Complex Decision-Making in Economy and Finance

Pierre Massotte, Patrick Corsi

• Publisher: Wiley-ISTE
• Year: 2020
• Print ISBN: 9781786305022
• eBook ISBN: 9781119694984
• Edition: 1
• Topic: Business
• Subtopic: Management
• Index: Business

Pertinent to modern industry, administration, finance and society, the most pressing issue for firms today is how to reapproach the way we think and work in business.

With topics ranging from improving productivity and coaxing economic growth after periods of market inactivity, Complex Decision-Making in Economy and Finance offers pragmatic solutions for dealing with the critical levels of disorder and chaos that have developed throughout the modern age.

This book examines how to design complex products and systems, the benefits of collective intelligence and self-organization, and the best methods for handling risks in problematic environments. It also analyzes crises and how to manage them. This book is of benefit to companies and public bodies with regards to saving assets, reviving fortunes and laying the groundwork for robust, sustainable societal dividends. Examples, case studies, practical hints and guidelines illustrate the topics, particularly in finance.

PART 1
Dealing with Complexity

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 nowadays imposes more stringent requirements to take into account technical (dismantling, reuse of components), social (customization of products), ecological and economic (pollution, energy savings) constraints. These constraints are grouped under the name NMPP (New Manufacturing Production Paradigm) and their consideration in future industrial management systems raises adaptation problems in terms of approaches, techniques and methods. The transformation of a discipline is attributed to the development of a new technique or the use of a new technique that has been ignored until now. In fact, it is never a question of developing a new technique: it is the nature of the results obtained that prevails and enables us, compared to other approaches, to implement or reveal new properties.

In the following, we will focus more specifically, after defining it, on the behavioral complexity of a system and explore the notion of deterministic chaos. Its nature is fractal and we will see how to exploit its properties in complex industrial systems.

1.1.2. What are the problems to be solved?

The ultimate objective – the purpose – of a complex system is the gain, which means that it tries to achieve, collectively, an overall objective. We therefore continue here on the topic of intentions, but not on firm and rational commitments, rather “safe” strategies to achieve an overall optimum. Indeed, in a programmable network, comprising many interacting elements and complex behavior, we are confronted with strange attractors; we cannot predict in advance the attractor on which we are, at which precise point in the cycle we are and at which precise horizon we will have converged. Methodological elements need to be defined to identify which sets of objectives can be achieved.

We can be inspired by the approaches used in the game of checkers for example. In this case and according to a context, each partner, when playing, chooses a tactic and explores the moves as much as possible; he or she tries to anticipate the opponent’s reactions, evaluates them and decides on the least bad or best possible movement. By doing so, the player optimizes an economic function over a given time horizon. On the contrary, to accelerate the process and according to past experiences, he or she will be led to carry out reflex actions that are the result of winning repetitive strategies and that will have been the subject of successive learning. In this simple example, relating to a specific game, we see an interesting approach emerging:

• – it is a system in which agents are intelligent (i.e. with behavior capable of emulating, in part, that of the human brain), autonomous, capable of communicating or exchanging information with partners or agents, with whom they are in a competitive or cooperative situation (hence the notion of conflicting objectives!);
• – it is a situation and a mode of operation that we encounter, to a greater or lesser extent, in any distributed system and whatever the field considered. For example, we can cite decision-making problems in industry, the evolution of the immune system in a cell, the phenomena of metabolic adaptation in a living being, the flexibility of the behavior of a population of individuals in the context of the human and social sciences, etc.;
• – understanding how global objectives and behaviors can emerge is a key factor in guiding the evolution of complex systems at the structural, organizational and operational levels, in order to move step by step towards a predefined goal.

Many other examples exist in chemistry, economics, metabolism, the immune system, etc. However, 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 come into play:

• – 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 it is common for them to overlap with those of the neighborhood and to be in conflict with others;
• – each element tries to improve a number of its own properties and reduce the least valuable or effective ones in relation to a given criterion.

From these examples, it can easily be deduced that the strategies and tactics commonly used in production or industrial engineering are not applicable here. Indeed, the systems we currently have are not decomposable and are nonlinear. Moreover, because of all the existing diffuse feedback, it is impossible to start from a global objective (linked t...

Table of contents

• Cover
• Table of Contents
• Introduction: New Beginnings
• PART 1: Dealing with Complexity
• PART 2: Dealing with Risk in Complex Environments
• Conclusion: Different Types of Crises
• List of Abbreviations
• References
• Index
• End User License Agreement