3.3.1. Symmetry and asymmetry in nature

In decision-making, operations research, and more generally in economics, industry, or even in human and social systems, all theories are based on a reductionist approach and are part of the so-called “exact” sciences. We have to observe what we have in nature: this does not mandatorily apply since everything is global. Thus, each time we are handling a problem in a given way, we have to ask rather reversed questions.

In [MAS 08], we have already discussed the problems related to ambivalences, Nash equilibria (game theory), chaos and emergence, etc. It is advisable to come back a little bit to these concepts: in various fields of interest, for instance the decision or optimization fields, there is often a tendency to minimize these concepts because few people are able to transpose results coming from theory onto the real world. Moreover, if we look at the structure of organisms in nature, we can observe that the backbone of all that exists is always based on well-designed geometrical patterns; also, in software, the sustainability of an application is highly depending on its architecture. Consequently, it is difficult, under this context, to elaborate an appropriate and right decision if it is poorly structured around a bad architecture.

Now, regarding the emergence of a steady, stable and relevant decision, we can state the following comment: in nature, moving forward requires some diversity and “smart” balances (which are not the result of rigid calculating processes based on formal algorithms). For this reason, we must integrate and develop, in our decision sciences and practices, advanced concepts related to asymmetry, chirality, or even some specificities such as the Coriolis effect to better understand how new processes can emerge, are organized and may refine the existing ones: the objective is to provide more efficient, and effective, human or cognitive systems.

3.3.2. A reminder on matter, dark matter and dark energy

In this book, reference is made to the concepts related to physics (matter, antimatter, black and/or dark energy). Our intent is to adapt some experimental results to our decision technologies, then develop in a better way inductive and/or abductive reasoning processes to get more precise and better fitting decisions. Indeed, the available theories on these subject matter are too complex and intricate to accurately explain for instance what we can do on the data, define their structure, and to extract their pith and marrow. Through some current examples and experiments, this enables us to raise several major open problems or issues, and to elaborate some main assumptions and theories to explain these new results. For instance:

1. In physics: the first evidence for dark matter was obtained in the 1930s when Zwicky and Smith looked at the velocities of galaxies within clusters: to explain some unexpected anomalies, they assumed and found that the total mass involved in the Universe was 10–100 times larger than expected from the visible mass. Indeed, larger velocities indicate larger gravitational forces ⇒ larger masses than are visible are contained in such systems. So, dark matter is the general term for matter that we cannot see to this point with our telescopes, but that we know must be there because we see its gravitational influence on the rest of the Universe, or that we supposed to be since such a concept may explain unexpected phenomena.
2. In 1999, several experiments looking at distant supernovae reported groundbreaking results: the measured expansion of the universe appears to be accelerating. This was consistent with a small, but nonzero, positive cosmological constant that accounted for the difference above. But, the fact that this is still completely inconsistent with the quantum mechanical expectation leads people to believe that there could be more to this than just a cosmological constant [JUS 09]. Nobody knows exactly why the expansion of the universe is accelerating, and why the initial inflation was so fast: the new concept introduced to provide an explanation to these effects is called “Dark Energy”.

3.3.3. What kind of matter and dark energy? Is it an asymmetry?

If we look at the composition of the cosmos, the assumptions presently accepted show the following distribution: matter = 5%, dark matter = 25% and dark energy = 72% (as per the last estimates from the NASA).

We do not really and exactly understand the structure and meaning of their actual constituents, but we know that such asymmetries are due to very small variations in key cosmologic constants. We can write, however, that a strong asymmetry exists between these three above components; some evidence has been highlighted by many scientists for several decades:

• – asymmetry is necessary for the very early existence of our universe;
• – in baryogenesis, asymmetry requires CP violation (combination of particle-antiparticle about the “C” charge conjugation, mirror effect about the “P” parity symmetry), and Baryon/Lepton number violation.

Dark matter and dark energy are not well understood by a lot of people since they address the very small scale of physics. Their presence is identified and mathematically explained, but it is not yet a visible concept. The question is: are we faced with the same problem or phenomena in our common life, at the medium scale of nature? The answer could be “yes” and as we will see, sometimes we are involved with a kind of dark substance whether it is visible or not.

3.3.4. Physical or virtual substance? Aether and “mind stuff”

When asymmetry is discussed in our real world, most of the people will immediately think about “information asymmetry”. Asymmetry means that two elementary agents, holons or components, in a given system, appears to have a partner agent called its anti-agent that shares many of the same characteristics, except for one (characteristic) which is at the opposite side of those possessed by the initial agent. For example, in an information network, a Web “geek” will be connected, in a peer-to-peer mode, to another agent. Both agents have exactly the same influencing power, but they may have exactly opposite way of thinking or submitted to a “mental power” which will distort the normal flow of a process (e.g. the way of thinking of an isolated agent). There are some invisible causes to this effect:

• – In this different example, through the social networks, a huge volume of information can be exchanged on a given topic; the information flow is identified by a specific a specific/metadata tag, or hashtag. Moreover, the posts issued through a typical social network are not just a free and mandatorily immoderate ad hoc discussion forum. It is possible, however, to control the content of such posts and to flood the forum with directed messages: under these conditions, such a tactic acts as a “dark power” or “mental substance”. This is consistent with the basic principles of the universe (the Gödel Theorem) which stipulates that any system (e.g. a product and a decision process) is always the emergence of an idea, a concept or a program, that is to say, a piece of information. Consequently, a system always depends on something external to the system; this external “something” is of a different nature than the system considered.
• – Again, in a company, diversity of ideas issued during brainstorming sessions is quite limited for some intrinsic reason: the thought of the living beings is immersed in a kind of “aether” which plays a continuous influence on all the ideas and thoughts issued by the employees. This is because, in that case, the corporate culture acts as a fingerprint, a metarule, or even a global directive. In our context, aether is a global thought (like a physical substance, the mental substance is of a quite subtle kind we do not see, but it exists in any part of a space which is apparently empty). So, aether (mental substance) can be an invisible dark power, mind stuff, a culture, an underlying thought, whose effect is insidious and dominating. Such an aether, as above described, acts as a metarule that modifies the way of thinking: it is able to change, control or superimpose some trajectories onto individual thoughts.

• – Underground economy. In this area, many underground activities or illicit financial flows are grouped together. They address legal and/or illegal production of goods and services. They are organized into networks and involve either small traders or major players who can help each other and form “transnational crime” organizations. These activities include forgery, moonlighting or shadow working, criminal activity, large financial crime, weapon trafficking, production and sale of dangerous drugs/substances and services, tax havens (money laundering) etc.

The impact of the shadow economy is quite important: it may concern (very roughly) from 15% up to 45% of the GDP, in a country (according to where it is located), and could cause severe damages in terms of revenues and equity. Consequently, it is a kind of “dark economy” which causes a lot of asymmetries.

3.3.5. Asymmetry in nature: is this a novelty?

In our visible word, asymmetry is everywhere. It is very important to note that in nonlinear complex systems (because of the SIC effect), two agents may basically have been all annihilated but except for a tiny difference between some of their human excellence or capabilities. As a result, completely different behaviors or results may occur regarding the evolution of a system. It is a consequence of the butterfly or pocket billiard effect. Indeed, the pool billiard includes convex obstacles represented by the presence of several balls on the pool billiard table. When two balls are launched from a same given corner of the pool table, along paths which are very close to some angular range, they will diverge after a few collisions until they have opposite trajectories. This is what is happening each time power laws apply in a system: deviances are amplified and nonlinear effects are quickly observed, until we have a disruptive behavior.

It is also common to quote the asymmetry existing in the brain. Here, asymmetry refers to the uneven involvement of both cerebral hemispheres, within its different mental functions. Indeed, the two hemispheres are anatomically very similar (as highlighted in life sciences experiments and observations, they are due to stem cell differentiation caused by protein interactions at the DNA level), but their differences are related to very thin and subtle characteristics, as follows:

• – The “left side” brain is more analytical, logical and mathematical; it includes speech processing, and preferably functions in a bottom-up approach: starting from the detail to move toward more complexity.
• – The “right-side” of the brain is more analogic, empirical and intuitive. It works as a global holon, in a more holistic way. It is more suited for image processing and non-verbal communication.

Based on Piaget’s work, we can bring out some concepts related to analytical intelligence and empirical intelligence. In this book, the comparison will stop there because applications of this theory could be subject to much debate. What we can say, anyway, is that any human being needs the two hemispheres to exercise all the brain capabilities (as in a holistic approach). Nevertheless, the precise architecture of the links and interactions (which also requires the simultaneous activity of several part of our brain) are not fully known: they enable the synergy between these structural and functional differences; they are even poorly understood, they are fundamental to better implement co-working notions within an inhomogeneous network.

This is what we observe, at a different scale, in a company: decision-making can be defined due to the use of a mathematical model. Such a model, however, can be inadequate and give bad decisions. Here, the most important contributing factor is related to the interactions, and asymmetries existing everywhere, just because the knowledge databases (KDB) are not consistent. As soon as such a decision-making process is unable to highlight and define the significant interactions between facts causes or forces, it is necessary to imagine and integrate virtual forces or “confined” factors that govern them. This is a kind of inductive or reconstructive work, in large DKB’s, to extract or extrapolate some possible missing information.

Another example concerns the project management process. Here, 90% of project management problems and failures are not due to technical difficulties, but due to the external factors such as: assignment of responsibilities, sharing or reconsidering skills, span of control and management, power struggle, etc. As a result, when comparing a project development situation with the initial plan, big asymmetries are often caused by seemingly minor human issues: personal skills, moral sensitivity and behaviors whose effects are quite huge on the project development success.

3.7.1. When does asymmetry occur?

Asymmetry related to information availability occurs when one party to a transaction knows the quality of a good/service, while the other party does not. For instance:

1. In health insurance, the purchaser of an individual policy knows his/her state of health and has an idea whether he/she is a high risky person; the insurance company may not know the potential risk related to its expenditure. In the same way, the doctor understands the proper treatments to be applied; patients do not know all the effects of a medicine. But nobody can predict something about your precise life expectation.
2. Job applicants know their quality as workers; the potential employer does not. It is a difficulty when you have to hire an employee: you do not know what the performance of a worker will be until several months later.
3. For used cars, the seller knows the quality of the car, the buyer does not. The objective is not to buy a troubled used and poor quality car (a so-called “lemon”).

As soon as the information is uneven (asymmetric), the question will be to determine the right price of the buy in a competitive market: here, the price becomes a discriminative parameter since it is associated with the quality of the product, the fashion, its “image”, etc. Thus, the demand level for a specific product or good depends upon its real price, the one related to the user acceptance. The solution has given us the result of a convergence toward a unique or multiple equilibria, similar, to the evolution of a monopoly price.

Several approaches are used: they are based on utility functions, correlation factors between various descriptive variables, statistics on many connected data, probabilities, analysis of pre-existing functions, etc. This is why asymmetry is so different to uncertainty which is used in non-predictable and chaotic systems.

3.7.2. Asymmetry due to mental predisposition (or soul)

In this area, we focus on the influence brought to the system from the outside (with regard to the Godel’s theorem): it can be linked to a social environment, a culture, a political learning, rumors, interpreted facts, hunches, etc., widely broadcasted, insidiously repeated, distilled in the mental constructs of the human being (as the effect of an invisible dark substance).

Under these conditions, it is clear that we better understand why:

• – in a company, or manufacturing plant, planning will overreact each time a disturbance occurs according to some situation or top management stress;
• – during a political election, clusters of people can be conditioned by a political left-sided party (or republican for instance), with the help of multimedia advertising, TV speakers, etc. Accordingly, people will vote based on certain inclinations guided by spheres of influence and allow a particular candidate to be elected;
• – in economy, decision-making will be highly depending on the influences related to stream of thought owned by a group of economists, managers or media group of people where some specific ideas or concepts prevail.

3.7.3. Application

For instance, in the case of manufacturing large products requiring a lot of components and huge manpower resources and costs, a “make or buy” decision-making process will be started to determine in which best location/country, the assembly and test will be implemented. Some factors will be considered and introduced in a decision model, such as the:

• – nature of the business conducted by the provider;
• – control system implemented;
• – size of the different companies able to participate at the product manufacturing;
• – number of employees;
• – financial situation;
• – quality of the products, goods or services;
• – location;
• – social situation;
• – resources turnover;
• – skills and career profiles;
• – job-lock practices;

• – reliability of the involvements;
• – legislation, laws, etc.

Moreover, in each area, some corrective factors will be defined to compensate some influences due to our so-called mental substance. The problem consists of building policies that lead to high-and low-risk solutions, either for the manufacturer or the customers:

1. The “make” solution is associated not only with high prices, production control or reactivity, but also with low customer losses and copays.
2. The “buy” solution can be linked with low prices, high quality risks but catastrophic uncertainties such as higher administrative fees, volatility of prices, production coverages.
3. In fact, the right choice will often consists of a selection between a “good” deal and a deal that is “not as good”.

In some decision processes, where scientific technologies are not well introduced, it is common to call for “adverse selection”. This technique is more suitable as soon demand elasticities are low: due to lack of information, large differences can be observed in terms of results.

Adverse selection is quite different to moral hazard: under this condition, an informed person has an advantage through an unobserved action. It is a more global notion than asymmetry. For instance, the driver of an insured car may drive faster than allowed, beyond the yellow line. Also, in business, when you know the legal regulations in practice, you can better satisfy a specific demand, sometimes beyond the borderline or in violation of what is allowed, insofar as it is done discreetly.

Both concepts, however, can generate catastrophic equilibria (because of the SIC characteristic of a complex system), and the consequences of a given decision cannot be reliably planned since such systems are chaotic. This is exactly what we have in electronics when the circuitry is submitted to chaotic or unknown inputs. This is also the kind of observation we have either in prey–predator systems or in game theory.

Considering what has been said, either in Chapters 3 or 4, the combination of emergence and asymmetries generates needs and disparities directly depending on users. Sustainability is also becoming an emerging property: indeed, we cannot control and regulate a complex system in a top-down way, through procedures and rules, as usually done; information and actions are going both ways in our interconnected systems. Solutions can only come from actions on the structure and interactions (coupling and weight considerations) of the system.

3.8.1. Extensions of chirality

By extension, chirality is used to describe an object that is non-superimposable on its mirror image. Human hands, for instance, are the most universally recognized example of chirality: the left hand is a non-superimposable mirror image of the right hand; no matter how the two hands are oriented, it is impossible for all the major features of both hands to coincide.

Many biologically active molecules are chiral, including the naturally occurring amino-acids which are the building blocks of proteins and sugars. But, in our living systems, most of these molecules are of the same chirality: most amino acids are from L-type and their elaborated proteins are naturally left-handed (Levogyres proteins), whereas the produced sugars in living systems are of D-type (Dextrogyre). Then, life, on the Earth is homochiral.

Getting such patterns requires to perform a transform processing on the initial object (pattern) with respect to a point, an axis or a plane (or even an n-dimensional space) to obtain the associated geometric object. In industry, or anywhere else, the concept of chirality can be extended and applied in very different domains:

• – in practice, this is what we do when we look in the rear view mirror of a car: the real image and the reflected image are chiral;
• – this is also what we get, in physics, during the phase transition of preonic fields at some initial conditions (role of preons and dark matter in the production of early cosmological structures): we may have a spontaneous emission of some orders or events (catastrophic fails) or ideas (brainstorming, etc.), emergence of alternative concepts, all of them independent to the external environment, totally unpredictable and uncontrolled.
• – At last, this is what we could have through “spontaneous symmetry breaking”: in nonlinear dynamic systems, underlying symmetries (for instance, an even number of negative feed-back loops) reflects and conducts to unstable states. Thus, any dynamic system trades stability for asymmetries or for a predominant ambivalence.

We have no reliable information about this Earth life’s “choice” of chirality, (which is generally specific to mechanical physics), about a possible selective destruction of one chirality of amino-acids, about the type of carbon-based life forms in the universe, in accordance with an opposite chirality, thus about this asymmetry.

We can make a parallel with existing systems and organizations: they are mostly based on stable components, (like the chirality of molecules observed in DNA, in living organisms); to converge toward a global attractor, however, it is quite easy to proceed through a succession of stable–unstable patterns, thus to achieve the goal by various forms of stabilities and basic components.

3.8.2. Applications of chirality

Use of the chirality concepts is not so simple as expected. Indeed, when talking to people, chirality is seen as specificity or property. For instance, a dextrogyres screw driver, the spin number of an atom, driving in the right lane of a road, right or left priorities at a crossroad, the emergence of right-handed or left-handed people, etc. Truth is not exactly what is described above and chirality is an interesting notion we can use in our everyday life.

Chirality is important in our lives. According to our structure, we are able to detect some chiral molecules: for instance, the D-form amino acids would tend to taste sweet, whereas L-forms would be usually tasteless. Also, in the family of terpenoids, some carvones may smell differently: caraway seeds, respectively, may contain L-carvone and D-carvone, enantiomers of carvone. These smell different to most people because our olfactory receptors contain chiral molecules that behave differently in the presence of different enantiomers.

In the pharmaceutical industry, chirality may induce different effects on drugs. These chemical compounds are generally obtained through asymmetric biocatalysis, and their effects reflect the specific chirality inherent in biological systems.

This is why a number of commercially highly successful drugs are chiral; then, the economic significance of stereochemistry to the pharmaceutical industry is obvious. Such an approach will associate with a strategy to provide more sustainability of the enterprise, in extending the profitable life of a pharmaceutical “bestseller”, and to provide an advantage against generic competition.

In industry, there are potential advantages in developing best of bred products based on single enantiomer products or to implement more achirality in their business. Indeed, they enable:

• – less complex and more selective processes (effectiveness);
• – develop potentialities for an improved and consistent span of products (efficiency);
• – reduced potential required for integrating and controlling complex interactions (complexity);
• – less complex relationships between stakeholders, in terms of needs, interests and satisfaction consistency (better holistic approach).

In manufacturing systems, this is also a kind of chirality theory which governs global optimization during a simulated annealing process, and consequently the management of the sustainability concept. The basic principle is quite simple: an optimization process can be based on the reformulative approach using a metaheuristic (Metropolis-Hastings) with a probabilistic acceptance depending on the Boltzmann constant. The best answer (global optimum), within a large search space, is progressively emerging after several hill climbs, as we have in the elaboration of a crystal. Indeed, when a crystal is growing, some defects may appear in its structure. In order to recover a more perfect structure, the approach consists of disturbing the solution; as it crystallizes, we proceed to some heating and stirring of the solution (this is similar to the creation of some disturbance or disorder): then we can restart the optimization process by cooling the relevant parameter. In this case, the objective of the simulated annealing process is to simplify some structures; this leads to one chiral form almost exclusively over the other. Boiling a supersaturated solution similarly perturbs the crystallization process with one of the two chiral forms resulting in preference; in the same way, heating a solution to be crystallized, enable to smooth irregularities and singularities, then to recover a solution in equilibrium and to generate a single chiral phase, by cooling the solution according to a specific temperature gradient. These dissolution–nucleation cycles lead to a solid single chirality structure.

This method is simple; it has a great potential and can be easily adopted into any industrial process.

3.9.1. A physics reminder: the dynamic movement of bodies

In any physical environment, non-fundamental and fundamental forces apply on all the bodies. Here, since we are more interested to the motion and movement of a given object, we consider that it is always submitted to three main non-fundamental forces:

1. The Pressure force provided by the fluid in its surrounding environment. This force is applied and directed from the higher down to the lower pressure of the fluid, perpendicularly to the isobars; it is proportional to a pressure gradient.
2. The Coriolis force. This force is associated with the rotation of the Earth: when an object is moving, this force diverts the horizontal movements of an object to the right, in the northern hemisphere, and toward the left in the southern hemisphere; it is vanished at the equator level.
3. The Frictional force is a surface force that opposes the motion of an object: it tends to slow it down. It is well known and will not be more described in detail.

We will skip the two well-known pressure and friction forces: on the contrary, we will spent some time on the Coriolis force.

3.9.2. Description of the Coriolis effect

The Coriolis effect is a deflection of moving objects when they are viewed in a rotating reference frame. For instance, on our planet, the Coriolis effect is caused by the rotation of the Earth; it depends on the inertia of the mass experiencing the effect. It behaves exactly like a real force (that is to say, it causes acceleration and has real effects on the orbit of the object) and is not attributable to an identifiable originating body, as is the case for electromagnetic or nuclear forces, for example.

It disappears in a non-accelerating, inertial frame of reference. In non-vector terms: at a given rate of rotation of the observer, the magnitude of the Coriolis acceleration force applied to the object is proportional to the velocity of the object and also to the sine of the angle between the direction of movement of the object and the axis of rotation.

The Coriolis effect strongly affects every dynamic flow on our planet: large-scale oceanic and atmospheric fluids circulation flows like jet streams, propagation of specific waves and currents in the Ocean, and, more generally, the emergence of large-scale ocean flow patterns such as the “Sverdrup balance”, as we can see below [PAU 10].

3.9.3. Displacement and moving of physical objects: a question of amplitude

The Coriolis force causes the moving of various objects under displacement on the surface of the Earth: they appear to veer to the right in the northern hemisphere, and to the left in the southern; rather than flowing directly from areas of high pressure to low pressure, as they would do on a non-rotating planet; winds and currents also tend to flow to the right of this direction, north of the equator, and to the left of this direction in south of it. This effect is responsible for the rotation of large cyclones.

However, in local tornadoes, we have to consider both centrifugal and Coriolis forces. The relative importance of Coriolis forces is determined by the applicable Rossby number, a dimensionless number which is the ratio inertia/Coriolis force. Tornadoes have high Rossby numbers while in low pressure systems where centrifugal forces are quite substantial, Coriolis forces associated with tornadoes are negligible for practical purposes.

In practice, water rotation either in bathrooms, or in toilets, under normal circumstances is not related to the Coriolis effect or to the rotation of the Earth: no consistent difference in rotation direction between toilets in the northern and southern hemispheres can be observed. The formation of a vortex over the water drain hole may be explained by the conservation of an angular momentum: the radius of rotation decreases as water approaches the plug hole so the rate of rotation increases, for the same reason that an ice skater’s rate of spin increases as she pulls her arms inside her body.

Note that climatic depressions, also known as cyclones, cannot emerge near the equator where the horizontal component of the Coriolis force is zero. Coriolis forces are useful to study global turbulences occurring in dynamic systems and to measure their importance within the evolution and overtime of the system.

3.9.4. Curvature of displacements on the Earth

In terms of ballistics and inertial circles, the variation of the Coriolis force therefore gives different atmospheric circulation patterns with latitude. Presently, application fields are essentially focused on storms, tornadoes, blizzard, etc. In fact, the problem is to find analogies in transdisciplinary fields such as: economy, industry, logistics, etc.

Ballistic missiles and satellites appear (for an observer) to follow curved paths when plotted on common world maps mainly because the Earth is spherical and the shortest distance between two points on the Earth’s surface (called a great circle) is usually not a straight line on those developed maps: the real trajectory is, in fact, the integration of three effects: curvature of the earth, wind forces and Coriolis force. In parallel, and more precisely, Coriolis force is used to calculate the trajectory of projectiles in the atmosphere: when we throw a bullet, once a shell is fired or a rocket in suborbital flight has exhausted its fuel, their trajectory is controlled only by gravity and wind (when it is in the atmosphere). As soon as the wind effect becomes negligible, the Coriolis effect becomes predominant, according to the mass of the projectile: in the rotating frame of Earth, we must add a deviation (toward east or west) to find out where the shot will fall versus the target, or on the ground.

3.9.5. Application to molecular physics

In polyatomic molecules, the molecule motion can be described by a rigid body rotation and internal vibration of atoms about their equilibrium position. As a result of the vibrations of the atoms, the atoms are in motion relative to the rotating coordinate system of the molecule. Coriolis effects will therefore be present and will cause the atoms to move in a direction perpendicular to the original oscillations. This leads to a mixing, in molecular spectra, between the rotational and vibrational levels.

3.9.6. Insect flight stability too

Flying insects such as dipterans and moths (Lepidoptera) use the Coriolis effect when flying: their halters, or antennae in the case of moths, oscillate rapidly and are used as vibrational gyroscopes. Within this context, the Coriolis effect has nothing to do with the rotation of the Earth.

3.9.7. The Coriolis effect on the shape of live beings

Some theories and observations exist in this area. We will quote them, although there is insufficient proof to validate their cause of existence: this is the case about the shape of leaves in some plants. It is also the case concerning modification of limbs and growing organisms in the human body, depending on whether one lives in the northern or southern hemisphere.

Now, in a very speculative and implausible way, the following develops demonstrations based upon the influence of the Coriolis force on the shape and functioning of our organs: our face, body, personality, and lifestyle would show the mind’s emphasis and neglect, or convergence-to-divergence ratio. This ratio is said to be high for males and low for females. Body and lifestyle show how the mind is used, including the dominant hemisphere, or order-to-randomness ratio. A male would prefer to learn this by converging simultaneous information of the body. A female would prefer diverging the serial information of the lifestyle. Face and personality show how the mind is, in relation to the Coriolis force value. To learn this, a male prefers convergence of the face and a female prefers divergence of the personality. These above statements are just quoted for information and will not be used in the framework of this book, even if, in some large and worldwide companies, some research is in progress with neurobiologists to explore such properties.

3.10.1. Growth process of living organisms

Complexification of the living organisms is generally based upon the application and replication of simple and developmental algorithms [WOL 01]. Thus, the whole structure of a living being is an assembly of repeated appearances of specific multicellular structures [LIN 90]. In the case of a compound leaf, for instance, some of the lobes (or leaflets) have the same shape as the whole leaf at an earlier stage. Consequently, in such a developmental process, and step by step, a living organism possesses a self-similar structure and becomes a space-time event.

The developmental processes, envisioned here, is the result of L-system formalisms introduced a few decades ago by Lindenmayer G. Herman and G. Rozenberg in the Mathematical Systems Theory Journal [LIN 68, HER 75].

The modeling and computer simulation of living organisms, including branching patterns and racemic inflorescences is based on the concept of cellular automata. Additionally, the tropism of the organisms can be taken into account (by tropism, we mean a turning or bending movement of an organism, a structure or a part toward or away from an external stimulus, such as light, heat or gravity). The technology usually implemented to model such systems refers to the L-systems generation associated with infinitesimal variations of sympodial, ternary branching or even tropismic constants. Now, this technology enables us to model context-sensitive and stochastic L-systems.

Computer-aided generation of live patterns explains how new orders can emerge. This approach also gives a focus on self-similarity and relates living structures to geometry of fractals. It is also possible through this technology to imagine innovative forms, patterns and/or structures which are of great importance in industry or biology, etc.

The fractal algorithms and associated technologies are not only used to create new forms, shapes and orders, but they can also analyze the use of texture models describing their appearance and provide useful tools in a lot of practical applications in the industry:

• – fractal techniques can be used for data compression and image analysis: image compression by fractals is one of the first compression techniques to take advantage of the scale information contained in an image;
• – fractals are also used for feature extraction in tasks related to object recognition, and image characterization through the measure of the fractal dimension.

Fractals and wavelets are connected by the “theory of self-similar measures”, also called multifractals. In fact, fractals and wavelets share the fundamental properties of self-similarity at different scales, so common in nature, and in finance: the multifractal characterization involves the determination of a measure of regularity of a signal, or roughness of a surface, etc.

Measures of regularity are not only useful for image segmentation but also for noise reduction: noise absorption, for instance, depends on the rough material porosity and size of cavities.

Fractals and their “self-similarity” at different scales are used to describe the texture and roughness of an object. This analysis of roughness, however, is coupled with additional approaches to reinforce some of their properties and characterization:

• – statistical methods which characterize textured regions from statistics (features) and describe the spatial interrelationships of their grayscale pixels. They are based on characteristics analysis such as moments (mean, variance of gray level), entropy, energy, homogeneity, etc.;
• – geometric methods which consider texture as a combination of textured (primitive) arranged in a placement rule. Markov random fields are a popular tool for modeling images; they are a stochastic model where the probability of a pixel having a given gray level is conditional on the value of gray levels of its neighbors.

Such signal processing methods include a large number of other techniques that exploit the appearance frequency of the texture (fractal analysis, filtering in the Fourier domain, Gabor wavelet decomposition models, etc.), which can be deployed in various domains, and also what we call “adaptive shapes”, as defined in the first section of this chapter. In terms of applications, we can quote:

• – angio-genesis: objective is to analyze intricate tumors possessing complex blood networks, while the healthy tissue has a fractal structure;
• – finance: multifractals are used for the analysis of Eulerian and Lagrangian turbulences. In the study of the disturbances dispersal, the occasional time breakings are basically characterized by a specific invariance of a scale with an exponent which represents a given spatial structure;
• – noise protection features, as described above;
• – turbulence analysis: the dissipation and chaotic phenomena, with their velocity fields, are made of continuous stochastic processes associated with multifractal properties.

In fact, a lot of applications can be found concerning the fractal technology. They may not only concern the dissemination of a fire or the timely deployment of information, the ranking and clustering processes in the organization, the assembly structure of electronic components in a computer, etc., but also explain some phenomena in quite different fields such as: the empowerment of a culture, the interaction evolution during a negotiation, etc. This enables to decrease the lack of asymmetry in available information.

3.10.2. Local complexity growth

Life is based on structures depending on basic principles used in physics. Their application may vary according to some parameters (magnitude or rotation of a parameter, scale of the involved object, etc.) which affect either the shape or function specific to an object.

However, there is a fundamental change: for a number of decades, we have understood that the complexification of nature, or the complexification of a given system, is not the result of linear structuring processes, as we have observed in physics in limited fields of application. In fact, the structuring processes are nonlinear: they can be based on “power laws” or replication of specific ratios (e.g. X = k.F[X2,X] +c), representative of underlying fractal structures [MAS 08].

This concept is a general one; it can be applied in any discipline: biology, physics, mathematics (geometry), social, organization, industry, etc. and can produce complex and sophisticated organisms.

Plants, capillaries, bronchioles and kidney ducts in higher evolved animals or living beings are typically structured, such as a tree, and each tip as it extends is divided into two branches. The same kind of structure exists in the digestive systems of lower animals. In the human body, we have our two arms.

In our lungs, there are about 16 levels of pair by pair branching (set of dichotomies) producing about 60,000 bronchioles associated with a same number of acini (secretory cells which have excretory openings on a channel in the pancreas, that produce digestive enzymes). In parallel, our blood system comprises a dispatching network (arteries, arterioles, etc.) based on about 23 dichotomies which enable the blood to flow in up to 8 million of arterioles.

The “lung tree” is the name given to the air-flow structure of the lung. It is basically a fractal network; it looks like the following an ideal one, as described and modeled by B. Mandelbrot [LOS 05].

3.10.3. Characteristics of the fractal structures: applications

A main property is that all the ending leaves or cells are at the same distance from the origin of the network. This is a fundamental structuring characteristic in order to get a consistent effect of conducting to a homogeneous distribution of fluids (e.g. ventilation of the lungs, measurement of the arterial stiffness or feeding enriched blood in a human body). It is the same problem we encounter in urban traffic: the objective is to calculate the best for fit ratio between the size (capacity) of large avenues (or high ways) and the size of small streets, without creating turbulences. Under these conditions, the ratio between the section of arteries, before and after a bifurcation, follows a power law.

It is also the same structure and characteristic we encounter in the Internet network: as previously mentioned in the book, in about 20 clicks, we can access to any required information located somewhere in the network.

A second advantage of such a structure is that it enables a very large contact surface for a limited volume: by studying the laws of diffusion through an irregular and rough surface, the dissemination or diffusion of a liquid or gas is easier. It is the same with a chemical reaction; heat exchange, etc.

In Figure 3.7, the image on the left represents the detail of an arteriole similar to a Sierpinsky object (a set of holes characterized by small volumes, large interfaces with a big global surface). The image on the right represents a piece of glowing charcoal in a fire (the contact surface, as we can see in the upper location of the photography, is a fractal one to get a better chemical oxygenation).

A third specificity of fractal structures is related to the geometry of the initial transformation: it can affect the topology of the network. For instance, when a branching occurs in a plant, a tree or even a blood vessel, the cross-section of the main and secondary branches can be calculated in a precise way in order to optimize the logistic flow of fluids or products.

Moreover, and particularly in biology, in the human body to be precisely, some other parameters can be calculated in order to optimize the flow of the blood either in the arteries or in the arterioles as explained hereunder [PAR 97]. This includes, for example, density, speed and pressure gradients of the fluid, risks of turbulences, emergence of new pressure gradients, branching angles, flexibility of the arteries, etc.

On the left side of Figure 3.8, we represent a symmetrical bifurcation showing branch angle (Θ), rotation angle (φ), and parent (δ), and daughter (α) branch lengths; these parameters are defined and applied (http://library.thinkquest.org/26242/full/ap/ap11.html). Here, as for plants, phyllotactic architectures, which govern leaf arrangements on a stem, will optimize access to an ending capability (e.g. oxygen exchange, etc.).

On the right side of Figure 3.8, because the binding of particles to the boundary is strong, the network does not reconfigure easily; therefore, the way the particles are initially distributed largely determines how the particles connect to one another. This constraint sets the relative number of trunks, branches, and termini that the network forms. This figure, compared to the first fractal Mandelbrot structure, shows how a different initial state can lead to a different final network. The distribution of traffic flow within such a network is optimal. A good accessibility to any destination is achieved. The sustainability of the distribution solution, based on such fractal patterns (with an adequate throughput at each branch level), can be maintained since it is related to a stable structure.

Moreover, in case of failure of a given ending leaf, or holon, a secondary network can be built in lieu of the failing holon of the structure and replace it. This is of a great flexibility: due to a replication processes, we can replace a component, a function or a holon by another one (or several ones), or to displace a difficulty to solve it somewhere else in the network.

3.10.4. Traffic improvement

Nature is multifaceted. There is nothing more sustainable than nature: the origin of sustainability in living nature is as old as the origin of life (3.5 billion years); thus, it would be pretentious for humans to start to reinvent or redefine the sustainability concept. In difficult cases, nature can adapt and survive; it knows what reliability means.

For this reason, fractal structures like hierarchical networks are supported by the duplication of holons, or subsets of a network, so that a faulty element or an underperforming circuitry can be replaced by another.

Thus, a traffic infrastructure leads to obtaining an interconnected network: the fractal structure is always maintained (as shown in the figure). The concept of self-similarity is still present, and all the above properties remain (as in the Web). We always have to remind that objective of such an infrastructure is to get a sustainable traffic flow, whatever bottlenecks we may encounter.

We obtain the same ribbing phenomenon in nature when cracks appear with the drying of soils, or when homogeneous groups of human populations are formed in a country (structure or assembly of towns, regions, etc.).

Now, if we analyze the urban traffic in Paris [OUL 11], we obtain the following map.

As we can see, the traffic structure is similar to the structure contained in the previous diagram. Two interesting results can be stated:

1. There is a strong correlation between the speed of the vehicles (products) and the traffic density in the highways (not as evident as expected).
2. The speed limit is never the good one to be adapted. As often demonstrated in our industrial systems and proven by simulation, when a connection is near to be saturated (caterpillar effect) the optimal speed (or throughout) must not exceed about 80% of its upper limit.

This is shown on the following graph (speed-throughput) related to the traffic performance (Figure 3.11, road traffic near Paris in France).

This curve is based on real data collection: the maximum capacity of a road is always limited. A consistent estimate of traffic density is about 2,000 vehicles/h, at around 60 km/h. For information, the throughput is around 2,500 on a two lanes normal highway, while it is of about 8,000 vehicles/h in the Paris turnpike.

When the speed of the vehicles is high, the intervehicle distance is naturally increased and the throughput is reduced.

Near the saturation point, the speed may amplify undesired effects (like turbulences, traffic risks, etc.) because the interactions are much stronger: thus, we are evolving in a nonlinear dynamic system. This is why deterministic chaos and fractals apply to this study case.

These statistics are just given as an information [OUL 11]: indeed, the traffic capabilities are depending on additional parameters such as: weather conditions, braking and grip capabilities (ABS, tires, nature of the concrete, etc.). This is why more holistic properties such as driving quality and interrelational behaviors of the driver are required.

Whatever the impacts of such studies and the powerful results obtained from some models and underlying principles, we have to keep in mind that the decision is never majorly deduced from technical or economic decisions. Most of the time, the emotional, social and political aspects are always predominant. This explains why the asymmetry of information is so important and why it is of key importance to integrate that component in the future decision-making processes.

In an assembly plant, the same problem is applicable. As explained in [MAS 08], the main challenge in logistics is to optimize the flow of components, parts, products in the manufacturing line and final goods or services. Within this framework, the plant layout is of key importance: it requires implementing planar traffic graphs and improving route sections and feedback loops. Linear assembly line structures are often obsolete and need to be adapted to cooperative and networking approaches.

3.10.5. Sunflower considerations

In a similar way, in plant morphogenesis, we can recall some properties of sunflowers. Their fractal structures were studied by many scientists in-depth.

Phyllotactic processing, based on the main principles governing the arrangement of leaves, is used to optimize access to rainfall or sunlight. Thus, a repeated basic pattern (growth through opposite, or alternate positioning leads to spiral patterns) is implemented; positioning of wheat seeds, or alternate leaves rotation, length and positioning are regulated by golden ratios (or Fibonacci numbers). In fact, the pattern of leaves on a plant is ultimately controlled by the local activity or depletion of a plant hormone, called “auxin”, located in some areas of the meristem.

For these reasons, we get a picture with regular spirals or parastichies emanating from the center of the flower; these spirals can be visualized in interconnecting seeds (or points) whose ranking numbers differ by a Fibonacci step. We can note that the tips of many growing stems seem to be approximately nonlinear (paraboloïdal) and that the Nth point of the flower is located to a distance of √N from the center (Figure 3.12).

As each seed is independent from each other, the optimization of the layout pattern is energy avoidance oriented. Now, if we look out what is happening in a large computer, the component/chip lay-out, in a 3D construct ID of key importance. The interactions, however, are strong in such a feature. Then, the problem consists of assembling a large complex circuitry in structuring the whole electronics in consistent subsets (TCM: Thermal Control Modules) as defined by E. Rent in IBM (no external publication on this subject). What we can say is that a nature-inspired technique has been developed to optimize the organization and structure of the TCMs. Some parts of the concepts have been in-depth studied by Mandelbrot [MAN 97] again; it is based on a power law similar to the following formula:

where C is the number of components, A is a constant value depending on the technology, T is the number of external connectors and D represents the degree of parallelism used in the logic circuitry.

In [MAS 06], we spent some efforts in describing the advantages brought by cellular automata technologies to explain the emergence and arrangement of organizational patterns. We will just remind that the distance (length of an attractor path) and number of attractors are optimized when the K-connectivity of a network is low, that is to say, around “3”. It is a kind of situation we may observe when the sunflower growing is in progress. Under these conditions, the results about number of attractors and distance are about √N and N/2.

We will use the same approach to design the cooling system of electronic assemblies: indeed, the flow of liquid (cold water or fluid) has to be distributed between the different components, in a special way, so that a maximum of calories can be recovered and reused to provide an alternate energy to the complete systems in order to have him functioning in a sustainable manner.

This means that during the optimization of emergence principles, the usage of fractal patterns and self-organizations always enable us to take into consideration some structuring rules superimposed by the nature, then to exploit them in order to better design sustainable systems.

3.11.1. Generalities

In a global and decentralized market economy, we are faced with complex and multivariate systems. Decision-makers is a category that includes quite different people: some people are good experts; they know various things, possess their own specific thinking and sometimes are never in agreement among each other. Later, when a conflictive situation arises, this is most of the time due to information asymmetries and disturbances.

Some of these asymmetries are inherent to the structure and knowledge available in the system. For instance, an individual could know more about himself than does anyone else. Other asymmetries can naturally arise out from a more global economic process:

• – a current employer knows more about an employee than other potential employers;
• – a firm may find out a great deal of information in the process of dealing with his supplier that others may not know;
• – the owner of a car naturally knows the defaults of the car better than others, and in particular, he/she knows whether or not he/she has a lemon, etc. [FRE 10].

In parallel, we are submitted to various deviances due to external forces, the effect of which is similar to a Coriolis force. But we do not realize that the system is deviating since we are working in a static mode in our reference system (indeed, an internal reference system is always moving dynamically with respect to a global system and external forces).

Finally, chirality is a way to introduce a piece of diversity in our life. Here, we will just remind that nature is ambivalent: diversity continuously arises; this requires to permanently integrate mutation and diversity to evolve and adapt itself to a changing environment. So even through there are big trends within the distribution and the processing of these ambivalences, the problem, however, is to take a right decision with imperfect information, and imperfect behaviors or structures as well.

The consequences are going well beyond a mere missing or deviating information, or changing resources and practices. When such disequilibria happen:

• – weak equity means that risks could not be divested. This leads firms not to act in a risk-averse manner, but to integrate anomalous phenomena and behaviors in their operations;
• – people can neither anticipate nor observe these emerging imperfections into their operations;

There are potentially other inefficiencies associated with these deviances. As explained before, some of them are very sensitive, in terms of sensitivity to initial conditions (SIC) and may have a strong adverse effect on volatility.

3.11.2. From rationality to ethics

Deviances can lead to the destruction of a stable situation and dynamically generate the emergence of new orders. Here, sustainability becomes a self-organized property. As said before, only structural rules, instead of operational rules, can be applied to the system. Individuals may also have incentives to obtain information (creating an asymmetry of information), which then leads to the destruction of some existing welfare, working principles and finalities to develop new ones. Here, we address the notion of ethics: even when information or a new structure is available, there are issues concerning its use based on personal consciousness. Indeed, the use of certain kinds of information according to either a discriminatory intent or effect, in circumstances in which such direct discrimination itself would be prohibited, make it possible to hijack a system of its global interests and its overall objectives.

3.11.3. Consequences: highlighted concepts for a new engineering methodology

The objective of this chapter is to propose a good way to integrate uncertainty and unexpected phenomena. It is not an exhaustive underlying methodology to develop sustainable systems, but part of an approach implemented in IBM manufacturing and development a few years ago, to improve the processes.

Just to give a taste of this multidisciplinary approach:

• – asymmetry addresses imperfections and inconsistencies in information and behaviors. It is like a soul or a mental substance acting as a global influence. It is a part of the so-called ambivalences, since asymmetries and symmetries will be merged in a same methodology. The best way to adapt asymmetries into conventional strategies and operations is to control the interactions between the holons, in a holistic manner. Here, ethics is of great interest;
• – Coriolis effect: it can be viewed as a hidden force, permanently applied in an insidious way, to the agent of the system. It requires a lot of reactivity:
• – chirality involves the internal and in-depth architecture and structure of a system. Thus, its intrinsic and external properties and effects are modified. The objective is to increase creativity and to get more flexibility in terms of innovative and adaptive structures;
• – at last, the fractal growth approach is of key importance to elaborate new structures and generate new orders and solutions better fitted to complex systems and customer needs.

Our intent, with regard to these above fields, is to use these underlying mechanisms, oriented, with the following action graph.

Thus, to switch from a physical point of view toward a management and decision scheme in terms of methodology, we can model this transformation sustainability.

TRX represents the transformation process. On the left of Figure 3.14, there are the physical principles we are interested in. On the right, there are new capabilities we intend to develop to achieve a better sustainability.

All the mechanisms and properties related to chirality, asymmetry and Coriolis will be integrated and applied onto the target system. The target system itself, architectured according to specific fractal rules, will be able to receive and support such mechanisms.

As already explained, we are evolving in the field of complexity sciences. Figure 3.14 is a partial graph: it does not yet integrate the notions related to nonlinear dynamic systems, system analysis and emergence (self-organization), or even fundamental physics and life sciences contributions. This will be detailed later, on demand.