Pierre Massotte - Complex Decision-Making in Economy and Finance

– the same is true in logistics, with the possibility of organizing a round of distribution in terms of means of transport where the optimization of the route also requires these techniques;

– in the field of Information Technology, IT systems can be dynamically reorganized to deal with problems that can evolve over time without the intervention of an external operator. Such a system could be adapted to the current context, and therefore to possible disruptions, through learning (supervised, unsupervised, reinforced, etc.).

In conclusion, a system with the capacity for self-organization has several states of equilibrium, i.e. particular organizations. Each particular organization is characterized by a set of different initial conditions that, when verified, converge the system to a corresponding stable organization. Most of the time, the self-organization system is between one or the other of its equilibrium states at the end of a time cycle that can be determined. It moves from one organizational state to another under the disorganizing pressure of its environment. The system that can adapt to changing circumstances by modifying the interaction structures between its components has the potential to achieve some consistency in environments with a high degree of uncertainty or change.

In a simple system, i.e. with a reduced number of elements, feedback loops ensure homeostasis. As a reminder, homeostasis is the property of a system to be able to stabilize around a given operating point. For example, a simple temperature sensor or detector, combined with a temperature controller, can act to keep the temperature of an enclosure between two limit values. The actual temperature value is then compared to a predefined threshold value and any excess is used to activate or deactivate the heating or cooling system.

More sophisticated and progressive approaches are also available, such as those used in the human body. The latter wishes to stabilize certain physiological constants at a given value, for example, to keep the temperature of the human body at a stable temperature of 37°C. Temperature sensors (neurons in the hypothalamus) can detect variations in the order of 0.01°C. Any excessive deviation makes it possible to activate compensation mechanisms that are not simply of the “go-no go” type but graduated according to the situation. Excessive body temperature triggers sweating and dilation of capillaries and certain blood vessels. Too low a temperature causes opposite effects, as well as shivering and an acceleration of the metabolism.

Many similar examples exist in chemistry, metabolism, the immune system, etc. where the system is able to regulate itself, i.e. to regulate its own functioning. In social systems, communication techniques between agents, based on game theory, make it possible to define very elaborate strategies whose evolutions and results are impossible to guess. Indeed, several elements specific to a complex system are taken into account:

– there are many interactions in a given neighborhood;

– each element modifies not only its own state, but also that of its close neighbors, according to rules with a low visibility horizon;

– the objectives are local, but they often overlap those of the neighborhood and are in conflict with others;

– each element tries to improve a number of its own properties and reduce those that are less valuable or less effective in relation to a given criterion.

Which advantages can we advocate for the method presented in this chapter?

Firstly, that tackling complexity is an opportunity to design and develop the sustainability function in complex systems. Secondly, that it leads to reaching a global and best fit objective by means of local rules. In fact, tackling complexity is a way to get a system evolving towards a chaotic attractor. While this obeys simple principles, it leads to disruptive change.

As a result, new patterns may emerge through the disruptions. Thanks to the diversity and adaptive properties at the local level, associated with aggregation ability, the system can eventually reach stable patterns.

Finally, thanks to interaction and feed-back loops within the system under development, it is possible to generate more sustainable and stable systems. And the benefits can be expressed in terms of flexibility, stability, reliability and controllability.

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