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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
Faced with ever-increasing complexity on a daily basis, the decision-makers of today are struggling to find the appropriate models, methods and tools to face the issues arising in complex systems across all levels of global operations.
Having, in the past, resorted to outdated approaches which limit problem-solving to linear world views, we must now capitalize on complexities in order to succeed and progress in our society.
This book provides a guide to harnessing the wealth inherent to complex systems. It organizes the transition to complex decision-making in all business spheres while providing many examples in various application domains.
The authors offer fresh developments for understanding and mastering the global “uberization” of the economy, the post-modern management of computer-assisted production and the rise of cognitive robotics science applications.
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Veröffentlichungsjahr: 2017
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Table of Contents
Cover
Title
Copyright
Preface
Acknowledgments
List of Acronyms
Introduction: A World Swept by Complexity
I.1. Our changing world: benchmarks, transformations and futures
I.2. New relationships of uncertainty
I.3. It is still and always will be Descartes who instructs us
I.4. Is the problem-solving approach sufficient?
I.5. The new paradigm of complexity
I.6. Which trains of thought guided us?
I.7. Let’s develop the focal point of this book
I.8. The structure of this work
PART 1
1 The Foundations of Complexity
1.1. Complexities and simplexities: paradigms and perspectives
1.2. What is the prerequisite for the handling of a complex system?
1.3. Applications: industrial complex systems
1.4. Time to conclude
PART 2
2 Evidencing Field Complexity
2.1. Introduction
2.2. Qualitative study of deterministic chaos in a dynamic simple system
2.3. Test for the presence of deterministic chaos in a simple dynamic system
2.4. Properties of chaos in complex systems
2.5. Effects of fractal chaos in “Complexity” theory
2.6. Self-organization: relations and the role of chaos
2.7. Applications: introduction of new concepts in systems
2.8. Conclusions
3 The New “Complex” Operational Context
3.1. The five phases of economy – how everything accelerates at the same time
3.2. The expected impact on just about everything
4 Taking Up Complexity
4.1. Taking into account complex models
4.2. Economy and management of risks
PART 3
5 Tackling Complexity with a Methodology
5.1. Any methodology must first enrich the systemic interrelationships
5.2. Towards a transdisciplinary co-economy
6 Management and Control of Complex Systems
6.1. Introduction
6.2. Complex systems: the alternatives
6.3. Control principles of production systems
6.4. PABADIS: an example of decentralized control
6.5. Generalization of the concepts and mechanisms
6.6. A basic mechanism of control – the auction
6.7. The control of self-organized systems
7 Platforms for Takingup Complexity
7.1. The VFDCS: a platform for implementation
7.2. The application of VFDCS: the auction market
7.3. The application of VFDCS: the virtual supply chain
7.4. General method for the control of systems
7.5. Conclusions and prospects
PART 4: Introduction to Part 4: Overviewing Trends to Complex Futures
I.1. Some comments on the notions of complexity
I.2. Composition of Part 4 in this book
8 Applying Intrinsic Complexity: The Uberization of the Economy
8.1. Preamble
8.2. The context: new opportunities and new consumption needs
8.3. The domains that are studied in this chapter
8.4. Concepts, definitions and remainders
8.5. The business model and key elements
8.6. The problem of property and resource allocation
8.7. The uberization approach in context
8.8. Generalization: the complexity of allocation problems
8.9. Conclusion
9 Computer-assisted Production Management
9.1. Introduction and reminders
9.2. Intercommunication networks
9.3. Communication network topologies
9.4. A few important properties
9.5. Analysis of new concepts and methods in manufacturing sciences: instabilities, responsiveness and flexibility
9.6. New concepts for managing complex systems
9.7. The change of conduct
9.8. Improvements in manufacturing: process balancing
9.9. Conclusion: main action principles in complex environments
10 Complexity and Cognitive Robotics
10.1. Introduction
10.2. The new industrial revolution
10.3. The factory of the future: trend or revolution?
10.4. Inputs for the factory of the future and their impact on the industry’s professions
10.5. Conditions for success
10.6. The data sciences
10.7. A few technologies in data sciences
10.8. Mechanisms of conventional cognitive engineering
10.9. The new mechanisms of engineering
10.10. The study of links and relationships in large databases
10.11. Application of cognitive robotics: the Watson platform
10.12. The impossibilities and unpredictabilities of complexity
10.13. Current strategies of digitalization
10.14. Conclusion: a maximum risk economy
Bibliography
Index
End User License Agreement
List of Tables
2 Evidencing Field Complexity
Table 2.1. The results of the descriptive analysis of faults
Table 2.2. Typology of behavioral complexity
6 Management and Control of Complex Systems
Table 6.1. Comparative table of the four types of auctions
7 Platforms for Takingup Complexity
Table 7.1. Evolution of concepts in our research team, via the interactions between different components of a production system
9 Computer-assisted Production Management
Table 9.1. Flynn’s taxonomy for classifying various types of parallelism
Table 9.2. Complexity management, a comparison of the analytical and systemic approaches (Joel de Rosnay [DE 75])
List of Illustrations
1 The Foundations of Complexity
Figure 1.1. Graphical summary of the concepts discussed
Figure 1.2. The loop of imbalances which feeds the complex system of evolution
2 Evidencing Field Complexity
Figure 2.1. The effect of feedback loops in a production system
Figure 2.2. Mode of operation for MAQ
Figure 2.3. Evolution curve of the invento ry
Figure 2.4. Second evolution curve of the inventory
Figure 2.5. Systems or behavioral classes encountered in complexity
4 Taking Up Complexity
Figure 4.1. An extract from the book sequel “Beyond the Limits to Growth” (1992) from the Club of Rome (cited by http://www.pelicanweb.org/solisustv10n12page5.html). For a color version of this figure, please see www.iste.co.uk/massotte/smartdecisions.zip
6 Management and Control of Complex Systems
Figure 6.1. Ambivalence in the approach of complex systems. On the left, the conventional top-down approach (as found in MRP) aiming to reconfigure the schedules. On the right, the dynamic self-organized approach (which is bottom-up), appealing to the reconfiguration of resources
Figure 6.2. Comparing a conventional structure with the PABADIS structure
Figure 6.3. Architectural approaches for production management systems
Figure 6.4. Nonlinear adaptive networks
7 Platforms for Takingup Complexity
Figure 7.1. Zone of convergence in an auction market
Figure 7.2. Structure of the multi-agent universe in a virtual supply chain
Figure 7.3. Process for the consistency analysis of a distributed production system
Figure 7.4. Trends in the development of the concentration of a configuration
Figure 7.5. Interactions between the different entities of a production system
8 Applying Intrinsic Complexity: The Uberization of the Economy
Figure 8.1. Logical architecture of an uberized system
Figure 8.2. The stakeholders of a conventional non-uberized transportation system [YOO 15]
Figure 8.3. Structure of the multi-agent universe in a virtual supply chain
Figure 8.4. Cournot–Nash equilibrium
Figure 8.5. Graphs showing the Cournot–Nash duopoly equilibrium [VAL 09], https://www.cairn.info/revue-d-economie-politique-2009-5-page-727.htm
Figure 8.6. Comparison of several tactics in production management (REF)
Figure 8.7. The evolution of useful strategic knowledge according to Joël Mokyr [MOK 16]
Figure 8.8. Interactions between stakeholders in a strategy (C. Rochet, C. Freeman, http://i1.wp.com/claude-rochet.fr/wp-content/uploads/2015/08/Diapositive1.jpg)
9 Computer-assisted Production Management
Figure 9.1. Examples of static topologies
Figure 9.2. Network architecture of distributed systems [DRI 16]. Note that: processors, storages, communication nodes or 2×2 commuting switches are used in either Crossbar or Omega type architectures
Figure 9.3. Shortest paths between two nodes in a hypercube http://abyss.uoregon.edu/~js/images/hypercube5.gif
Figure 9.4. The hypercubes, or n-cubes, form a family of Eulerian and Hamiltonian graphs
Figure 9.5. Eulerian Graph – https://commons.wikimedia.org/wiki/File:H3hamilton.png?uselang=fr
Figure 9.6. Hamiltonian cycle in a graph with edges in the form of a dodecahedron
Figure 9.7. A MRP management system (source: IBM)
Figure 9.8. Management of conventional production
Figure 9.9. Enhanced Production Management System
10 Complexity and Cognitive Robotics
Figure 10.1. Image of clustering on the factorial design of the MCA (Multiple Correspondence Analysis) (source: Wikipedia ACM). For a color version of the figure, see www.iste.co.uk/massotte/smartdecisions.zip
Figure 10.2. Model of a decision tree (source: edrawsoft.com)
Figure 10.3. Multilayer perceptron (MLP) without feedback, and with feedback (Source: iacenter.free.fr)
Figure 10.4. The IBM supercomputer Watson (Source: IBM)
Figure 10.5. Product as an interface between market and company
Figure 10.6. Organization of “Centric Big Data” ([MAS 17])
Landmarks
Cover
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Smart Decisions in Complex Systems
Pierre Massotte
Patrick Corsi
First published 2017 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2017The rights of Pierre Massotte and Patrick Corsi to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2017938651
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78630-110-9
Preface
Why do we need to look at complexity?
When complexity is a part of everyone’s daily experience, what is more fitting than a book that aims to process this “complexity”? When catastrophes of every kind appear on the media screens in our homes, it may be useful to question the true meaning of the word “catastrophe”. According to some, the term means chaos and disorganization; it can also mean, for example, the return to rest of a vibrating musical string as per the laws of mechanical resonance. Likewise, it may be equally useful to contemplate on the true meaning of the word “chaos”: in the beginning chaos is not differentiated from divine thought and is, in a way, the matrix of a future yet to become, as well as an opening on new ways of thinking.
In order to better understand the scope of this issue, it is worth recalling the historical approach as employed by Science since the 17th Century, when Descartes published the “Discourse of Method”, which serves as the foundation of modern rationalism and its ongoing development. From a scientific point of view, this “classical” way of thinking is based on the fact that the world is a rational, mathematical, knowable and decomposable quantity. On the literary level, an examination of “classical” dramaturgy reveals the rule for three key devices (time, place and action). Notable playwrights whose oeuvres follow this stagecraft doctrine are Boileau, Corneille, Racine, etc. Essentially, these principles advocate that everything can be systematized, decomposed and organized, and is recognized as the basis for the great progresses in knowledge and the management of systems. They continue to permeate the overall scientific approach, which is characteristically fragmented, isolated and centralized.
However, this concept and essence of Truth reaches its own limitations, as it inevitably leads to a hierarchical organization that limits our abilities and potentials for research and engineering, at times juxtaposed, and is responsible for creating inherently fixed spaces with reduced freedoms, which deny multidisciplinary cooperation, consultation and synergy. As such there are many opportunities for development and problem resolution.
Hence, the cultural heritage of the past has not predisposed us to the current socio-economic transformations created by globalization, as it is applied to other contexts and approaches, and which is already changing towards a single-system approach, often referred to as the holistic approach.
What does this book claim?
The contribution of Complexity Science is, in a sense, an attempt to rebalance the classical analytical approach and its particular limitations. This can be seen to be made up of individual perceptions that together become the complementary polar, thereby allowing for a global understanding of the world, our systems and our societal behaviors.
While this complement has become as important as the Cartesian approach (i.e. the analytical approach) to our context, it has also become increasingly urgent. Everything around us pushes us to review our patterns, to enlarge and to stretch them to the point of breaking our educational, behavioral and structural limits. It has become essential to understand the Global and to propose a new path based on connectionism and self-organization. The Global is before us, within us, within our reach and on our scale; it is the awareness of factors that we have so far hidden from ourselves. “How long does a fly live?” A life. And Pieng-Tsou, the oldest man in the world? Also a life”. (Shipper). It is our deafness to the relationships between entities and the factors that connect phenomena, which has unintentionally limited science and engineering so far.
This book proposes to open a door. Without negating all the progress made so far, it is the authors’ belief that the time has come to give prominence to a conscious and reasoned apprehension of the Global and the role it plays in our socio-economic lives. Our aim is to introduce a new paradigm, which we have experimented on with concrete case studies, and to establish a preliminary set of scientific and technological bases. That said, this book does not aim to be a theoretical or scientific contribution and is rather intended for all those wishing to broaden their practice of management and engineering systems. As such it has been written for engineers and technicians, strategists and planners, managers, researchers, teachers, and students. It is but a first step, carrying with it the hope that it might inspire hitherto unknown advancements, as well as engage other authors to grapple with this valuable appreciation of complexity and its many applications on the ground.
By writing this book…
The history behind the genesis of a work is sometimes most unexpected. Since the 1970s, both of the authors have been situated within the industry, and have been involved with the creation of new technologies, privileged to experience each phase of the great wave that is artificial intelligence. One of the authors has spearheaded multiple projects investigating the limitations of knowledge, research that was conducted at IBM France, for Networks and Telecommunications at the La Gaude Research and Development Center, for large-scale computer systems at the Pompignane plant near Montpellier, and finally, with IBM Europe in charge of research and development projects to improve the competitiveness of the group’s R&D plants and centers. As such, over the latter part of the last century, he has coordinated large teams comprised of more than 60 people. The other author was formerly employed at IBM’s San Jose Research Laboratory in California, then at the La Gaude Research and Studies Center, after which he became involved with an artificial intelligence start-up, and ran the R&D management for a subsidiary of THOMSON-CSF, where he oversaw the portfolio for advanced artificial intelligence projects in line with the European Commission in Brussels. As such he is reputed as an expert member on numerous European commissions concerned with complex approaches, and is a specialist in designing breakthrough innovations pertinent to the complex processes involved.
Neither author came across the Complexity Sciences by chance. After having met over the application of neural software networks in the late 1980s, it was not until the 1990s when the “era of networks” became irrevocably established, and with it the associated frustration brought about by increasingly complicated computing systems, that the real connection happened. Their paths then separated, with one choosing to terminate a long career as Head of Research at IBM in the field of Complexity and the Transfer of Technologies in Industry at the Ecole des Mines d’Alès (EMA) in Nîmes; and the other embracing a career with the European Commission, and later working as an international contractor and consultant. At this pivotal point, they were struck by the acceleration of transformations within the industry, and became convinced that it was mostly due to the quality of interrelations between previously isolated elements. This progression, they felt, would need to mature in the years to come. As privileged observers, users and internal actors of the various instruments implemented in the planning and conduct of European research and development, and framework programs for more than 20 years, the authors noted a growing incongruity, the novelty without appeal of conventional systems strategies, the limitations of top-down planning and monitoring. A new dynamic in the markets appeared by way of a transdisciplinary sidelong perspective. Success stories emerged less from structured bureaucracies and more from the mass market, a growing arena that connects all the actors in a hectic agora with a seemingly irrepressible capacity for innovation. In short, systems were no longer the solution. They had become the problem.
In terms of networking the citizens of the world, any organization not able to operate at the level of intensity and adaptation of its actors is rendered null and void. But how to explain this general sense to our customers? How can they free themselves from the obstacles to their own innovation, from the structural obstacles to their competitiveness, often generated by themselves in an earlier era? Little by little we had the same idea, to write this book, to bring forward the elements of a strategy for change and to make these accessible to all organizations and companies.
With the good fortune of a rich research heritage of the first magnitude, spanning more than 30 years, we have developed a method for developing and launching “global applications”. The intention was not to develop an academic work, but rather to focus on setting up methodological bases, validating and refining this new discipline, as consultants and entrepreneurs, to play our part in the global management practices of companies, organizations and consultancies, to help managers identify and model, internalize and innovate, in a word, to experience the crucial decisions tied to their “global apps” of tomorrow. Without a comprehensive approach, there will only be failures and ruination on the path to business success. The maxim “Think globally to act locally” led to our own motto: “Formulate globally to decide locally”.
Having interviewed managers, companies and administrations, we examined their organizational structures and their successes and failures: whether in production, distribution and indeed throughout the supply chain; in financial institutions, administrative and social institutions, as well as research organizations. We also examined the dynamic links of these managers and how they interact with their associates and partners, suppliers and customers, their structures, their business model: in short, their economy. The job of tomorrow is there because of these relationships. It will be the wealth of the old nations too; bearing in mind we always observe through the appropriate lens. It is important to change our thinking, even if it involves a shift in our cultural, organizational and economic paradigms.
Pierre MASSOTTEPatrick CORSIApril 2017
Acknowledgments
When writing these first books, we received many suggestions from friends and colleagues. The formation of ideas was inspired from firsthand experience in the field. These ideas were enriched by crossfertilization and discussions held with researchers from the IBM Corporation and the Research Institutes in France, notably at the Ecole des Mines d’Alès (Henri Pugnere, I.G des Mines, and Gérard Unternaehrer, I.G Arùmmement) and abroad (such as the Santa Fe Institute). Many thanks to our former colleagues at the European Commission, who, in addition to their already heavy burden, made themselves available and who were very generous with their attention. We equally benefited from the commitment of friends and colleagues to clarify lesser known points of their strategic planning, with several of them allowing us access to information in order to establish concrete cases.
This book, which is at the same time sourced from these two preceding publications, as well as constituting a significant increase in order to reflect the socio-technological developments of our time, could not exist without the help and support of co-workers and management. As such we would like to take this opportunity to express our thanks to: Jean Taverne, General Manager of Technical Services of IBM France who carried out experiments on IBM France sites and helped to set up the former European Competencies Center in Artificial Intelligence; René Balmès (IBM Global Services) who was a great visionary in the management of complex systems; Scott Kirkpatrick, Benoît Mandelbrot and John Sowa of the IBM Research Division in Yorktown Heights, NY, who always responded to requests for information. Finally, from the academic point of view, thanks must go to the teachers Pierre Ladet (Grenoble) and Alain Haurat (Annecy), as well as Prof. Hermann Kuhnle (F.I. Magdeburg) and Prof. Abdelhakim Artiba (MonsUniversity) – who consistently encouraged the underlying works and inspired our confidence.
Thank you to ISTE for their support, patience and dedicated enthusiasm for the manuscript.
Finally, thank you to Anne Marie Massotte for helping within the completion of this book.
List of Acronyms
ACCA:
Agent-Container-Communication-Auto/Self-Organization
AFI:
Agri-Food Industry
AI:
Artificial Intelligence
ANN:
Artificial Neural Networks
ATG:
Advanced Technology Group
B2B:
Business-to-Business
B2C:
Business-to-Consumer
BA:
Broker Agent
BDIN:
Belief, Desire, Intent, Need
BoM:
Bill of Materials
BPR:
Business Process Reengineering
BTO:
Build To Order
BTP:
Build To Program
CA:
Cellular Automata
CAD:
Computer-Aided Design
CAM:
Computer-Aided Manufacturing
CAP:
Computer-Aided Production
CAPM:
Computer-Assisted Production Management
CBR:
Case-Based Reasoning
CC:
Collaborative Consumption
CEA:
Commissariat à l’Energie Atomique
CFM:
Continuous Flow Manufacturing
CIM:
Computer-Integrated Manufacturing
CLT:
Central Limit Theorem
CML:
Complex Mutual Logistics
CMU:
Cooperative Manufacturing Unit
CNP:
Contract Net Protocol
COBOT:
Cooperative Robot
CSR:
Corporate Social Responsibility
DAIS:
Decision-Aid Interactive Systems
DAPS:
Dynamic Analyzer of a Production System
DE:
Differential Equations
DFT:
Demand Flow Technology
DLF:
Direct Line Feed
DP:
Dynamic Pricing
DSS:
Decision Support System
EMA:
Ecole des Mines d’Alès (France)
ERP:
Enterprise Resource Planning
FBL:
Feed-Back Loops
FBM:
Field Bills of Materials
FFT:
Fast Fourier Transform
GNOSIS:
Knowledge Systematization – Configuration Systems for Design and Manufacturing
IBM:
International Business Machine Corporation
IDAS:
Interactive Decision-Aid System
IDE:
Integral Differential Equations
IDSS:
Interactive Decision Support System
IIE:
Institute of Industrial Engineers
IMS:
Intelligent Manufacturing Systems
IOT:
Internet Of Things
IS:
Information System
JIT:
Just In Time
KADS:
Knowledge Acquisition and Data Structure (a project)
KBS:
Knowledge-Based Systems
LBD:
Ligand-Binding Domain
LCM:
Life-Cycle Management
MAQ:
Maximum Allowable Quantity
MAS:
Multi-Agents Systems
MCA:
Multiple Correspondence Analysis
MES:
Manufacturing Execution System
MFG:
Mean Field Games
MFG Order:
Manufacturing Order
MIMD:
Multiple Instruction on Multiple Data
MLP:
Multi-Layer Perceptron
MMI:
Man–Machine Interface
MPP:
Master Production Plan
MPS:
Master Production Scheduling
MRP:
Material Requirement Planning – Also: Material Resources Planning
NAN:
Nonlinear Adaptive Networks
NANN:
Nonlinear Adaptive Neural Network
NCP:
Neighborhood Coherence Principle
NICT:
New Information and Communication Technologies
NLAS:
Nonlinear Adaptive Networks
NLDS:
Nonlinear Dynamic Systems
NMPP:
New Manufacturing Production Paradigm
NP:
Negotiation Protocol
NPDI:
New Product Development and Introduction
ODE:
Ordinary Differential Equations
OR:
Operations Research
P2P:
Peer-to-Peer (or Point-to-Point)
PDE:
Partial Differential Equations
PLCs:
Programmable Logic Controllers
PLM:
Product Lifecycle Management
PLOOT:
Plant LayOut Optimization
PnP:
Plug-and-Participate
PPB:
Parts Per Billion
PPC:
Pull Production Control
PPM:
Parts Per Million
PR:
Production Reservation
QUETA:
European ESPRIT 4 project #22367 “Quality Engineering Tools for Assembly and Small Batches Manufacturingˮ
RFID:
Radio Frequency Identification Devices
RMLP:
Recurrent Multi-Layer Perceptron
ROI:
Return On Investment
SCADA:
Supervisory Control And Data Acquisition
SCM:
Supply Chain Management
SDS:
Simple Dynamic System
SIC:
Sensitivity to Initial Conditions
SIMD:
Single Instruction on Multiple Data
SISD:
Single Instruction on Single Data
SME:
Small and Medium Enterprise
SMED:
Single Minute Exchange of Die
SMI:
Small and Medium Industry
SPC:
Statistical Process Control
SPSM:
Self-Production System Monitoring
SPT:
Shortest Processing Time
SSPR:
Single-Step Production Reservation
TAT:
Turn Around Time
TCM:
Thermal Controlled Module
V&V:
Verification and Validation
VAC:
Value-Added Chain
VFDCS:
Virtual Factory, Distributed and Control System
VOD:
Video On Demand
WIP:
Work-In-Progress
IntroductionA World Swept by Complexity
I.1. Our changing world: benchmarks, transformations and futures
What is happening in the world and in our environment? Our reference points intermix, our hierarchies collapse and our own certainties disappear, even as we learn to prepare for any eventuality: natural disasters, economic and social uncertainties, overloaded regulations at the societal level, even scenarios of success and the resolution of failures at the individual level. The more our knowledge becomes confined, the more extensive it becomes, accumulating in acceleration, and fading into a kind of ambient ignorance, to be later swept away by more information and knowledge.
But should we desire it, are we not supposed to have access to all knowledge pertaining to any subject! We have lost the excuse to ignore (something) due to the lack of connections linking us to (quantities of) knowledge. And little by little, constrained by a new impermanent immanence, we take measure of a world in perpetual agitation.
Yet, our vast industrial knowledge archives and practical know-how – which include virtually all historical cases and past events, and which are available to somebody somewhere – still do not deliver the simple “Open Sesame” retrieval solution and good sense that our expectations hope for and which our minds would like to anticipate. We continue to research with Search Engines, engines which unfortunately lack the true ability to find (search). Unsatisfied, our searches, measured thorough techniques still mysterious to our sciences have remained largely unchanged since the industrial revolution, and attain results that yield more but not better information.
When and wherever our ancestral civilizations have opted to administer order, and the maintenance thereof, there follows a quandary of causes and effects. Where once the implicit and immutable models of the past reassured us, our recent systems of explicit reason and procedure serve only to increase our rampant anxiety? Worse still, where change is a function of context, at the quasi-geological scale, for example, stability can be understood to be an unnatural ephemeral phenomenon, a suspicious and unreliable state of risk. At this point, we find that the insurrection of change has taken over once again, reoriented us and made us dependent.
The eminent Heraclitus wrote “There is nothing permanent except change”. This indicates that putting our immutable assertions into practice continues to provide daily challenges, thus legitimizing our existence. What if the apparent complexity of today’s world was only the consequence of our reluctance to see facts through simplified glasses, our stubbornness to resist the collapse of our linear patterns or our pretention to disguise a priori models?
The real challenge may be the taking into account of this “apparent complexity” and its incalculable effects, as it is multiplied in our daily economic and individual lives; certainly, if such an event were to take place, it would represent a quantum evolution of our society. How should we handle such a vast challenge? Would it take into account the extension of our physical and economic reality in terms of “cubic” modeling, whose easy representations decompose into simple elements? Obviously, with the promise of access to such vast horizons, the challenges will be commensurate to potentialities. Research and development is dominated by three trends, said former Commissioner Erkki Liikanen shortly before leaving his position at the European Commission in 2004: “a growing complexity, a growing interdependence between products and services, and an increasing level of competition”. Let us thus admit that in terms of engineering we are deliberately and implicitly postulating new advances that by definition must be commensurate with the fantastic potentialities, which we can scarcely glimpse through the veil.
This is the essential mission of this book: to speak on the environmental complexity of our industrial and economic systems; to reveal ways to approach the fire of complexity, and even, to a very new extent, how to master it. It is the inherent hope that this work contributes a modest step to this new “conquest of fire” by economic man, to augment the possibilities of action for all human actors, those of today and of tomorrow.
I.2. New relationships of uncertainty
Every day we observe a pell-mell of modern paradoxes:
– The probability of failure of a high-speed train component is somewhere in the order of 10
−12
, thereby reducing the likelihood of a disaster occurring. However, in the event of a failure, it can become far more spectacular precisely because of this, and with its creation comes the creation of consequences of an unexpected magnitude, far greater than the original frame: for example, your train is delayed, and in turn you miss your flight, or else involved in an accident, etc. How can we fully appreciate such concatenations?
– Thanks to various technological advancements, the size of particles released into the air from the combustion of diesel engines has become much smaller (with a diameter less than one micron). As a result, their diffusion is necessarily far wider, and being finer, penetrates more porous systems as the finer particles are no longer weighted down by larger ones. The question now, is how to go about blocking their diffusion as it becomes increasingly irreversible, and beyond this, how to measure this direct impact on our environment?
– Our classical disciplines and hyper-specialized models (e.g. molecular biochemistry, diffusion of physical matter in gaseous states, direct viral marketing models, etc.) have forged quasi-independent terminological systems, which classically cannot be assimilated or reduced from one into another. It is only now, with the advent of nanotechnology that there is a call for a fusion of these systems. Although they rely, for example, on the science of materials, they offer a new point of origin: not necessarily one that is more generalized but rather more
global
, which when combined together impacts the majority of traditional disciplines. Will this lead to a generic terminology that is global in its impetus for the development of nanotechnologies?
Since 1927, the famous German atomic physicist, Werner Heisenberg (1901−1976), inventor of Quantum Theory, has accustomed us to considering the product of two antagonistic but intimately connected factors, as a possible constant of our physical universe. We may feel that the “strengthening” of one of the factors leads to the “weakening” of the other. His Danish contemporary and confrere, Niels Bohr, further educated the world on the duality of approach that is encountered when we are observing an object (e.g. we cannot observe a particle and a wave, since they are two “antagonistic” manifestations of the same physical phenomenon). What stops us from applying this same notion for the duality of behavior to the analysis of industrial or economic problems?
I.3. It is still and always will be Descartes who instructs us
Looking further back, René Descartes (1596−1650) bestowed on the enlightenment as a rational way to solve problems, a process from which still today we cannot seem to distance ourselves [DES 37]. Indeed, if we look at modern progress through old glasses: we have clearly embraced a didactic taste for the art of decomposing a problem “into its simple elements” and our approaches are often identified by this reductionist method. What has humanity really achieved thus far? Essentially, we have solved linear problems on the basis of an analytical approach.
Let us thus begin with a lesson in humility. What if we have only solved the (very) simple problems so far, and are completely lacking in complexity? What then? Would most of the problems have remained outside our scope of investigation? Let us take the following examples: the effects of globalization and virtual enterprise networks; public aid policies and their impact on entrepreneurship; the reconfiguration of product manufacturing models and their related processes; the highly scalable structure of organizations, etc. Are these not simply problems which having been ingeniously decomposed into so-called simple elements?
There is therefore no need to define these new problems, since it is evident that they must be approached differently. What are the issues? Why are those in charge de facto at the foot of the wall, purported by their peers as unable to solve the real problems. Our answer: the limits of the traditional organization, those of the company, the limits of scale; the barriers to complexity; and the walls of calculability. The challenges of tomorrow require a different approach.
I.4. Is the problem-solving approach sufficient?
“If you ask the Taoists how they see the world, the first thing they’ll tell you is that the world is changing”, said Brian Arthur [ART 00], who added, “Science doesn’t like perpetual novelty”. We see problems on the one side and solutions on the other, when in fact everything is in the making, a flow of new beginnings in the projected meaning and that, in fact, we are investing in a myriad of contexts. In a world consisting of only organic processes, complexity is pretty much everywhere, we are fundamentally immersed within it. The perceptible world appears at first to be simple, and yet its underlying essence makes us comprehend, rather subjectively, an original complexity. Two kinds of vision (our gaze), two attitudes, which induce and express a transition from an observation (the “observer”, invasive and projected), towards a perceptive (participative and nourished) relationship.
It is in fact very complicated to simplify! Our human way of recognizing the preponderance of reasoning forces us to divide in order to analyze, to understand and, ultimately, to complicate relentlessly. We reject confusion to claim comprehension. However, such “exprehension” (that which is not an understanding) forces us to exclude the organic whole. John Seely Brown [BRO 00] said that “in the classical economy the challenge was to manufacture products, while in the current economy it is to make sense”.
Are the new Information and Communication Technologies (ICTs) or NBICs (nano-bio-info-cogno) invariably producing complicated evolutions? This is possible because, under their control, the effects of uncertainty are paradoxically increased: the overabundant amount of data hides, but points out the lack of data. When the data is difficult or dangerous to obtain or measure, strongly changing, or simply missing, the resulting information is unreliable; it may be false or incomplete. Similarly, an accumulation of knowledge can lead to distortions, such as false reasoning and false appraisals. The relations between elements of a system become nonlinear and possess intense dynamics. The focus on change therefore leads to an inability to reach stable regimes. The reactivity of relevant contexts takes precedence over the system itself and, undoubtedly, deprives it of its sovereignty.
Hence, presented here in vague terms, is the framework of our study. Throughout this book, we discuss complex systems, behaviors rather than states, emerging behaviors rather than stable models, reactivity to the environment, adaptability to the context and mechanisms of self-regulation. That is to say the survival strategies - ontologically ecological processes - and strategies whose intention is the immunization of interference… Thereby introducing, superficially, our new vocabulary.
To travel this path, we will have to free ourselves from the chains of linearity, or else be penalized as we enter this new game of pursuit that has decidedly open rules. In addition, because of the effect of ambient mimicry, the complexity of an organization seems to grow according to the complexity of its environment. This explains why we have so many difficulties in controlling increasingly sophisticated management systems.
Take, as an example, the role of competition in the strategic development of enterprises. Seeking to achieve a competitive advantage, the attention of all companies is predominantly focused on competition; a company, seeking to do the same thing through this urgent mimicry, takes similar steps (e.g. de facto underdeveloped standards). The fiercer the competitive environment, the more problem-solving is developed, and with it the mimetic activity increases, as if to compensate for the lack of differentiated models available. New “patch” solutions appear that incorporate environmental complications, solutions which interact with what already exists.
Therefore, the economic future of our organizations lies in this ability to expand this density and relational intensity. Would it not be basically the same if the basis were innovation instead of competitiveness? In the mathematical sense of modern economics, there is only one equivalence class that has a bearing on economic value: the binary relation “to be in relation with…”. In turn, this relationship generates the fundamental origin, the source of becoming. This explains why predominant competitive tactics often abandon breakthrough innovations, in the process losing out on any particular originality that accompanies them. We could argue that this happens all too often. Nonetheless, here we must acknowledge the realities of situations where the customer is firmly at the center of the economic model. It is indeed through a kind of intimacy with the customers themselves that we develop the relational intensity vital to maintaining a lasting competitive advantage.
I.5. The new paradigm of complexity
I.5.1.From information theory to global networks
We are living in the Information Age. Nowadays, the coding of digital information into outmoded networks, analog telephone calls, for example, requires that the signals be converted. The notion of frequency is central and refers to both the base frequency and the bandwidth around the fundamental value. When Claude Shannon (1916−2001) put forward the two approaches for sending information through a channel – either as a narrowband solution or as a bandwidth solution – work on communication systems first focused on the former, and it took a long time for the industry to consider the “band” approach (the latter), which as it turned out was the more effective solution. It is important to note, however, that it required technology then not yet available, in order to exploit the exponential complexity inherent to bandwidth.
Today, the example of the Internet gives us a day-to-day illustration of such an availability of sufficient bandwidth: the all-round boost of Peer-to-Peer (P2P or “equal to equal”) introduces a new density in our connections and makes the Internet resemble a macro-organism, endowed with a mental life and its own behavior. Inside this organism, time is not eliminated, but rather plays out at different speeds, at personalized tempos. Bandwidth capacity, as offered by the various nations, is today a quantifier of their economic power. As such, bandwidth can be seen as an integral tool that allows for the complexity of application systems. As we approach the boundaries of the client–server relationship and the object-oriented approach of the 1980s and the 1990s, we turn our attention now to networked system architectures (grids), where each point, each node of the network is a member and expressive in its own right. George Gilder [GIL 96] called this bandwidth “communicative power” and even imagined a reality he called the “fibersphere”, where bandwidth is completely free, with no (relay blocking) communication bottlenecks: so that, the local and the global merged into one.
Here, complexity becomes a new paradigm occupying the Global: settlement of the global method becomes the primal interest, because without this, there can neither be more nor less. In fact, it is research for a new simplicity that is the true new paradigm – an approach that is both organic and synergistic. It liberates creativity and local individuation while thinking globally, and it supports diversity (e.g. biodiversity) in an all-encompassing unification. Like Bohr, we search for the dual but complementary representations, such that, even if they remain mutually exclusive, they can become a whole in the sense that they have the same goal: to solve complex problems.
I.5.2.Systemic thinking: what is a system?
The Belgian painter and leader of the surrealist movement, René Magritte (1898−1967), inspired researchers in artificial intelligence and helped them “think abstractly, globally and systemically”. It was a critical period in art because this systemic philosophy is the art of perceiving fluidity, a process that naturally arrives at the Global. Underlying this is the concept of a transitional nature that is no longer stationary. In other words, the underlying concept focuses more on the relations between states, as opposed to equations of the states themselves. Let us not forget: to see Globality is to think systemically because intellectual reasoning is fundamentally limited by the analysis of a “coherent local vision”.
The mechanistic view is diametrically opposed to the systemic view. The mechanists appreciate the supremacy of the formal, the structural and the linear with a computable rigor, from quadratic to polynomial. This was followed up by the concept of “non-polynomials”, which still formulate very little about the ontology of this ethereal dimension wherein animate beings live every day. The emergence of this definition of incalculable paradigms was thus able to transcend, without contradicting, the path that John Von Neumann (1903−1957) traced, nor those developed in cybernetics. Similar is a so-called Knowledge Base System (KBS) that sets out pre-established production rules and makes deductions, but which does not allow itself to invent new ones (this would be an inductive phenomenon). Or again, a decision support tool (Intelligent Decision Support System or IDSS), which incorporates functionality related to a need – somewhat related to tasks of the problem-solving variety – but as it is perceived and interpreted by its users, who influence it by using it, thereby personalizing it and bonding it with our volatile and ever-evolving human context. The products we use daily reflect our way of living and evolving: from this, we see that there is complexity only in evolution. Without the latter, there is at most “complication”, and this only serves to obscure the intrinsic phenomenon; it cannot last because we cannot co-evolve with it.
Systemic thinking makes networks work. And networks accelerate time to the extent of their bandwidth. Joël de Rosnay [DE 94/04] demonstrated the relationship with real time: “the notion of real time, forged by computer scientists, signifying a succession of parallel, linear or sequential actions, determines a change in the conditions of an environment or structure and brings about a response before a fixed deadline. If we get the answer after the deadline, we lose interactivity, there is no more real time”. Dense times and slow times are “linked to the genesis of new information” (ibid.) and signify the emergence (or not) of new modes of social interaction.
I.6. Which trains of thought guided us?
In order to better accompany the fundamental transformations invoked in this book, it is worth recalling some recent developments concerning the approach to studying complex systems. These have guided us throughout our work and have characterized the way in which we have written this book, a product that is the fruit of our experience. They are essentially based on two underlying and omnipresent concepts: universality and transdisciplinarity.
I.6.1.Universality: a transverse vision of the world
The perception of the world in which we live seems to evolve into greater sophistication and complexity. On the scientific level, the passage from the Simple to the Complex is based on an elementary rule, which states that all phenomena of nature belong to the same fundamental laws, from the infinitely small to the infinitely great. It is this “theory of the whole” that also highlights a number of characteristics about the world around us: elephants, forests and mice constitute one way of being. This is the principle of universality.
In this context, for example, the identification of finch songs – with frequential or neuromimetic approaches – has enabled new methods of quality control to be developed for ball bearings: a study found that the acoustic signature is in fact similar between “singing” birds, bats, bells and ball bearings [DUR 04]. The domains involved are unlimited: they affect economics, weak interactions at the atomic level with notions of left-right asymmetry, biology with DNA, particle physics, molecular chirality, human behavior, and so on.
For our own purposes, we will investigate three key aspects of universality.
I.6.1.1. Everything appears as an assembled set of components and yet, everything is connected
At present, it is customary to consider that quarks and pentaquarks represent the basic components of matter. These building blocks allow for the composition of a cell, an organ, and a living being, which is itself the result of several billion years of biological evolution. Such structures are fractal in nature (according to Benoît Mandelbrot) and do not cease to appear over time, to evolve around us and to form ever more complex ensembles such as social structures, living beings, evolution of biological organisms and industrial organizations; all structures use the same basic mechanisms. This process has led to the existence of complex (biological) adaptive systems that can be integrated into immense non-adaptive systems (galaxies), as they still possess the same micro-properties.
Whatever the levels of evolution and the disciplines concerned, the invariable natural, biological or other constants always appear: temperature and limits of physical life, the size and celerity of living beings, the capacity for learning linked to social relations, etc., with orders of magnitude in keeping with “power laws”. This property keeps certain equilibriums and orders of reference. Moreover, the way of observing, reasoning and measuring performance remains comparable, regardless of the domain and scale of observation, which should be avoided so as not to complicate the R&D approach.
I.6.1.2. Nature is varied and diverse
The diversity of life on earth is the result of an evolution taking place over some 5 billion years. Human cultural diversity dates back a few tens of thousands of years and continues to evolve. For scientists, this diversity is the result of self-organized phenomena that produce local orders and generate new structures while the general disorder of the Universe is growing.
This diversity must be preserved: it is a source of wealth because it generates elements (essentially: solutions), some of which will be better able to adapt to changing contexts. This is how the continuity and evolution of the world is ensured. Diversity can become very complex, and if we analyze the relationships that bind humanity to itself or to the biosphere, we must be able to integrate aspects as diverse as environment, demography, economy, society, politics or ideology.
Diversity reminds us that our source of inspiration must remain varied and pluridisciplinary. The aim here is to translate the values and scientific advancements of unrelated, and even very different, fields and to benefit from these cross-subject experiments in order to advance a science that is often at the cutting edge of creativity or at the brink of a technological breakthrough.
I.6.1.3. Stability does not exist
Every element of our Universe is subjected to seemingly random fluctuations. At the level of matter, this quality allows for the emergence of clusters, the primordial creation of galaxies in the universe. These “emerging” forms become progressively varied and complex and no matter how evolutive they possess an aggregate of volatile, individual characteristics. As in nature, these equilibria are precarious – nothing stable exists – from the phenomena of condensation to the collapse of stars that are only observed later on, within a larger timeframe. This line of thought originates a regression that leads to new patterns and other forms of complexity, with many applications, for example, on how the stock market and its financial markets work.
We are thus immersed in a universe that is both simple and complex, and yet we cannot simultaneously embrace all aspects of the same problem – the difficulty involved in taking into account the presence of these many interactions, those within the same level of Complexity, as well as those between different levels. Under these conditions, the concise description of a complex system becomes physically impossible.
An engineer, confronted with this problem, is led in practice to develop solutions that are increasingly complex, unstable and often inapplicable, or rejected by users. This limits the possibilities of traditional approaches to research: the latter bringing only scraps of solutions to our problems: what will enable industrial systems to recover better than others? How to integrate innovation into a manufacturing plant? What is the effect of this phenomenon on the relations between social groups?
As nature proceeds, it is therefore necessary to place ourselves at the frontier of several sciences. We will study how to combine the skills of the engineering sciences, life sciences, humanities and social sciences, as well as others.
I.6.2.Transdisciplinarity: a new way of thinking
Transdisciplinarity and Globality are intimately linked and are essential modes of thought for the engineering of the Complex. In this context, the sociologist Nietzsche conducted long studies on the behavior of people and cultures. He distinguished individuals from logical and analytical approaches through a cold evaluation of the facts (rational thought) and from these inclinations towards an intuition, synthesis and passion (oriental approach). There is, however, another category of people, such as engineers or researchers, who are sometimes ignored: those who combine the two inclinations in areas crossing the boundaries of several disciplines. As is often the case, these people are not necessarily socially recognized and tend to remain isolated in their traditional institutions. On this point, the widespread ideology, for example, in the academic and bureaucratic circles, has to be broken, and needs to move away from the rhetoric that the only ideas worth taking seriously are those belonging to the most concentrated research of a given specialty.
We must not forget the vital contribution by those who venture to suggest an “abstract overview of the whole”. It is in this direction that the field of innovation is the most promising; innovation often belongs to those who know how to make such transpositions. For example, to accommodate originality at the Santa Fe Institute in New Mexico, a network was created to communicate between transdisciplinary research topics. For example, the study of DNA hybridization is currently being covered by this approach. A direct relationship between biology, humanities and social science has been made, in order that it may ask: what is the impact of new genetic technologies on society? Are alterations in a person’s genes likely to affect his or her descendants? This examination of transgenesis is a topical issue that is particularly important to the understanding of genetic cellular transformations.
Closer to home, we can bring our skills and know-how in computer science to the service of bioinformatics, and likewise, take inspiration from advances in biology and in the design and development of infinitely small, new approaches to problem-solving (info-biology), or even, the creation of new devices for application in the field of molecular or quantum computing.
Similarly, computers can be used to simulate, study and improve ecological processes or the adaptability of community agents. Furthermore, we can consider computers as social beings and utilize them as complex adaptive systems, in coadaptation, able to describe and predict their reciprocal behaviors (we can read further on virtual animal societies recently popularized by robotic based games, that is to say robots that succeed in inducing affectionate relationships). We thus place ourselves at the frontier of the order and disorder of regimes indicative of self-organized criticality. Thus, by reversing roles and concepts, new paradigms emerge, which alone can bring new and innovative products and solutions.
On the other hand, the complexity generated by our new environment (this complexity that has as its corollaries, the notions of interactivity, reactivity, openness and competition) makes any major industrial, economic or social problem become increasingly difficult to model with the rational and conventional approaches. It implies a process that will use the transpositions and adaptations of solutions known, tried and developed elsewhere in the world.
Such collaborations transcend the boundaries of disciplines and involve the networking of actors in scientific, economic, social and cultural development. Specificities, needs, skills, productions and advice are distributed and accessible at the lowest cost. The network necessarily becomes a virtual superorganism, without borders, like a swarm of industrious bees each possessing their own function, and able to communicate through signal converters (modems) to examine, for example, a multitude of Internet sites, in order to collect and synthesize applicable information or solutions. Borrowing an analogy from the culinary arts, the ingredient products and services are procured, and this information soup mixed in a pluridisciplinary and multicultural melting pot, from which can be extracted new and remarkable dishes with properties capable of satisfying the demands and tastes of society. Today, the lack of proactivity, the lack of exploitation of advantages within our reach, is damaging: we fall behind and we isolate ourselves, resulting in all subsequent consequences we can imagine.
I.7. Let’s develop the focal point of this book
This book is therefore an essay on complexity. It is polarized according to the challenge of problem-solving within dense information networks – of which all problems can be assimilated with the decision-making phase. However, here problem-solving encounters difficulties in the modeling thereof. Little has been said thus far of the heuristic processes, even though the heuristic approach is able to express a quintessential aspect of our question, in terms of the difficulty in apprehending the Real, and thus the complication. To elaborate, a heuristic process plays a similar role to an algorithm but, unlike the latter, it focuses on the work process and does not need to prove itself! The art of the engineer is to establish an operative link between a given problem and finding quick economical solutions, which necessarily involve the design (conception) of a statement able to satisfy the intellect, and his own models of reasoning (rationally).
The art of the “complexity engineer” will soon be to trace paths for managerial thinking, beyond the strictly repeatable and predictable, and as such, he will be located at the heart of all networks: information systems, organizations of all kinds, decision makers and agents, humans and non-humans, machines, robots and circulating agents on communication networks. His science will be to be an expert in the process of innovations, beyond purely incremental, sequential and linear innovation. Yesterday, the engineer gave a balanced, generalizable solution for everything – an order in relative ignorance. Tomorrow, he will have to create innovative habitats to suit everyone – a “dynamic order” amidst the surrounding chaos.
If successful in giving a new meaning to each new process, that is to say a personalized quality to each and every process, and its constituent members, within these networks, he will carry with him the promise of a quantum wave of socio-economic progress. He will allow for “management in confusion” within the intrinsic richness of systems, not as they used to be in yesteryear, where any confusion was excluded whenever an optimized abstract solution faced a well-defined problem. Like the martial arts, a company ignores what its competitors will do, it ignores new technologies and how customers react. However, it knows it will react to any change appropriately. The strategic center is not the so-called problem (nor the solution) but the relation to the situation. Companies function within this attitude of continuous observation, strategic expectation and resolute action.
This book provides the first principles for this new genre of building and constitutes a first and modest contribution dedicated to the advent of this new type of engineer. The construction of a strong and sustainable knowledge economy requires, as John Seely Brown says, the original development of a “robust ecology of knowledge”. It is our implicit hope that this book prepares and provokes vocations and multiple advances.
I.8. The structure of this work
The work we present includes four principle developments – referred to as Parts – that offer a path of progression regarding the understanding and practice of the phenomena that is complexity:
–
Part 1
offers a preliminary familiarization with the phenomena of complexity;
–
Part 2
aims for a greater depth of understanding for complex phenomena on the ground;
–
Part 3
focuses on the control issues surrounding complex systems and gives examples of suitable tools and platforms;
–
Part 4
covers the latest expansions as compared to previously published works. It develops three practical cases that mark the present socio-economic disruptions that lie behind changes in organizational and environmental complexity.
In the first part, Chapter 1 brings together notions, basic principles and properties related to complex systems, and provides basic definitions and concepts associated with complexity, simplexicity, etc. We also introduce principles underlying “Complex Systems Theory”, which we also call the “Science of Complexity”. This data is a summary of our research results as well as the results obtained by research teams from around the world. Utilizing what is currently in existence, it has been deemed necessary to adjust the method in the spirit of being understood by the industrial, economic and financial worlds, even if it means the wrath of other experts in the field.
The second part begins with Chapter 2
