Complex Decision-Making in Economy and Finance. Pierre Massotte

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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

       – 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

      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

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