10.2.1. Fundamental rules are quite simple and universal

The action of the interactions existing between agents is a key element of complex systems: our conventional approach based on the concepts of a global function or control of a global system does not apply. Life is not unique, it is not held and tamed in a comprehensive program: it arises from the internal dynamics of its individual elements, each of them being endowed with causalities and interactions that are continuously linked together with regard to simple and universal structuring elements such as:

• – initial master plans, structuring or specific conceptual models;
• – basic ground rules or laws, simple and standardizable, involving general numbers and codes (as seen in fundamental physics, or biology, etc.);
• – feedback loop action which attenuates or amplifies interactive phenomena.

The corollary is that these systems are unpredictable and the only possible strategy rests on two words: responsiveness and adaptivity.

In terms of organization, or rather self-organization, we cannot ignore that any social construction implies to consider some concepts of life and evolution: organization mechanisms of nature, or life, are forcing us to rethink the way we view, analyze or design what is surrounding us.

If we are rapidly evolving, this is because of thermodynamic principles: we are progressively using less energy to perform a same function (eating, thinking, producing, etc.). If we are rapidly evolving, this is also because we are able to create more diversity through ambivalences existing in nature (with offsetting effects due to the feedback loops).

At present, our organization principles are based on common simple concepts, easy to understand, easy to teach: quite often, they are not appropriate to the context of complexity, but we forget to challenge them by intellectual lax: indeed, the fact that we are living in a fractal world shows that the laws and rules governing us are not “global” and universal; as mentioned before, structure of living organisms (a company or a social network is made up of a progressive growth and complexification) is self-similar: their architecture is the same regardless of the level of detail considered.

Within the context or complexity, purely hierarchical systems become obsolete. This has a direct impact on the area of corporate organization and governance; it is also associated with the question related to the distribution and scope of authorities (and consequently, the so-called concept “span of control”).

In this regard, it should be noted that complex systems are not built and work in such a preprogrammed way, according to a predetermined assembly plan. They are governed through the notions of emergence: indeed, they are self-organized organisms: they are the result of a spontaneous creation of stable patterns from disorganized and ambivalent elements.

Each time we refer to either emerging patterns or self-organized phenomena issued from social networks, or interconnected population or organisms, we will make use of concepts such as:

1. Epigenesis

   This is an ability, for a living organism (an embryo, a company, etc.), to grow in complexity by multiplication, differentiation and gradual cell assembly. This is not done from pre-elaborated elements, structures or existing architectures, as expressed in an egg or program. It is, therefore, based on combinatorial growth mechanisms and basic speciations: that is to say, activation or annihilation phenomena, attraction or repulsion forces, in the presence of tiny external factors (e.g. in a bee swarm, and due to a diversified food, a single fertilized egg from the same DNA can produce a queen bee, a bumblebee or a worker bee).

2. Morphogenesis (emergence of self-organized patterns)

   This is a process which consists of self-creating or generating patterns and orders. For instance:

   • – a snowflake and a starfish have a given structure and pattern;
   • – a group of people working together is subject to self-organization and tasks to be performed will be assigned according to the skills, availability, etc., of each one.

   This is directly the result of a so-called “collective intelligence”; several concepts are issued from the same phenomenon: cooperative engineering, development of innovative control systems and implementation of management systems as efficient as possible with regard to our networked environment. This can be to a social organization such as the Web, or even an industrial organization, new project management approaches, etc. Here, the objective is to simply translate and transpose what nature has discovered: the secret to achieving perfect patterns and living systems, or patterns best suited to face a specific situation.

3. Symbiosis

   This is a strong and intimate association, like a sustainable relationship between two or more organisms belonging to different living and interacting species. These organisms are called symbionts: they have complementary properties and capabilities: they need each other to survive, evolve or leveraging their capabilities. It is, therefore, logical to consider them as inter-related systems whose relationships are based on:

   • – cooperation and/or competition mechanisms (with a possible destruction of a population: parasitism);

   • – rational association (commensalism, i.e. profitable to one partner and safe for the other);
   • – coevolution (mutualism related to resources and skills).
4. Homeostasis (stability conditions)

Homeostasis is the ability of a system to maintain a constant internal state, or environment, in an autonomous system (such as a multicellular organism or a prey–predator model).

The variables, or states, we would maintain are very diversified: they can be a population of agents, a temperature, the level of inventories in a corporation, the blood pressure in a body, the production level in a manufacturing plant, etc.

10.2.2. Application: an example of complexification

Let us take the example of biomedical and metabolic pathways in a cell. By definition of epigenisis, we have an increase in organization complexity in assembling many atoms, molecules, proteins, etc. within a cell.

Everyone has seen the DNA model of a protein or enzyme: a macromolecule with a quite complex assembly structure involving thousands of nucleotides, the structure of which is dictated by a nucleotide sequence of genes (the nucleotide itself is composed of G, A, T and C nucleobases). Its final properties result from the folding of the protein into a specific three-dimensional (3D) structure that determines its activity. Hereafter is the representation of the 3D structure of the myoglobin protein [WIK 08] showing turquoise α-helices.

From this elementary model, we can build a more General Metabolic Pathway. At this upper level of an assembly (Figure 10.3 represents an assembly of interacting macromolecules combining the tricarboxylic acid (TCA) cycle), there will be coactions and interactions among the organism’s parts. This is what is happening in a more complex network, including organism-environment coactions, which are a waterfall of biochemical reactions in the cell.

Figure 10.4 shows a graph adapted from the KEGG pathway database that shows wiring diagrams of molecular interactions, reactions and relations in the genome (http://www.genome.jp/kegg/pathway/map/map01100.html). In these two following examples, we can see how the yeast TCA cycle works (the enzymes of the TCA cycle are encoded by many nuclear genes in Saccharomyces cerevisiae) [MCC 03].

The Krebs cycle, also called the Szent-Györgyi and Krebs cycle, or TCA, details how the citric acid cycle is a set of biochemical reactions that represents a stage of cellular respiration following glycosis and decarboxylation of pyruvate. Here, proteins are interacting enzymes which modify and regenerate the network that produces them. It is a kind of self-reconfiguration process, such as we may have, in industry, within the production systems we have developed.

Now, if we consider an additional complexification, we get a still more complex network as represented hereafter. As mentioned previously, this complex network contains sophisticated feedback loops leading to a waterfall of biochemical reactions.

Continuing the complexification, we can see in such a system view individual developments as hierarchies organized into multiple levels (e.g. genes, cytoplasm, cell, organ, organ system, organism, behavior and environment) that can mutually influence each other. The traffic of information, and energy, (or chemical products or electrical signals) is bidirectional because of the positive or negative feedbacks.

For instance, with regard to the CUMC Office of Communications ([email protected], 212-305-3900) we are looking at the global mapping of cancer gene expression changes to the human metabolic network; “increased enzymatic expression across tumors is shown in red and decreased in blue”, said Dr. Vitkup (who provided the image of Figure 10.5).

The role of interactions is of key importance. Indeed, if we consider the structure of our complete human body, we can state that in any system (a human being, an industrial enterprise, an administration, etc.) a central dogma lies behind the persistent trend we observe in biology and psychology to view genes and the environment as making identifiably separate contributions to the phenotypic outcomes of development.

There is no doubt that development is constrained at all levels of the system, not only by genes and environments… since individual emergence appears in terms of autonomous activities: energy → matter → chemical → biological → physical → cognitive → mental behaviors, etc.

Remarkably, we can observe that complexification or self-organization principles are the same, whatever the observed scale level: they give a similar fractal structure based on same basic rules. This is encouraging in terms of solutioning approaches because we know that some source of progress will be available somewhere else.

10.2.3. What is next?

This above development explains why brain, mind and thinking are not just a computer. Again, in this partial example, we did not take into account the notion of cohesive forces, entropy considerations in equilibria, Darwin evolution laws, innate and learned behaviors, etc.

Since cognition, mind and consciousness are the next step of evolution, hereafter, we can see the progressive evolution, through the principle of emergence, consciousness and metacognition concepts, we have developed in this book.

Metacognition, an upper level of our cognitive capabilities, expresses a mental process that consists of thinking about the way we are thinking. It requires activities related to introspection, self-regulation and individuation.

We will have the same for sustainability: the main concerns in our global societies and complex systems will be to implement self-diagnosis capabilities. It is a kind of self-examination and self-testing based on observation, surveys or examination of one’s own mental and emotional states, mental processes, etc.; it is the act of looking within oneself (inside checks) to analyze the tendency, disposition or ability to do something. Some could talk about sympathetic introspection: this means that sustainability is becoming a cognitive concept far from technics or economics.

10.3.1. Closed loop systems

Any control mechanism is associated with feedback mechanisms as expressed in Figure 10.7.

Such a control system can be described as follows:

1. A sensor, also called a receptor or detector: detects and quantifies a stimulus. The stimulus is a value related to the factor or parameter being controlled or regulated. This value can also be a detectable change in the level of the input.
2. An information processing unit or controller, which receives and coordinates an information coming from a comparator (positive or negative adder) and sends out instructions which trigger the action in order to correct a deviation.

3. An actuator (also called an effector): carries out the action that brings about the necessary change needed to return the system either to its original level or to the right functioning level.
4. The feedback loop informs the comparator of any change in the system as a result of action by the actuator or effector.

In such a homeostatic control system associated with a self-corrective mechanism, the inputs and outputs are representative of the same parameter. For instance, the level of an inventory “I” at time “n” (input = I_n, while output = I_n+1), a fluid level in a tissue “F” at time “n” and “n+1”, etc.

10.3.2. Analysis of the feedback loops

A positive feedback loop can be modeled as follows.

In a positive feedback, a small change occurring at a set point generates further changes in the same direction. In most of the cases, the changes are nonlinear since the causes of the deviances are often the fact of power laws. Thus, a positive feedback is usually harmful because it tends to produce unstable conditions. For instance, when a rise in body temperature occurs, a spiral upward may threaten death if this deviance cannot be corrected by a negative feedback.

When applied to the closed loop system, deviations from the set point are not corrected, but made even larger. This results in a process called positive feedback in which a small change in output causes further change in the same direction. Positive feedback is usually harmful because it tends to produce unstable conditions.

Examples of application:

1. In biology, enzymes that control the chemical reactions in our cells operate best within fairly narrow limits of temperature, pH, substrate and product concentration. So, for cells to survive and function efficiently, it is important that the composition of tissue fluid be kept as stable as possible. Here, the information processing system (or controller) will act according to the above control and environmental parameters.
2. In industry, the level of inventories is a function of the order planning, the disturbances coming from the sales services and supplier back-orders. The objective is to reduce the level of inventories without interrupting the final product supply.
3. For example, when the negative feedback mechanisms in mammalian temperature regulation break down, a rise in body temperature can spiral upward and threaten death. However, in certain circumstances, positive feedback can be useful as in oxytocin release during childbirth.

Negative feedback: each time a homeostatic mechanism breaks down the effects of a positive feedback, some equilibrium can be reached. The basic principle is as follows.

As we can see, homeostasis is achieved by a process called negative feedback. A change in the level of an internal factor causes effectors to restore the internal environment to its original level. For example:

1. An increase in the internal body temperature causes the body to lose more heat; a decrease in body temperature causes the body to generate more heat.
2. An increase in defective components (SPQL probability ratio: shipped product quality level) leads to a reduction in the level of final product shipments, while better 6-sigma ratios will cause a manufacturing plant to better adjust its shipment ratio.

This type of system, in which a change in the level of a factor triggers a corrective mechanism, is called a self-adjusting system. Again, we have to note that in a networked system, the number of negative feedback loops must be an odd number.

10.4.1. Networks, social networks and Web applications

Figure 10.10 represents an interconnection system: in fact, it is the image of the Internet network. This network comprises direct links (highways) between clusters of servers (each one being mainly based on a star-oriented structure). There are, however, some redundant links between about 1 billion servers which enable us to structure the global network into subnetworks, and then to manage them independently of one another.

In terms of structure, it first seems to be a hierarchical organization based on a woody perennial plant (like a tree): it has a single stem or trunk growing to a considerable height and bearing lateral branches at some distance from the ground. In fact, everything can be interconnected and we can say that we are connected to a networked network (network of networks), without clearly identified hubs, consisting of millions of networks both public and private, academic, commercial and governmental, which connect in a closed way 6 billion digital accounts, accessible via mobile internet devices (MIDs).

Figure 10.10 [WIK 08] was produced by the Oxalide Shamrock company, highly skilled in the design and management of autonomous and secured networks: as we can see, everything is based on a K-network connectivity, where K is the number of main links between servers.

This structure is not the direct accomplishment of a human. It is the result of a spontaneous complexification over time. What is remarkable is that the Web is a complex self-organized system: it evolves and adapts continuously to life needs and events. This network is so complex that it would be delusive to preimpose a given order, structure or planned functioning. As it is dedicated to information systems, we will discuss cognitive complexity: a new mode of governance, new kinds of knowledge will then emerge.

As detailed at the beginning of this book, from a molecular network, through chemical neurotransmitters, then neural networks, network brains, social networks and then the global Web network … these are the same underlying mechanisms that govern complex systems.

Let us just make a comment about the development of social networks: in terms of evolution, we are just in the middle of Figure 10.11. We have not yet integrated in our society the emergence of the impact due to social networks.

Now, concerning sustainability, it will follow the same self-organization and complexification principles: from simple and local sustainabilities, we will reach a global and cognitive self-sustainability. We do not know what kind of specific sustainability will emerge from a complex system, because nothing is predictable, but most important is that we will get some sustainability based on a new paradigm that I did not intend to develop yet.

10.4.2. The brain: the evolution of the human species is in continuous momentum

It has long been focused on technological, economic and social upheavals of humanity. Today, it is primarily concerned with changes in the environment and climate, and once again people put pressure on the energy consumption issue throughout the world.

In terms of evolution and complexification, however, we must insist on the fact that biological evolution is going slower and deeper than cultural evolution. Consequently:

• – it intrinsically modifies the organisms themselves;
• – new forms of social organization have always replaced the more archaic ones;
• – culture, helped by the emergence of information and communication hypertechnologies, will support and foster that complexification: it becomes a driving force of evolution, creating stimuli in the brain, promoting and developing interactions with other agents and increasing functional abilities.

These observations lead us to draw several lessons:

1. Interactions between genes and culture are very strong: our brain, being in permanent contact with the outside world, continuously receives new signals: it interacts with social groups and mimics them; it receives cultural information and changes its viewpoint; it is subject to huge technological and psychological pressure and turns to immersion. Consequently, a brain’s mental capacities evolve.

   A living organism, a species, never regresses; it evolves continuously toward a better or worse adaptation. Our brain is ductile and malleable: it is designed and scheduled to be adaptable and reconfigurable at any time. So, it has exceptional self-learning capabilities.

2. However, as for any advantage, the brain’s plasticity is not free. In terms of cost, it consumes more energy. It is more susceptible to diseases: recent DNA mutations have altered some parts of the genome that were once quite stable … for tens of millions of years of evolution. The evolution of the brain is now deregulated, it became free from certain constraints of nature. Therefore, individuals who stray too far from current standards are becoming “out-of-specification” and are eliminated from the reproduction process; it is the same for those agents who become weaker and weaker since they are more exposed to pathogens surrounding us and become sick: they will disappear.
3. Genomic information controls the evolution and developmental processes in living organisms. It shows that minor differences in gene sequence or regulation can result in striking differences in shapes and forms. Thus, we are programmed to learn and respond not only to provide appropriate responses to uncertainties, but also to adaptively respond to unexpected or unpredictable events.
4. Finally, what is remarkable is that the man who is not immune to biological evolution is now able to change part of these rules. He has changed the environment to his advantage, he can manipulate the genome of other living species (e.g. the selection and propagation of plants, creation of specific genetically modified organisms (GMOs)). He knows how to control the fertility and regulation of birth and how to manage the information technologies to improve storage capacities and mental information processing; now, he is going to be able to change his own genome, that is to say his own biological structure.
5. Within this framework related to mind and consciousness, homeostasis is not only devoted to stabilizing a situation ad vitam aeternam, but to monitoring its progress, to regulating disruptive and unpredictable phenomena. Finally, the objective is to allow a harmonious complexification of the whole. Thus, due to learning about the underlying mechanisms of Nature’s evolution, homeostasis is able to regulate the design or the emergence of new cells and new neural interconnections in the brain, in order to enhance specific functions (biological or mental) and delete some others.

Generalization: what is the impact on sustainability?

In any domain, fabricational evolution impacts either the systems under study or the technologies associated with its measurement and control. This is the case for the sustainability.

Sustainability is to society what the brain is to man. Focusing on energy issues is a mistake because, naturally, the complexification is everywhere: it is an irreversible phenomenon that consumes more and more energy over time and rejects more waste heat.

Its function is to allow society to continue evolving smoothly, and to provide him with new coping skills and happiness.

Sustainability is no longer a fixed constraint to save assets, prior learning or gained experience. Its aim is to preserve some beings from random vagaries of life, protecting us against the development of some aspects of our environment, saving some kinds and types of our environment, fighting against adverse effects of technological innovations and … to slow down some questionable evolutions. Sustainability is an elastic and malleable concept: it is there to allow the development to continue and be in the best possible conditions … in the public interest.

10.5.1. Impact on risk management

In terms of risk management, we often talk about the need to anticipate, but what kind of anticipation? What kind of event? As apposed to what most people are thinking, we cannot know what specific event will happen and when. On the other hand, one decision-maker can anticipate or imagine a disaster or a major disruptive event that could plausibly happen, thus defining and establishing some scenarios.

Within this decision theory and sustainability context, things are never simple:

• – the role of experts is limited to identifying possible assumptions and assigning them a probability of occurrence (that is sometimes a subjective probability);
• – the manager’s decision is based on irrational beliefs (emotional aspect of a decision) as well as on rational trusts.

  This perhaps explains why it is easier to make convenient adjustments rather than a full reconsideration of the usual paradigms. We can quote two examples:

• – Benoît Mandelbrot’s works [MAN 04] remained unheeded for a while, despite the occurrence of several recent economic crises;

• – while many physicists are intensively working on the unification theory or the globalization of the standard model, some technical experts in more conventional areas do not ask the question of what impact the Pareto distribution, the power laws, biomimicry, scale invariance or even network theory may have on our economy or society: there is a whole field of enhancements to exploit. Thus, a substantial work remains to be done: it is related to the general theory of organizations.

Now, discussing sustainability, it is the field of organization theory where a lot of improvements can be made.

In complex systems, we cannot reason in terms of optimization, but in terms of balance: in a network having a complicated and highly interconnected structure, convergence toward a strange attractor is hard to achieve. Nevertheless, it depends on:

• – the number of interactions to which we are connected;
• – the number of elements or agents considered in the network, etc. Just to measure the problem complexity, we recall that, currently, in the “so-called” complex Web, we can be in connection with billions of people in less than 20 clicks.

Managing a complex system in a conventional detailed manner is quite pretentious: we cannot control and plan everything over time. We are just able to establish some general behavioral rules and practices based on ethics or morals. Thus, it becomes possible and preferable to play more with the peer-to-peer capabilities of a network and try to find a kind of Nash equilibrium: working local to achieve a global result (which is the reverse of “think global and act local”).

10.5.2. Impact on system sustainability

Another way of thinking is related to the evolution of sustainability itself. As observed in this chapter, sustainability is no longer an economic, technical or ecological concept. In the past, we could improve system sustainability by using automated approaches, following predefined rules and procedures, using programmed or algorithmic actions, etc. Now, according to the complexification and evolution of our systems, we have to introduce more evolved notions to attack the so-called cognitive fields associated with information, cognitive processes and consciousness, etc. This is the reason why sustainability will progressively be included in its approach: as complex systems are arising, in a more observable way, we cannot embrace this new paradigm with old concepts such as rationality concerns and claims of certainty. This evolves to include notions such as “common sense”, “judgments of truth”, “understanding” and notions of “artistic appreciation”.

Here, “common sense” is a basic ability to perceive, understand and judge things, systems or situations …. shared by nearly all people: under these conditions, we understand that information about an item is reasonably expected of nearly all people without any need for debate.

The term “common sense” is not a claim of certainty. It is not a pejorative word, it just expresses a consensus, experience, know-how, a trick or good practice. It is not a decision at random since it represents a good axiom or solution needed when science, logic and complexity become powerless in proposing the right action.

We are in between rationalism and empiricism. Thus, we see why in order to reach a best fitted sustainability, the holistic and more cognitive approach is much more powerful than the holonic approach itself.