 1. By application of Shannon’s formula for entropy one obtains: EABC = — {(6/36)ln(6/36) + (3/36)ln(3/36) + (9/36)ln(9/36) + (6/36)ln(6/36) + (2/36)ln(2/36) + (4/36)ln(4/36) + (3/36)ln(3/36 ) + (2/36)ln(2/36) + (1/36)ln(1/36)} = 2.02288 . The entropy potential relative to this new system of 3 components is given by HABC = ln(32) = 2.x.ln3 = 2.19722 , which, as expected, is greater than entropy EABC calculated above, while both EABC and HABC are lower than E6 and H6, respectively. In this case, at variance with the previous case, the system’s entropy doesn’t coincide
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any more with the entropy potential. The difference between the two quantities, expressed by SABC = HABC — EABC = 2.19722 — 2.02288 = 0.17434 , is the amount of order that has been introduced by the observer in reshaping the 6component system into the 3component one. Substantially, it’s the effect of the amount of information that has been introduced by the observer. Some important conclusions can be drawn from the preceding considerations. The first one is the following: In any state of the system, the “natural” tendency to disorder is partly checked by the presence of “constraints”, which drive the system toward a behaviour whose randomness declines with the number of different “constraints” affecting the system. In the preceding example, the only information used by the observer has implied a constraint to the shape of the system. This is not banal, for the amount of order in the system is detected in connection with the constraints to which the system is subject according also to the observer’s knowledge. To help us to grasp the importance of constraints in determining the behaviour of real systems, it’s worth making at least one example, which puts the role of the observer in a shade. Consider the following system: A mixture of hydrogen and nitrogen is put into two interconnected containers. If the temperature is the same for both containers, the molecules of the two gases blend together randomly, so that the mixture distributes within the two containers almost uniformly. See the sketch that follows. ●
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separate, with one of the two gas gathering in one container and the other gas in the other container, as schematically shown by the graph that follows: ●●
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In this way, the system has been subjected to a thermal constraint. On the one hand, in doing so, energy is spent and dissipated to determine the difference in the temperature of the two containers; on the other hand, the system achieves a certain degree of order, in the form of a marked differentiation in the positions of the two gases. (The phenomenon is known as “antidiffusion”).20 In general, the various characteristics of different molecules constrain these to respond in different modes to the action of external agents, with the consequence that different kinds of interactions cause different effects, which in turn work as additional constraints with respect to the properties and the behaviour of different kinds of matter, and so forth. In analyses that concern social and economic systems, in particular, the importance of constraints is given evidence by the possibility that constraints offer to solve problems otherwise impossible of solution, according to procedures based on the determination either of the maximum amount of constrained entropy or – equivalently – of the maximum degree of syntropy that the studied system is allowed to achieve under the given constraints. 7.2 – Using Entropy and Syntropy: a Practical Example Out of physics, one example of practical use of the concepts of entropy and syntropy, as discussed in the preceding paragraphs, regards the analysis of economic systems and the description – as possible on the basis of hypothetical (or actual) measures of economic policies  of probable evolution processes for such systems. I deem this digression useful to point out the operational aspect of the concepts
20
This example has been used by Ilya Prigogine during conferences of his.
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introduced, with a view to minimising the dose of abstraction that is normally associated with unusual theoretical terminologies. Schematically, any modern economic system consists of a many different activities, which exploit various resources, including manpower, to produce commodities and services bound for the internal market (i.e., for the market formed by the “main system”) as well as for the external market (i.e., for the “external universe”). In turn, the “external universe” does also sell some of its resources and products to various activities of the “main system”. Econometrics is an important discipline that applies statistical and mathematical techniques to the analysis of economic activities. This branch of economics, during the 20th century, has also introduced methods for the quantitative study of regional and national economic systems. Economist Wassili Leontiev (19051999) developed a method for the analysis of very large economic systems, which soon became renowned and adopted, for decades, by the national statistical bureaus of most developed countries. Leontiev’s method allows for the systemic relationship between the production of each type of economic activity and the relevant sales of products to other types of activities of the same economic system. Leontiev proposed a simple direct proportionality between the various amounts of production sold by each economic sector and the total production of each other purchasing sector. By this criterion, he could construct a numerical table (matrix) of interindustrial relations, which basically consists of proportionality coefficients, usually mentioned as “production technical coefficients”, to be assumed as constant values. The idea is simple. Consider, for example, that to produce A tons of steel, it is necessary to buy KA kilograms of coal, IA kilograms of iron mineral, WA watts of energy, MA hours of manpower, FA dollars of financial services, TA dollars of transport, etc. Leontiev’s method assumes that the numerical ratios defined by KA./A, IA./A, WA./A, FA./A, TA./A, etc., keep constant with time and for any amount of A, quantities K, I, W, F, T, etc., being in turn the overall productions of other activity sectors of the same economic system. The assumption can be summarised saying that the purchase of the various production factors necessary to Sector A varies in a direct proportion to the variable quantity of the total product A regarded. In principle, it’s quite a reasonable assumption. Therefore, for example, the constant technical coefficient aKA is used to express the portion KA = aKA.A of the product of Sector “K” sold to Sector “A”.
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By another example, if the sales of the “energy sector” to the other sectors is constantly proportional to the production of each sector, then the overall production of the “energy sector” can be expressed as a sum – in the form of a linear combination – of the other sectors’ production, each different sector production being multiplied by the respective technical coefficient of energy purchase. Analogous obvious considerations apply to any production activity in the system; so that a set of simple interrelated linear equations can be written to describe the system’s relationships by which each activity is tied to every other one. In this way, it is possible to calculate, for example, the extent to which a possible alteration in the production of any economic sector depends on alterations in the production of any other activity sector in the system. In order to establish all the numerical values of the production technical coefficients, it is necessary to carry out an initial statistical survey to see how the product of each activity sector distributes among the other sectors. Unfortunately, one of the major inconveniences that affect Leontiev’s method is just the need for relatively frequent updating statistical surveys. Leontiev’s method, also known as InputOutput Analysis, emerged as quite an innovative approach to the analysis of complex evolving systems, as large economic systems undoubtedly are. The method shifts the attention of macroeconomists from abstract theories based on axioms to simple and efficacious algebraic processing of real data collected on the field. However, the method involves technical problems just because of the hypothesis of direct proportionality between purchase of resources and production (i.e., the proportionality between input and output), and because of the hypothesis of “constant” proportionality. First issue: The hypothesis of direct proportionality between input and output is quite reasonable and acceptable if it regards the activity of one single factory, farm, service, etc.. But it becomes more and more questionable when every “economic sector” aggregates a large number of activities that may certainly be considered as akin to each other, but which also differ from each other because of differences in kind of product, in production technology, in innovation pace or seasonal sensitivity, etc. Leontiev’s method necessarily requires that the definition of each economic sector consist in aggregating as many production centres as possible, with a view to limiting the description of the system to the use of a number of equations not exceeding – at the worst – one hundred or little more. It is a method that cannot be applied to a system whose
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components are identified in every single activity (one sector per each type of farm, one sector per each type of mine, one sector per each type of factory, one sector per each type of office or shopping centre, and so on), because the matrix of the interindustry transactions would otherwise become monstrously large, regarding many thousands of interacting components. The relevant set of equations would also become unmanageable, even by use of supercomputers, because of immanent limiting conditions of an algebraic nature, which can in no case secure significant solutions to the equations.21 Moreover, such an extreme disaggregation of the activities of an economic system would also be conceptually mistaken and misleading, for the components of any complex system must distinguish from each other only according to the respective specific functions within the system they form. This means that two or three different metalmill factories cannot form three separate sectors within the same system, since they actually belong to one and the same economic sector. If the equation set is not too large, the algebraic inconveniences can somehow be managed through cycles of reiterated adjustments and corrections in the values of the technical coefficients, in conjunction with simpler or more appropriate reaggregations of activity sectors. This becomes practically impossible (and is in no case advisable), if the number of sectors and equations is too high. Let alone the difficulty of detecting and measuring millions of interaction flows. Second issue: Once the grouping of economic activities results in a “reasonable” number of different sectors (say fifty to sixty sectors), the hypothesis of linear proportionality between inputs and outputs reveals a misleading conceptual strain, especially if it is associated with the hypothesis of constant technical coefficients of proportionality. In practice, the only way to measure interindustry transaction flows is through the statistics of the relevant monetary flows (payments), which obviously reflect the continuous fluctuations of the market prices. As known, the value of any production factor is quite a different thing with respect to the price of the factor. It is possible that the quantities of production factors (i.e., the respective production values) necessary to yield a unit of any final product remain constant for a relatively long period, but it’s unlikely that also the respective prices keep constant
Systems of linear equations may provide either positive or nil and negative solutions, the solutions of the latter kind being of no significance and use with reference to economic production activities. Unfortunately, there is so far no mathematical theorem to prove the existence of general conditions that could secure positive solutions only. 21
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during the same time. But just this is the point: if the technical coefficients of proportionality are not constant quantities, Leontiev’s method makes no sense, both from a logical and from a practical point of view. Third issue: InputOutput Analysis is “static”. Once an alteration in one or more of the sector productions has been introduced in the equation system, the “response” of the economic system is as if it were immediate. In other words, Leontiev’s method is not fit for describing the reaction chain of effects that promote a possible evolution of the economic system, as it is instead expected in consequence of modifications in the behaviour of one or more of the system’s components. The effects described by the method are all simultaneous and definitive, in a certain, immediate and stable restoration of the system’s equilibrium after any possible attempt to modify it. Naturally, as many have proposed and tried, the inputoutput analytical paradigm may be complicated at will through the introduction of additional hypotheses and by the aid of various mathematical techniques. On such a path, however, far from making the method more effective, one enters an entanglement of mathematical procedures, which rest on questionable assumptions basically deprived of concreteness. Leontiev’s substantial idea becomes much more fertile if one approaches the study of large economic systems by a probabilistic approach and through the use of the concepts of entropy and syntropy. These concepts are quite appropriate in describing an economic system, which is the most visible example of aggregation, organisation and development of human communities, i.e., the largest example of biological societies. As to any economic system, it is licit to suppose that the boost to produce, or the cause of the economic sectors’ production, i.e., what could in general be referred to as “the intent” of the production activities, is in the expectation of benefits. It’s a way to say it is licit to suppose that the interactions between economic sectors are not randomly distributed, but occurring in view of expected effects. Obviously, everybody knows that the production of any thing, be it a fruit from cultivated land or an hour of human work, aims at pursuing economic ends, which usually consist of clearly identifiable benefits. An interesting aspect of the theory proposed in the previous section of this book is the following: Once the statistics of the transaction flows between the activity sectors of a regional economic system are known,
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an appropriate average amount of “intent” (which in general differs from one interaction flow to another) can be associated with each interaction unit by use of the same flow statistics.22 This makes it possible to determine future changes in the whole system upon even minor changes in one or more of the activities considered. In other words, it is possible to simulate the most likely evolution of the whole system during subsequent years in terms of calculable effects caused by alterations in the system’s present state. According to the proposed theory, any complex system, obviously including any economic system too, is intrinsically unstable, with the consequence that any minimal permanent alteration in the system’s equilibrium generates a chain of feedback effects that lead to the transformation of the system’s activity and structure. All the preceding statements can be proved only by means of a complete exposition of the relevant mathematical theory, as shown in the first part of this book.
8. The Syntropic Evolution of a System The salient characteristic of the probabilistic method proper to the proposed theory is in considering that no system can attain a permanent equilibrium state, to mean that permanent stability is a condition intrinsically impossible for any system. The analytical approach is based on the principle that any natural system of events is in itself an unstoppable “dialectic” process between order and disorder, between syntropy and entropy. Any equilibrium state that can be observed shall always be considered as intrinsically unstable, as an accidental and ephemeral stasis. The proposed new method includes equations apt to describe and calculate the overall series of changes that involve the system’s interaction distribution, up to the determination of the necessary transformations in the structure of the system. The process develops according to transformation cycles, which bring the system from unstable equilibrium states to subsequent unstable equilibrium states, which are characterised by different levels of syntropy, i.e., by different levels of internal organisation. The process, as described by the mathematical simulation, has a “dramatic” development, since in every situation in
Concerning the application of the theory to regional economic systems, see also Chapter 5 of the preceding section of this book. 22
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which the system needs to transform there is also a chance for its disintegration. In simpler words, the actual transformations in the system are those that imply changes in the system’s structure; this structure consists of the set of expectations (the intents) that works altogether as the system’s “engine”. A sequence of convenient changes in the structure of the system is indispensable to the system’s survival. In the evolution of any system, there is a series of crucial points, at which either the system changes its structure suitably or the system incurs its disintegration. In between two subsequent equilibrium states (also referred to as “standing states”), the evolution process is described by transition phases, in which the system’s configuration, i.e., the interaction distribution, involves changes in the flows, which – however  do not involve changes in the system’s structure. In every sequence of transition phases there is always a “critical phase” that concludes a “transformation cycle”. The description of the system cannot proceed beyond that “critical” phase, because the solution of the simulation equations leads to complex values (imaginary numbers), which logically relate to a “no more existing system”; unless the calculable changes in the system’s structure, as associated with the critical phase, allow the simulation to establish the intervened conditions for the system’s survival.23 Such conditions describe the newly achieved “standing state” of the system, from which subsequent transformation cycles may start either according to chance or according to programs. The sequence of the “transition phases” of every transformation cycle shows how the system’s contents of syntropy and entropy vary from phase to phase, often through ample oscillations in the respective values. A collapse either of syntropy or – more often – of entropy is in most cases associated with the last and “critical” phase of every cycle.
The structure that “rescues” the system can always be calculated, either on the basis of the interaction flows proper to the “critical phase”, or else on the basis of the interaction distribution relevant to any other phase of the same cycle. The choice is strictly depending on the nature and purpose of the simulation exercise. The possibility of choosing the most convenient transition phase for transformation purposes is particularly important in planning activities, in which the simulation can in this way suggest the most appropriate components and functions to be selected for promoting development. In such cases, the choice of the transition phase that transforms the system’s structure is obviously based on the level of syntropy that the transformation can secure.
23
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The system’s evolution is development if the “standing states” of the sequence establish at higher and higher levels of syntropy. It’s instead recession if the sequence shows “standing states” that establish at higher and higher levels of entropy. It’s also possible that the described evolution develops according to alternate sequences of “development” and “recession”, with alternate sequences of different standing states at higher and lower levels of syntropy (as it might be the case simulating the alternate effects of good or bad socioeconomic policies implemented). The analysis also shows that a higher syntropy level implies a higher degree of stability for the system, “stability” meaning here “lower risk of putting the system’s organization in jeopardy”.24 It is an important theoretical result, for it seems possible to argue that the basic aim of the system’s complexity consists in achieving higher degrees of stability. Actually, higher syntropy means higher complexity, as this is shown by systems characterised by a wide variety of different activities, which are as more differentiated by function as more mutually interdependent. One significant aspect of complexity is that the “main systems” of complex systems, along with the respective higher degree of complexity, achieves higher degrees of autonomy with respect to the “external universe”. Higher degree of autonomy means also greater selfprotecting capacity against possible external or internal events – be these immanent or accidental – which can jeopardise the system’s existence. The degree of complexity of any system finds its limit in the value of the entropy potential associated with the system, because the entropy potential depends on the number of different functions performed by the different components that characterise the system. When the system’s syntropy approaches the value of the system’s entropy potential, the system enters a stage of relative stagnation. It’s a particular state that can be broken  either by accident or by program by the emergence of special new conditions, which lead the system to undergo a “mutation”. There are progressive mutations as well as regressive mutations. A progressive mutation occurs because of the emergence, inside the “main system”, of one or more new components, which perform kinds of activity that are not performed by any one of the system’s existing components. An emergence of this kind implies an extension of the system’s size.
24
As to the concept of “stability” in this context, see also Page 175 ahead.
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Instead, a regressive mutation occurs if one or more of the existing functions of the “main system” disappear, with no replacement, along with the respective components, thus also reducing the system’s size. It may obviously happen that a “regressive mutation” is ineffective, when the disappeared functions are simultaneously replaced by new emerging ones; and it may also happen that one or more “progressive mutations” occur after one or more “regressive mutations” have occurred, or viceversa. One of the interesting conclusions provided by the theory of syntropic systems concerns the actual meaning of the concept of “syntropy”. If one denotes with F the total amount of the effects that are expected in association with total amount T of the system’s interactions, the system’s syntropy, S , can also be expressed as S = λ F/ T . To make it clearer: If it’s an economic system, then syntropy S expresses the mean expected benefit associated with any interaction unit (λ is a constant that depends on the measurement system adopted). It is worth mentioning that beside entropy and syntropy a number of other parameters, such as “stress”, “phase and state strength”, etc., are also considered, which are useful indicators of the condition that characterises the state of the system during its evolution. Such indicators are said “phase parameters”, and are especially useful in the practical applications of the theory for an evaluation either of the effectiveness of a progressive evolution or of the damaging consequences of a regressive evolution. Actually, both improvement and worsening in the system’s state may imply different costs, which can make the improvement more or less effective, and the worsening more or less onerous, respectively. The total amount of entropy produced by the system during its evolution concurs in determining the system’s age. 8.1 – Syntropy, Stability, and Impossible “Chaos” In the preceding paragraph, I have introduced the concept of “stability” as a quantity related to syntropy. I deem it useful to linger on the subject to avoid possible misunderstanding. As previously stated, every complex system may be viewed as a set of interacting components. Each component generates and receives flows of interaction. Let’s call “output” the total amount of flows generated by each component, and “input” the total amount of flows received by each component, “flow” meaning “quantity of interaction (generated or received) in a conventional time unit”.
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Consider a hypothetical economic system whose outputs are denoted with letters “D”s and the inputs with the “A”s, all these symbols being affected by a numerical index relating each of them to the relevant economic sector. The set of all quantities “A”s and “D”s , taken all together, is referred to as “the system’s base”. The system’s “stability” depends on the distribution of the sector outputs and inputs in relation to the system’s total interaction activity, to say that the stability depends on the configuration of the system’s base. Given any system of interacting components, it should be evident that the sum of the outputs is always equal to the sum of the inputs. It’s worth fixing this point by the simple equivalence expressed by N
N
∑D = ∑ A i
i =1
i
=T
i =1
in which T is the system’s total interaction activity (i.e., the sum of all the system’s interaction flows), and N is the number of the system’s components, “external component” included. After division of the above equivalence by T, the following obvious relations can be written: N
N Di Ai = = 1. ∑ ∑ i =1 T i =1 T
In this way, two new probability distributions have been defined, D one of which regards the set of output probabilities defined by ratios i , T and the other one regards the set of input probabilities defined by ratios Ai . Then, it is possible to associate an entropy (i.e., a statistical T uncertainty) with each of the two probability distributions. Let’s call “output entropy” the former and “input entropy” the latter, according to the following formal definitions N
Eout = – ∑ ( i =1
Di Di ln ) , T T
and N
Ein = – ∑ ( i =1
Ai Ai ln ) , T T
respectively . The system’s “base entropy”, denoted with E*, is the sum of the two above entropies, i.e., E* = Eout + Ein .
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In preceding Paragraph 6, the system’s “entropy potential” is defined by H = 2lnN . Therefore, in correspondence with the “base entropy” just now defined, it is also possible to identify the “base syntropy” given by the difference between the entropy potential and the base entropy, as follows : S* = H ─ E* . It can be proved that this quantity indicates the degree of stability of the system. Making the preceding definition explicit, base syntropy S* (or stability S*) can also be expressed by
N2 S* = ln E* , e in which N is the number of the system’s components and E* is the relevant “base entropy”. From this formula it’s easy to deduce that the system’s degree of stability tends to grow both with the number of different components and with the lessening of the base entropy. In this connection, it’s significant to note that the greater the number of different components the greater the system’s complexity. It can also be proved that the system’s stability is in a direct relationship with the system’s syntropy S. However, the stability (which is only defined by the base syntropy) may remain constant in association with different levels of the syntropy S relevant to various configurations of the interaction flow distribution. Actually, the distribution of the N 2 interaction flows between the N components of the system may vary in infinite different ways with no change in the respective base syntropy. This reflects the fact that each output, as well as each input, results from the addition of N interaction flows, whose individual values may change while keeping their sum constant. Moreover, the distributions of the outputs and inputs may in turn change too, provided that the respective base syntropy remains constant to keep the system’s stability unchanged. Consider now a system that consists of 6 different components (i.e., N = 6), in which the 6 outputs and the 6 inputs present a distribution like that shown by the following table:
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Outputs (D) and respective Inputs (A)
Outputs and Inputs 2,5 2
D1 D3 D5 A1 A3 A5
1,5 1 0,5
D2 D4 D6 A2 A4 A6
0 D1
D2
D3
D4
D5
D6
A1
A2
A3
A4
A5
A6
Active components of the system
The above distribution configuration as such may remain unchanged even if there are alterations in the output or in the input values relative to individual components. In the next table, alterations are shown in the individual amounts of output and input: for example, the previous output and input of Component 1 (i.e., D1 and A1) have now been assigned to Component 3; the previous output and input of Component 2 (i.e., D2 and A2) have now been assigned to Component 1, and so on.
Outputs and Inputs Identical distribution scheme with different assignment per component OUTPUTS AND RESPECTIVE INPUTS
3 2,5
D3 D4 D2 A3 A4 A2
2 1,5 1
D1 D5 D6 A1 A5 A6
0,5 0 D3 D1 D4 D5 D2 D6 A3 A1 A4 A5 A2 A6
Active components of the system
What remains unchanged is the way in which the outputs and the inputs form the partition configuration of percents of the system’s total activity T. (By the way, to stress the importance and the prevalence of the configuration over the individual quantities regarded, in the new table all
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the outputs and the inputs have been increased 10% with respect to the values indicated in the previous table). The persistence of the percent partition configuration is a sufficient condition to keep both base entropy E* and base syntropy S* unchanged. Which obviously means keeping the system’s stability constant. However, it should be clear that there is an infinite number of possible different percent partitions which can keep E* and S* unchanged along with the system stability. The correctness of this statement can easily be guessed, considering that the output and input distributions reflect the respective probability distributions, which are all that which matters in determining the system’s base entropy and base syntropy. The preceding remarks show how the components of a system may exchange the importance of the respective roles without compromising the system’s stability. It’s a feature of flexibility proper to complex systems: These can cope with sudden difficulties encountered by some of their components, through a balancing supplement of functionality (or hyperperformance) provided by other components. It’s now worth going back to the equation that defines the stability (S*) of any system, with a view to pointing out an important logical implication of the concept. Consider a system with no detectable internal organisation, as it happens when the assessed interaction probability between any pair of components is the same as for every other pair of components, including all the “selfinteraction” probabilities; an example of such a situation is represented by the first matrix of previous Paragraph 7, regarding a hypothetical 6component system. Let’s refer to any state of this kind as to a “chaotic state”. The base of any system of that kind is characterised by two particular outputinput probability distributions, with which two identical entropies can be associated and expressed by the following equivalence: N N N N Eout = Ein = – ∑ ( 2 ln 2 ) = – N 2 (ln 1 – lnN ) = lnN . N N i =1 N The entropies of the two semibases 25 of any “chaotic” system are identical to each other. As previously seen (refer to the formula in Page 174), base entropy E* is the sum of entropies Eout and Ein , so that – in the case of a “chaotic” system – base entropy E* becomes
The output set is one of the two “semibases”, and the input set is the other semibase of the system. Therefore, the system’s base consists of the union of these two semibases. 25
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E* = Eout + Ein = lnN + lnN = 2 lnN , which coincides with the ”entropy potential“ of the system (remember that is H = 2LnN ). Therefore, according to the definition given for the system’s stability S*, the stability of any “chaotic system” is expressed by S* = H – E* = 2 lnN – 2 lnN = 0 . It’s a remarkable result, for it shows that the stability of “chaos” is nil, if “chaos” is a concept used to mean “maximum uncertainty” or “uniform disorder”. In other words, no system can either persistin or enter a state of maximum disorder. Paradoxically, it is right ”chaos” the state of maximum instability, at variance with expectations associated with a common interpretation of the second principle of thermodynamics. This conclusion clarifies also the concept of “maximum disorder”. Should such a state be possible, then it would be characterized by an absolute uniformity in the behaviour of the system’s components, so making these not distinguishable from each other. To conclude that no system exists without recognisable differences between the interacting components of which the system consists. Thus, through simple logic reasoning, it is possible to ascertain that the identification of any system implies also and necessarily the identification of a “structure” that binds the system’s components to each other because of the respective behavioural differences. The “structure” is always a network of relationships between different roles. Whatever the nature and the state of the system, its active components exhibit similarities and differences in their individual condition, which inevitably leads to determine the formation of both aggregations and separations between components, with the subsequent modification in the intensity and distribution of the relevant interactions. In turn, alterations in the relationship network establish “constraints” to the behaviour of the whole set of the system’s components, so starting evolution processes that may bring the system organisation to higher degrees of complexity or, to the contrary, to its decomposition. The preceding considerations should allow anyone to view that any apparent equilibrium state is intrinsically unstable. That’s why the concept of “stability” – far from meaning “static state” – takes in this context the specific meaning of probability for the system to modify its state, such a probability being always greater than zero. In this connection, it’s worth recalling the objections to Boltzmann’s entropy raised by physicist Josef Loschmidt (18211895) and by mathematician Ernst Zermelo (18711953).
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Though persuaded of the necessity of adopting a molecular interpretation of entropy, Loschmidt based his objection on the symmetry of the laws of mechanics with respect to the inversion of the time direction: Whence there must be the possibility of processes opposite to those that bring systems to maximum entropy states, with the consequence that states of increasing order – or decreasing entropy – shall necessarily be allowed for. The objection raised by Zermelo is instead based on a theorem proved by mathematician JulesHenri Poincaré (18521912). The theorem proves that interacting particles (whatever their nature), whose interactions occur through forces depending on the particles’ spatial positions, modify their overall configuration almost periodically, in that the same configuration reappears necessarily – and alternatively – according to determinate time intervals. The theorem excludes the possibility of proving the irreversibility of physical processes on the basis of mechanics, for any possible physical state tends to reconstitute periodically. Boltzmann replied to Zermelo, remarking that the time necessary to any macroscopic system to complete the full cycle up to the reconstitution of its initial state is longer than the universe’s age; so, Zermelo’s objection has no practical relevance. Nonetheless, the justified objections raised to the Second Principle of thermodynamics have cast doubts as to the irreversibility of physical processes. Also quantum mechanics and, more recently, theory of chaos show that no definitive certainty shall be associated with the Second Principle. In the light of the foregoing, it seems appropriate dismissing the idea that the final state of every physical process shall consist in the static equilibrium “proper” to maximum entropy states. On the contrary, the probability of inverting the entropic tendency does certainly increase with the entropy level itself, especially when the system’s state approaches the system’s entropy potential, which – as seen – is a state of “absolute instability”. Inversion of the tendency implies the beginning of syntropic processes, by which systems become more and more complex and “stable”. In any syntropic process, stability means capacity to preserve effective connections both between functions and active components, rather than capacity to preserve the individual components in their respective roles. In a complex system, it’s the system of functions and relevant connections the aspect that matters, whereas any active component may be replaced by any other component that can take over the necessary function in place of the replaced one.
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This aspect of complexity makes it clear how the system’s components shall be considered as different from each other, independently of the respective individual nature and/or feature.26 Thus, it’s reasonable to affirm that syntropic processes appear spontaneously and necessarily, though the rise of syntropic boosts may seem in nature to be less showy and less impelling than entropic tendencies. Finally, it seems also evident that there is an immanent “dialectic” between entropy and syntropy, at least because it is impossible to conceive any idea of “disorder” without the complementary idea of “order”.
9. Conclusions The general premise is simple: Today’s knowledge allows us to associate a level of entropy with the state of any system by use of a precise mathematical formula. If, in any system, the entropy level, i.e., the degree of disorder, is not at the relevant maximum, then it must be assumed that a “balance of nonentropy”, i.e., some degree of “order” can also be associated with the same system. Such a “balance” is taken as a measurement of the “syntropy” inherent in the state of the system. In this way, “syntropy” – as degree of order – remains defined as a quantity complementary to entropy; so that the sum of syntropy and entropy gives a constant value, which indicates the transformation potential (the “entropy potential”) proper to the system. Every form of knowledge is tied to the use of languages. These are the basis of our mental and material representations of the world’s physical reality with which we are in touch, nay, in which we are fully immersed. Our inborn inclination to notice “norms” or “regularities” in the events we observe or perceive belongs to behavioural springs of spontaneous organisation that are intrinsic to the cosmos, which is right what we perceive as such (κόσµος, “universal order”) in opposition to all that which is not yet perceivable as “order” or “regularity”.
A banal example: An industrial factory is a “main system” in which some roles previously performed by human workers can be taken over by machines, while the control on labour performance changes into control on machine performance. The functions remain, notwithstanding the radical changes in the nature of some of the system’s components. 26
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We are used to observe the formation of complex physical systems that develop along with the symptoms of their eventual decomposition, whence our need to understand the reasons for the rise of those systems and for their subsequent conversion into selfdemolition processes. We live still in the dawn of the reasoning species, which is still affected by epidemics of faulty imagination, due to the metaphysical basis of any attempt to rationalise our painful ignorance. This is the source of myths that should help us tackle undesirable events we dread, but our mythologies are instead throughout the world the cause of havocs that are much worse than those havocs we should overcome thanks to the practice of our myths. Since a couple of centuries, the scientific analysis of a few observed phenomena allows us to control them. One of the salient traits of scientific method is the use of mathematical calculation, which permits together with a widespread possibility to reproduce the phenomena that can be subjected to our control  reliable forecasts in a significant number of cases. Mathematical calculation is a system of procedures based on the exploitation of linguistic similarities, analogies and tautologies. It provides us with an instrument – very often inadequate – to check (as far as possible) the damages caused by ideologies and by mythologies. No doubt, mathematical language is amongst the few effective languages to use also for description purposes, and it certainly is the most effective language to use for calculation purposes. In presenting the elementary formula that makes syntropy the quantity complementary to entropy, I have also extracted comments that lead to some of the relevant logical implications. Among these, there is the analytical instrument that describes the probable evolution of social or economic systems observed in phases of lost equilibrium, though considering that any “equilibrium state” is a conventional and transitory condition, which inheres in our language mechanisms rather than in the real world observed. The approach to the subject, as proposed here, must basically be considered as a methodological proposal. No method can acquire scientific character until it is proved effective in repeated applications to real cases. There are schools of thought and research, particularly the one that refers to the activity and the teaching of Ilya Prigogine and collaborators, which have worked out other methods to deepen the study of complex systems. The complex phenomena that seem to escape the Second Principle of thermodynamics have been called “dissipative structures” by Ilya Prigogine, because of the large amount of entropy spent in the
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formation of organised material systems. A new discipline, referred to as Synergetics, has originated from those researches. It involves complicated analytical methods and aims at ambitious targets, though the theorems of Synergetics have not yet been applied successfully to the study of any biological system. Other schools of thought use more or less sophisticated mathematical instruments and jargon that focus on “complex systems” conceived as webs or network systems, i.e., each system viewed as a connection infrastructure, which develops according to the needs of the infrastructure’s users (in some cases) or according to the theorists’ expectations (for the rest). There is a plethora of papers flowing continuously from both individual researchers and cooperatives of coworking authors concerning network formation, functioning, transformation and growth. From another particular side of the world, a host of computer fans and hyperskilled practitioners of computational codes use computers to produce cartoonlike or even movielike imitations of realities in which they are interested, firmly believing to achieve “scientific” truths in that way. (It seems to me that it’s an irresistible fashion that affects also large areas of the basic research in physics and cosmology since decades).27 I am here below indicating a very few bibliographic references in addition to those provided by the footnotes of the foregoing text. The additional works that in my opinion deserve mention are not of a specialist nature and offer relatively easy readings to people interested in expanding their knowledge in related subjects, in consideration of the wide range of basic concepts that are inevitably involved by any discussion concerning entropy, syntropy and complex systems.
As to the use of computers, the ideas should be very clear: One thing is using computers for calculations and/or simulations inherent in rigorous scientific or mathematical theories, quite different thing is using computers as a hyperpowerful toy for any kind of phantasmagorical games. In the first case, computer data processing and simulations obey laws, rules and constraints that are proper to the relative well tested scientific discipline. In the second case, computers are only the best devices to realize any kind of “impossible” fantasy. Besides, consider that mathematics can also be used to process input “data” that relate to arbitrary situations, which do not exist in the real world.
27
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Essential References

W. Heisenberg, Physics and Philosphy, first published in Italian (Fisica e filosofia), Il Saggiatore, Milano 1961

F. Bonsack, Information, thermodinamique, vie et pensée, GathierVillars, Paris 1961

J. Gleick, Chaos, The Viking Press, New York 1987
 I. Prigogine, Le leggi del caos (The Laws of Chaos), Italian edition, Laterza, Bari 1993 (It’s a collection of lectures delivered at various Italian Universities and Cultural Institutions)

J. P. Crutchfiel, J. D. Farmer et Al., Il caos, in “ Le Scienze”, Italian magazine, n° 222, February 1987

J. S. Walker, C.A. Vause, Ricomparsa di fasi, in “Le Scienze” magazine, n° 227, July 1987

G. Caldarelli, Complex Webs in Nature and Technology, Oxford University Press, 2007, for an overview concerning studies on complexity.
(The background of this Attachment is the theory expounded in my book, published in Italian only, L’evoluzione sintropica dei sistemi urbani, Bulzoni, Roma 19881991. It is a text not easy to read even if translated into English. For readers endowed with a higher technical education and particularly interested in deepening the discussion, a concise English version of the theory can be found in the six chapters of which the first section of this same book consists. Besides, there are also a number of texts by I. Prigogine and G. Nicolis which include many sections not easy to read. The more readable sections address subjects and arguments that are often repeated passing from one text to another. The language is of a specialist nature. However, some chapters of those texts are a remarkable contribution to contemporary scientific understanding. I limit myself to mention the two following books only:

SelfOrganisation in NonEquilibrium Systems, John Wiley & Sons, New YorkLondonSydneyToronto, 19771981

Exploring Complexity. An Introduction, R. Piper GmbH & Co., Munich 1987 )
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(Room for notes by the reader)
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