I.3.1. Factors that cause complexity

In our society, a cohort of factors lead to ever greater complexity. They are linked to certain levers such as globalization, demographics, consumer profiles and expectations, natural resources, the environment, regulations and militant protests [LAU 05]. Currently, when we analyze a socio-economic system, we do not yet know to which destiny it will converge:

• – Towards a patchwork world?
• – Towards a complex community of dungeons and fortresses?
• – Towards a world of communities with open borders?

This actually is a question of entropy. Entropy being everywhere, and the second principle of thermodynamics which governs our environment, it stipulates that entropy – generally associated with the concept of disorder, randomness, or lack of structure and organization – is continuously increasing. Thus, we cannot predict what the future will be, either in terms of molecules, living organisms, our consciousness, business evolution, etc. Nevertheless, we know that some limits exist, since, for instance, black holes in the universe already possess a huge amount of entropy. Moreover, by analogy, in business there are also dark environments or dark information that are able to modify part of the entropy sources, and then able to delay the emergence and occurrence of a deterministic and unpredictable chaos.

Practically, in each scenario, the consequences are multiple and can concern the integrity of countries, the development of terrorism or wars, industrial and information systems insecurity, the acceleration of research and development and so on. But we cannot know in advance the importance of these impacts. On the contrary, in this complexity process, it is possible to control the evolution of certain factors such as:

• – the number of parameters to be considered;
• – the gradual shift from an information-based society to a knowledge-based society and, in the future, to a society based on awareness and on relationships;
• – the organizational model. The fluidity of a society based on the sharing of information, knowledge and know-how requires the networking of the various actors in a same economic model, of the same community of interest, which becomes the driving force of growth.

The increase in complexity can be observed in these socio-economic systems, where globalization remains an overall trend. The telecommunications industry is at the core of the need for sharing knowledge and expertise, and its backbone is the Internet and the Web. On the way, the Peer-to-Peer model is the preferred catalyst. As for the supply chain, it operates in increasingly open, international and transnational modes and includes more network partners, thus more complexity. In all these situations, it is the user who places himself at the center of the systems and who induces, in a relatively autonomous way, the interrelationships between the set of users.

Within our developed societies, the impact of connected user populations on complexity has now become paramount. Their accumulated effect induces a resulting chaotic behavior and turbulence. A simple example, the MP3 audio compression and transmission standard, having been adopted off-market by masses of autonomous users, led to the advent of a collective lifestyle adapted to these same masses within less than a decade. There has been no shortage of turbulence, particularly with regard to institutions that owned audio content or are in charge of distribution: they are threatened by the new modes of sharing and expression. Knowledge society is highlighting the semantics of the interrelation of these autonomous agents. The marketing of the knowledge society is now capable of understanding market turmoil, in particular its emergence, growth models, dissolution, competitive ecology, cooperation schemes (tailor-made partnerships and alliances), technology substitution models and their extinction and so on. The MP3 battle was a business model for other players to come into play, based on the shift from the traditional logistical supplier/distributor pair to the polar “influencer”/consumer pair. The first model punctuates the existence of suppliers and users by opposing them. The second model instead emphasizes the ability to prescribe, therefore influence, through a network of influence, and this transcends the previous duality. The influencer is accredited by the network because the consumer becomes a source of creation. The values created are very diverse: they concern knowledge, the economy (values and wealth that make it possible to materialize acts of creation and innovation), technology (which calls for more technology), etc. In the case of knowledge, for example, value is created from available information and data. Since the consumer-customer is at the center of the system, it is, in the same way, a source of complexity for the resulting system.

I.3.2. What is missing or penalizing us today

What is missing today for the proper management of complex systems? Certainly, neither cultural factors nor tools are yet in place. Here is a list of some of the factors and barriers that need to be addressed by managers, consultants and educators at large:

• – An appropriate way of thinking. There is still an intrinsic difficulty found well spread among the human species to consider complexity natively. It is used to thinking locally as much as acting locally!
• – A culture. The knowledge and experience acquired by humanity allows us to develop certain faculties of the mind such as a critical sense, taste, judgment and discrimination. We cannot escape the constructivist grip of culture, which touches the very roots of our nervous system, perception and interpretation of the world; it escapes our control, exposes our consciousness, and finally acts on our human and sociological behavior (fear, opposition, adherence to change, etc.).
• – Scaling. While the notion of size is not in itself a relevant or determining factor when talking about complexity, the challenge remains to scale, increase or reduce the size of systems while preserving the dynamics of the interrelationships among the elements of the system. This is the transition to scale challenge. There are still too few studies and results for an appreciative and reliable engineering scaling approach, particularly in the fields of society and public policy.
• – Other types of intelligence as specified previously.
• – Some technological limitations. A number of issues and problems remain unresolved due to the lack of tools and approaches to resolve them in a computable time. These include: disaster prediction based on low “noise”, uncertainty control, precise control of chaotic systems, etc. Another level of limitations, one of learning (or deep learning), we do not know how to integrate common sense into our decision support systems (some readers may remember the famous CYC encyclopedic project of the 1980s) and the all-time notion of emotion. Similarly, at the problem-solving level, computers are often called upon, but without knowing whether applied to nonlinear dynamical systems (NLDSs) such as Navier–Stokes equations, whether they have solutions, and if so, whether accessible for calculation or reasoning and so on. Finally, at the behavior and evolution of the population level in general, we have analyzed the characteristics that intervene in decision-making processes; these characteristics such as altruism, comperation (competition then cooperation) or coopetition (cooperation then competition) are important, but, in terms of convergence, we cannot predict whether this or that solution is the most appropriate.

I.4.1. Strategic risk management

Various events of this century (say the attacks of September 2001, the viticulture crisis, the textile crisis, etc.) considerably change the context, and also the perception of what is commonly called “risk”. In the frame of reference of this book, risk translates into physical or human losses, and greater vulnerability to global economic, technological and social change. We live in a complex world full of uncertainties and the two questions that first arise are: what awareness do we have of the potential risks? What is the impact of low noise or ICS on a system for which the consequences will be incalculable?

We are subject to an immense variety of operational risks and local disruptions which impact our entire interacting network. At the approach level, lots of effort and money are being spent to keep systems in their current state, to reduce potential risks and the risk of a downward spiral. But is such an approach relevant? By recalling the concerns of economy, they mainly concern: the increase in companies’ turnover and economic growth, the reduction of costs, the increase in market shares in the most developed countries or in those towards which purchasing power is shifted, and finally, better access to new knowledge. Similarly, competitive pressure is forcing companies to move beyond their local or national framework to export.

These combined phenomena will complicate the processes to refine and optimize them. Concurrently, operations are being internationalized. There, known and unknown risks increase. For instance:

• – outsourcing implies an improvement in productivity, a reduction in costs, as well as higher logistics costs (costs of delays, costs of risks related to stock shortages, etc.);
• – the creation of subsidiaries abroad leads to higher transition costs as well as to more difficulty in control of strategy and operations;
• – no longer being hesitant to relocate a growing part of the value chain. Outsourcing is based on often fragile networks: we are in an unstable and unpredictable mode, higher organizational costs, etc.

Some economists note that the notion of cost or productivity alone is no longer sufficient: certainly, manufacturing and technological costs have been reduced, but changing context and culture leads to an increase in social cost.

Thus, given that studies are generally not conducted in a comprehensive manner, it is difficult to measure the impact of changes and the new complexity of our systems. In which direction should we go? Realizing that you cannot understand everything, you have to change your paradigm and focus on two points:

• – Strategic Risk Management. Without going into detail, this method implies that it is necessary to identify high value-added processes, key infrastructures and assess their vulnerability.
• – Strategic Opportunity. The world is characterized by rapid change, it is volatile: customers change, so do their aspirations, tastes, preferences, wants and needs. Choice is not allowed because gaps between countries are widening and, if one does not respond to new needs and challenges, one will be overtaken by its competition. It is therefore a question of seizing opportunities and “conditioning” oneself in this sense.

I.4.2. The role of intelligence

Within the complexity maelstrom of systems, the interactions and the autonomy of agents are involved, which help create new orders. We are often confronted with the consistency of these orders with respect to the expectations of managers, consumers, clients or owners of the system. The only problem here is related to the fact that we only take into account analytical intelligence, and we model and integrate it into the programs we develop. Of course, as already mentioned, improvements have been made with the inclusion of “common sense” knowledge (e.g. à la CYC – Cycorp (society)), but this is far from enough. Indeed, this form of intelligence is too systematic and cannot by itself account for an individual’s profound satisfaction, the adequacy of a solution, a social, professional or sentimental success, etc.

In fact, in our brain, with regard to Gardner’s works [GAR 06], intelligence is considered as an ability or aptitude to manage some human being functions. Thus, human beings possess nine different kinds of intelligence, in addition to the logico-mathematics (here above mentioned). Here, we will note nine of them:

1. 1) musical-rhythmic (harmony, rhythm, equilibria, etc.);
2. 2) visual-spatial (organization, geometry, hierarchy, architecture, etc.);
3. 3) verbal-linguistic (manipulation of words and languages, concepts and semantics);
4. 4) logical-mathematical (modeling, cognition, reasoning, etc.);
5. 5) bodily-kinesthetic (control of motion, of physical expressions and actions);
6. 6) interpersonal (emotional intelligence, psychological);
7. 7) intrapersonal (empathy, ability of understanding the self; global and social strengths, interactions and weaknesses around us);
8. 8) naturalistic (ecology and sustainable receptiveness, including ethical and holistic understanding of the world);
9. 9) existential intelligence (related to consciousness and resilience: spiritual, moral, of deontology, ethics, etc., related to the concept of inclusivity and over-mind constructs).

All these types of intelligences lead to considering the so-called “smart” devices, oftentimes based on artificial intelligence programs. This book does not address artificial intelligence per se, as AI is but an enabling technology made to implement and automatize the different concepts and abilities relative to intelligence.

The example hereafter will introduce emotional (interpersonal) and social (intrapersonal) intelligence. It is indeed necessary to take into account a form of intelligence called “emotional intelligence”. It allows us to recognize, interpret, process and manage emotions, an essential concept for modeling and understanding the relationships between reason and passion, as well as the development of social relationships. As essential steps in a decision-making process [GRE 05b, DAM 05], emotion, reflection and reason are inseparable and it has often been shown that decisions taken “coldly”, in an “objective” manner and in the absence of emotion, sometimes turn out to be biased in relation to the objective pursued. In any decision-making process, it is important to be able to detect the profound feelings of partners, to discriminate against them and to anticipate events or consequences of actions to be taken according to their sensitivity, character and psychological state. This conditions the partner’s potential acceptance and adherence to the final choice or proposal for a decision.

With analytical intelligence, we manage information by bringing in knowledge and know-how while remaining in the objective field. With emotional intelligence, we rather manage emotions and feelings, by putting into play the notions of accomplishment and recognition: here we are in the subjective field.

Finally, and for the sake of completeness, it is necessary to mention social intelligence, which is the ability to perceive one’s inner motivations and behavior as well as those of others and to act accordingly. This should help us to become what we have chosen to become and it also allows us, in a group or network, to have access to agents where everyone is in harmony. Social intelligence is the ability of an individual or group of individuals to work in a group, to share values and to integrate into a common project. It is an indispensable criterion and a prerequisite for what we have discussed in the previous chapters, namely cooperation, from which collective intelligence emerges. Much work still needs to be done to take all these concepts into account in current decision support systems.

I.4.3. The role of public institutions

This section defines a new role and mission for public institutions and administrations.

Since the Industrial Revolution, institutions and administrations have been abused by technology and much more recently by the mass liberation of consumer power. It is important that they regain their true mission, probably to preserve the integrity of all kinds of networks. Indeed, network law, “netiquette” and exchange protocols are all focal points for 21st Century administrations. Until the last century, an administration was mainly concerned with activities related to the passage of human action: the law of blood (transmission of inheritances, etc.), soil (management of borders and space at the level of individuals, commercial goods, etc.) and time (administrative cycles, etc.). Now, it must restructure itself to integrate the advances and challenges facing our information society. These new focal points are left relatively vacant, only to see the private use that is sometimes made of models and practices of general interest. For example, a company filing a patent for a general business model, a type of technology transfer, a good practice, all elements of the knowledge society that would probably benefit from being infiltrated into the public domain in order to promote the emergence of scaling, thus freeing and multiplying the economic value of the whole. It can be seen that these elements of change and evolution are only slightly influenced by political changes in power.

This makes it possible to reposition the role of the administration: it must be the dynamic link between the individual and society, a society where the individual can experience his or her spontaneous and dynamic relationship with other individuals. This relationship is here called participation given that the consumer perceives an increased level of participation as an overall value. From the individual’s point of view, power is in sharing, following the famous slogan of Ed Feigenbaum (Professor at Stanford University and founder of Teknowledge in the early 1980s) “Knowledge is Power”. The role of an administration is to prepare and make possible the entire economy. Yet, such orientation, which we have sometimes called the “business of wholeness”, can suffer from many disruptions (economic and trade withdrawals, customs barriers, preservation of local interests, etc.) which can hinder the availability, dissemination and exploitation of knowledge.

Here again, we are faced with a complex problem, in the sense that it is vital to ensure the effective and efficient functioning of a global societal system. The question is: how can we benefit from the overall technological evolution to solve societal problems? However, advancing science and ensuring economic growth cannot be “driven” and managed only by policy-makers [CAC 05]. Such an evolution must correspond to the needs of end users and be planned by them according to their demand. Indeed, it is individuals who share the vision of the firm’s concrete needs, who sanction the economic value of an innovative project according to the “benefit” they see derive from it, who finance, directly or indirectly, the development of scientific progress and who take the technological and economic risk of research and development. An administration can only play a catalytic role and not an administrator role because the sources of progress cannot be altered! Just think of public R&D program subsidies, for instance.

We do not believe that the 21st Century can, in the long term, be anything other than ecological, in the original sense of the word. This word recognizes interrelation as a dual factor of entities (individuals, objects, etc.) and accrues an expanded value to it. Hence, we considered it useful to pave the way towards an engineering of this “ecology”, through the signs of complexity science engineering. We hope that this book will stimulate the reflection and activate corresponding work. With its importance, this work has also become urgent for our society.

I.4.4. Structure of the book

This book is intended to, by describing our previous experience, provide a technology for maximizing the returns on investment, in terms of time, money, reputation, etc.

During their lifecycle, organizations, from matter to living organisms, enterprises, up to our brain and mental constructs, are evolving together and ever growing. They are inevitably faced with the “complexity problem”. Indeed, in order to grow, they embrace more and more complexity in the way business is presently conducted: they include ever more functionalities, interactions, control mechanisms, etc. Thus, and according to Gödel’s theory², systems are associated with the failure to manage this complexity, thus creating vast uncertainties, unexpected (chaotic) behavior and uncontrolled risk. Complexity theory refers to multidisciplinary skills in both natural, mathematical and social sciences.

The first part of the book (“Dealing with Complexity”) is meant to willingly address the engineering and re-engineering situations found in today’s world, to understand and clarify the vision, complexity principles, with people’s alignment and their role in resolving or integrating complexity without overshadowing facts, data and objectives, and other factors. Meanwhile, the second part of the book (“Dealing with Risk”) opens up the discussion about risk analysis, the anticipation of new risks pertaining to complex environments and organizations, their operating conditions, in particular the financial and energy domains. Together, the two parts propitiously complement in providing food for thought and poise to action for the reader.