Complex Decision-Making in Economy and Finance. Pierre Massotte
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.
1 1 The work of the late American consultant Stephen Covey is fundamental in this respect.
2 2 For Kurt Gödel’s incompleteness theorems, see, for example, https://en.wikipedia.org/wiki/Gödel%27s_incompleteness_theorems.
1
Engineering Complexity within Present-Day Industrial Systems
1.1. Introduction
This chapter describes some new basic concepts and mechanisms applicable to industrial systems and organizations. The resulting properties are necessary to analyze them and provide them, from their design stage, with the adaptability and reactivity required by the new challenges encountered in today’s economic world.
1.1.1. Reference definitions
In this chapter, we will refer to “system”, in Churchman’s sense [CHU 92], as any set of elements coordinated to achieve an objective. By “industry” we mean all economic activities that produce material goods or services through the transformation or implementation of added value on basic components or raw materials. Thus, a software development center, a production system, a manufacturing workshop, a travel agency, etc. are industrial systems. The study and analysis of a complex industrial system is based on its modeling.
Historically, we have first retained the quantitative aspect of the systems studied, then more recently, the qualitative aspect, for example through knowledge-based systems (KBS). The notion of complexity that we have just become aware of has been processed by the techniques of artificial intelligence, but the approach has remained based on the fact that we can mathematically formulate a problem using parameters, variables and algorithms. This “Galileo principle” assumes that the system is predictable and that there is no ambiguity; the state of a system at a given time is assumed to be able to determine its state at any subsequent time. It is therefore purely a matter of determinism. More precisely, we will call determinism the theory according to which causal laws govern all things in the universe.
Any event can then be considered as the effect of previous events and as the cause of subsequent events; the successive natural states then follow one another as if by necessity. Laplace [LAP 25], a strong supporter of Newtonian mechanics, formulated the theory of universal determinism, and this theory has been considered a “religious hypothesis” for a very long time. Thus, by knowing at a moment’s notice the position and speed of each particle in the sky, it is then possible to know the future of the universe. This assertion is theoretical because one cannot make predictions from a present state due to instabilities that amplify errors or minute variations in a system.
This deterministic approach always remained in force when physics underwent its second revolution with the publication of Albert Einstein’s theory on the “Theory of Special Relativity” in 1905. The “space-time” structure was then introduced, and it has shown that any event, in order to be described, must be related to a four-dimensional spatial-temporal continuum. The notion of space-time is then the only one that can be described as absolute. However, the intellectual approach remained the same.
These efforts in the search for truth have always favored the current followed by theorists, and more and more phenomena have been described, explained and interpreted. Advances have made it possible to achieve immense scientific progress and better control our environment. By applying such principles in specific areas of industrial engineering, such as planning or scheduling, we could say: “if we know the position, condition and manufacturing process of each product in a production system, it is then possible to precisely determine the situation and condition of that system in the future, as well as how it will evolve”. This obviously requires in-depth and rudimentary knowledge of technical data such as nomenclatures and ranges, a technical description and history of products and processes, etc.
However, considering multi-product and multi-process systems involving hundreds of operations and references, we cannot calculate or predict a specific event at a future time. The same is true for the dynamic behavior of this system: this is simply due to the application of the principles of uncertainty as defined by Heisenberg and to nonlinear amplification phenomena relating to the system under study. In addition, our scientific theories are more and more elaborate, and mathematical formulas and demonstrations are more and more complicated, in order to be able to identify the increasingly imperceptible, growing and hidden difficulties in phenomena.
On another level, the change in industrial needs