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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
This book is a general presentation of complex systems, examined from the point of view of management. There is no standard formula to govern such systems, nor to effectively understand and respond to them. The interdisciplinary theory of self-organization is teeming with examples of living systems that can reorganize at a higher level of complexity when confronted with an external challenge of a certain magnitude. Modern businesses, considered as complex systems, ideally know how to flexibly and resiliently adapt to their environment, and also how to prepare for change via self-organization. Understanding sources of potential crisis is essential for leaders, though not all crises are necessarily bad news, as creative firms know how to respond to challenges through innovation: new products and markets, organizational learning for collective intelligence, and more.
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Veröffentlichungsjahr: 2018
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Table of Contents
Cover
Preface
1 Introduction: Why Do We Talk About Complexity in Management?
1.1. Examples of complex and/or innovative projects
1.2. Complex systems, rationality and knowledge
1.3. Cognition and the theory of the firm
1.4. The entrepreneurial dimension
1.5. Conclusions
2 The Evolution of Complex Systems
2.1. Adaptation, learning and flexibility
2.2. The nonlinear behavior of “imbalanced” systems
2.3. Autonomy and responsibility
2.4. Different evolutionary models
2.5. Implications for management
2.6. Closing remarks
3 Steering Complex Adaptive Systems: Managing Weak Signals
3.1. Navigating the ocean of signals
3.2. Managing interdependences and dancing with the system
3.3. Surfing on the wave
3.4. Conclusion
4 Entrepreneurship, Market Creation and Imagination
4.1. Some current stakes of entrepreneurship
4.2. The entrepreneur in the history of economic thought
4.3. Motivations, responsibility and identity of the entrepreneur
4.4. Entrepreneurship and complexity: the role of the imagination
5 Managerial Approaches and Theories of the Firm
5.1. Complexity and management: the first steps
5.2. Manager’s role versus complex systems
5.3. Marketing and complex systems
5.4. Complex systems and human resource management
5.5. Conclusion: managers’ creative responses
Conclusion
References
Index
End User License Agreement
List of Table
Chapter 4
Table 4.1. Characteristics of the three types of entrepreneurs
Chapter 5
Table 5.1. The first research on management in connection with complexity (sourc...
Table 5.2. Connects and complementarity between RBV and complex systems (source:...
List of Illustrations
Chapter 3
Figure 3.1. The discontinuous growth of a chaordic system (source: van Eijnatten...
Chapter 5
Figure 5.1. The four situations from the Cynefin model (source: Burger-Helmchen ...
Figure 5.2. Four types of complex systems in marketing (source: Wollin and Perry...
Figure 5.3. Theoretical conceptualization of the firm and complexity (source: Co...
Figure 5.4. Creative organizational reactions (source: Fisher and Amabile 2011, ...
Guide
Cover
Table of Contents
Begin Reading
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Smart Innovation Set
coordinated by Dimitri Uzunidis
Volume 19
Creative Management of Complex Systems
Jean-Alain Héraud
Fiona Kerr
Thierry Burger-Helmchen
First published 2019 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:
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www.iste.co.uk
John Wiley & Sons, Inc.
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© ISTE Ltd 2019
The rights of Jean-Alain Héraud, Fiona Kerr and Thierry Burger-Helmchen 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: 2018962282
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-84821-957-1
Preface
“Everything is becoming more complicated; we must go ever faster!”
This rather common statement will certainly remind readers of similar ones they have heard in the media or during a conversation. There is undoubtedly a shred of truth in these popular expressions, but to rationally analyze their meaning, we must first distinguish complication from complexity. In fact, the state of being complicated is different from that of complexity – the first is a linear progression even when it does not look straightforward, whereas complexity is an emergent state – novel outcomes emerge over time that were not foreseeable beforehand.
This concept is most interesting to consider when managing organizations, as it requires distinctive planning, managing and operating techniques. Complexity is born of interactions between a multitude of actors that are possibly aware but often unaware of the fact that they belong to the same system, with the formation of feedback loops that render the system’s evolution largely unpredictable. Complex systems have very specific properties, particularly the nonlinear response to stimuli that must be taken into account by the managers who are in charge of regulating or steering them. Whereas an engineer can manage a complicated system (often by way of technology), it is an exaggeration to the claim that the administration of a complex organization is “managing” the system.
Our planet is a complex system, as is our body, the organizations that we create, or our social and economic systems. Complex systems can often be analyzed as a system of systems. For example, a company is a system within the sector-specific system, i.e. of its partners, suppliers and clients, the institutional framework, etc. It is no simple task to define the boundaries of the system being observed (or steered) as complex systems are embedded within other complex systems. However, in order not to become overwhelmed we must deal with any question or specific problem by defining and determining which part of the system to investigate and at what level of scale. There are also a number of methodological choices that must be made at the outset in order to better understand and act.
As the complexity of systems increases with the number of connected elements, the contemporary world generates a veritable explosion in complexity taking into account the digital revolution and the Internet. The globalization of technology, economy and lifestyles brings not only attempts to simplify and standardize (in order to reduce complexity), but also an enormous development of complex interpersonal relationships around the planet, which renders the overall system terribly unpredictable.
Throughout this work, we will define complex systems with greater precision. We will evaluate their adaptive qualities, reactivity to changes in the environment and their resilience. We will also investigate the relationship between complexity and creativity: a complex system functions in a largely self-organized way and this can lead to the creation of novelty, emergent outcomes and unexpected properties, which is another form of creativity. As one can imagine, managing a complex system requires qualities such as open-mindedness, attentiveness and imagination. Those who manage and lead complex systems are acquainted with ambiguity and understand that systems (and people) can be steered but not controlled. This creative management must be capable of interpreting weak signals that have a heavy bearing on the future; they must be able to adopt behavior that is “entrepreneurial” rather than “administrative”. The variety of situations managers may be faced with obligates them to be creative, to use fewer fixed management rules and more incentivizing mechanisms to make the system adaptive and to encourage rather than block the system’s intelligence.
As such, we prefer the following expression to the one given at the start of this chapter:
“Everything is becoming more complex; we must be ever more creative!”
This work consists of five chapters. The first chapter, written by Jean-Alain Héraud and Thierry Burger-Helmchen, presents an overview of complex systems and some motivations that managers may (must) follow while managing these particular issues. This will lead us to managerial and economic considerations, for example, by revisiting classical subjects from economic theory, such as individual rationality or evolutionary processes. In management, we will mention new concepts such as “exaptation”, which generalizes adaptation.
The second chapter, written by Jean-Alain Héraud and Fiona Kerr, focuses on one of the primary properties of complex systems: their constant evolution. Complex systems do not present a stationary, immutable system. They are dynamic or, more precisely, evolving. With the help of examples taken from the course of enterprises or more general examples, the authors will gradually outline the competencies necessary for a manager in this kind of environment: being able to think in a complex manner.
The third chapter, written by Fiona Kerr and Jean-Alain Héraud, is dedicated to weak signals. After having defined these discrete facts that bear on the future, they will highlight the need to establish safety nets, identification and filtering devices, and the ability to interpret weak signals within organizations. Complex systems have phases, points of attraction that, through self-organization or a deliberate strategy, may be identified and used. The system the manager must steer may be labeled as “chaordic” – an intermediate situation between order and chaos – as there are powerful leverage points in such a system. The adaptation of the system through innovation is also one of the keys to management in the longer term, hence the importance of building on the skills of actors of particular importance by translating these from new ideas outside the system: the literature mentions “door keepers”, “boundary riders” or “knowledge angels”. The analysis of intercultural situations will help illustrate this problem.
The fourth chapter, written by Jean-Alain Héraud, analyzes the entrepreneur’s role in complex systems. Sometimes the primary actor, sometimes completely absent from theoretical representations in economics (according to the school of thought), this figure is, in fact, central to the interpretation of the history of real-world systems. It will become clear that a certain rereading of the history of economic thought is very elucidating when it comes to tackling today’s important issues such as the entrepreneur-innovator’s role within the company and in the entire economic system, processing uncertainty in decision-making, adapting to the market, or creating a market. The human sciences also contribute useful complementary perspectives such as the role of social identities and the imagination’s place in management.
Finally, the fifth chapter, written by Thierry Burger-Helmchen, adopts a resolutely managerial approach. He starts by presenting the overarching functions of management science that may benefit from new observations from the perspective of complex systems; next, the author focuses on two functions: strategic marketing and human resource management. In these different cases, the manager has a choice between several types of action, the basis of which may be more or less improvisational and more or less adapted to the situation.
Jean-Alain HÉRAUD, Fiona KERR and Thierry BURGER-HELMCHEN
October 2018
1Introduction: Why Do We Talk About Complexity in Management?
The subject of this work is the management of organizations in contexts that are characterized by strong systemic complexity. We wish to show that this type of management can nevertheless be creative in the sense that it necessarily evades linear thought. This way of thinking can be adapted for complicated problems, but not for complex ones. In the former, the application of causal reasoning and optimization methods enables us to arrive at the correct response for a properly asked question (even though this requires a great deal of calculations). In the latter case, it is an illusory wish to establish a precise and exhaustive model of reality and risks as we would be dealing with an emergent process, and we must be content with initiating the processes and performing experiments on both means and ends. The essence of life is in complexity, as shown by philosopher Edgar Morin – particularly in dialogue with economist and systemic specialist Jean-Louis Le Moigne (Le Moigne and Morin 1999). If an organization is to be considered living – i.e. evolving, dialectic, partially unpredictable and thus difficult to manage according to strategic planning formulas – then it requires exploring alternative management styles and thinking outside the box, hence the introduction of the concept of creativity. The subject of management is living, thus creative, which obligates management to perform in a different way.
Complexity and creativity are part of the research subjects that draw most of the attention towards economics and management fields. These two fields of research share numerous conceptual and methodological aspects. In both economics and management, complexity and creativity are also transdisciplinary vectors that require researchers and practitioners to revisit certain basic hypotheses and concepts.
Be it in economics and creativity management or in the application of the science of complexity, the number of academic publications, books, even special editions of entire journals in these fields, summer schools, or research centers has seen considerable growth in the last two decades.
Today, not only practitioners but also the political sphere and organizations (governmental and NGOs) often use the terms economics of creativity or complexity management. Recent developments in these fields of research as well as the synergies in their evolution within economics and management were the main motivating factors for writing this book, which presents recent issues in economics and management.
To tackle the issue of complex system management, we will draw our attention towards recent manifestations in the field of economics and creativity management. However, the present work does not warrant its contribution to the subject of creativity. The focus here is placed on the notion of the complex system. The aim is, in general, to cover a wide range of fields – as diverse as private or public organization set-up, formal or informal organizations, spanning from enterprises to urban systems. Our perspective towards this system will be similar to that of the organization’s manager, attempting to provide decision-makers with theoretical representations and useful, concrete examples.
1.1. Examples of complex and/or innovative projects
Launching a start-up and managing an innovative project in an existing enterprise are tricky jobs that elude typical strategic planning models. The description of the complex system in question is obviously not the same: managing an innovative project implies a detailed understanding of the company’s system (the stakeholders in a very broad sense, namely the internal actors and regular partners) as well as its environment, whereas the creation of a start-up implies knowing how to anticipate what may be the future multi-actor system where it will establish its competence.
Another example is that of a megaproject such as designing and building a new nuclear center model or redeveloping an urban zone in a state of decline. In the former situation, there is a strong technological innovation dimension even though this is not the only uncertainty that must be managed and the only field of creativity to be involved. In the latter case, it is not a matter of technological innovation – or only marginally – but rather of an operation requiring a great deal of creativity in the most diverse domains, often an innovative way of thinking about how to articulate the collective project, and then its governance.
In the above-mentioned examples, the common feature concerning creativity is that it is not simply a matter of implementing a new idea with a certain functionality in mind (by rationally constructing the optimal response to the question asked), but rather steering a complex system towards a goal which is not completely defined at the onset. To do this, management organizes a multitude of competences and the organization uncovers a large part of the pertinent data along the way.
The literature on management science provides solutions on such issues in several ways. The most promising solution is the entrepreneurship theory developed by Saras Sarasvathy, who popularized the effectuation approach as opposed to ordinary causal reasoning in project management (Sarasvathy 2001). Matters pertaining to general (interdisciplinary) theories describing dynamic systems and self-organized processes are also taken into account. Jean-Louis Le Moigne, complex systems theoretician, is also one of the thinkers concerned with self-organization in management (Le Moigne 1994). In fact, following the works of I. Prigogine in chemistry, H. Atlan in biology, F. Varela in cognitive science, etc., Le Moigne has applied this concept to management. Stating a system is complex implies it is self-organizing. With this attribute, it redefines itself over time and this creative faculty renders it unpredictable. This is the profound reason that connects complexity, uncertainty and creativity, and this is why the manager of such a system has difficulties steering with tools articulating causes and consequences in a linear way. We must break away from scientistic thought, at least as much in management as in other fields.
The management of innovative projects and that of complex projects are altogether different subjects, but they correspond to similar processes. One of the goals of this work is to comment on this similarity by highlighting two reciprocal logical chains:
– all innovations, in order to be steered, imply the mastery of a complex system (it does not suffice to have a new idea to innovate);
– the management of complex projects is an innovative act by definition, as complexity never leads to repetitive situations (complexity compels us to be inventive).
In every situation, success depends on the ability to articulate multiple forms of creativity. The creativity of the scientist (science) or the engineer (technology problem solving) does not suffice to ensure the success of the resulting innovation: entrepreneurial know-how is also a must. Inversely, managing a project in a conventional sector though regulated by a complex system of actors and artifacts compels us to proceed by trial and error and to create as an inventor.
Muller et al. (2017) pose the question of knowing the manner innovation systems are considered complex systems. Evolutionary economics, of which innovation is the central subject, has, curiously enough, done little until now in regard to the characterization of innovation systems as complex systems – being classically described as networks of actors. A network is better understood as a complex system triggered when feedback loops are produced in connection to the relationship between actors and learning processes. Recognizing the attributes of complexity poses implications concerning governance. For these macroeconomic systems, this means coming up with innovation policies. Correspondingly, we will explore the consequences of complexity on enterprise governance in this work. As for public policy, the design of European programs in the last decade based on the idea of smart specialization in regional strategy is an interesting example, leading to recommendations analogous to those that we will see at the microeconomic level: such as experimentation, the attention given to decentralized initiatives and the detection of weak signals (Héraud 2016).
1.2. Complex systems, rationality and knowledge
Although it is difficult to provide a precise and universal definition of a complex system, we will attempt to target the notion in this initial chapter. The common feature in all complex systems is their level of interconnectedness – the encompassing of a large number of elements, generally organized into multiple interlinked hierarchical levels like Russian nesting dolls – and the direct interactions between these levels. These systems can be adaptive and improved over time. However, it is difficult to steer them, for their structural richness creates self-organization phenomena, with many of the interconnections not visible. Because these phenomena are inevitable, it is better to take advantage of them rather than opposing them. The linear thought of project management ab nihilo is not applicable in such a context given that reality follows this pattern and thus cannot be manipulated using these methods.
1.2.1.Outlines of complexity and complex systems
Complexity comes from complexus, a Latin expression meaning “interwoven”. Complex thought studies the aspect which connects the subject to its context, in addition to the system, process or organization.
Le Moigne and Morin (1999) reiterate the three principles of rejecting complexity through classical science:
1)
the principle of universal determinism
, which says that intelligence is capable of knowing and predicting everything;
2)
the principle of reduction
, which involves becoming familiar with a composite whole through knowledge of its constituent elements;
3)
the principle of disjunction
, which assumes that a proposition can only lead to one single consequence.
For Bréchet (2012, pp. 257–274), complexity is born of recognizing the irreversibility of phenomena. An initial complexity approach (McKelvey 2012) is based on the triptych order/disorder/organization. Complex systems are dynamic systems characterized by a very large number of interactions and feedback. This interactivity renders phenomena that is difficult to describe, analyze and even more difficult to predict.
Edgar Morin distinguishes restricted complex systems from generalized complex systems. The latter, just like the former, are complex in their organization and behavior, but they produce complexity through their function. Generalized complex systems respond to three principles:
1) The principle of universal determinism against the principle of a dialogical relationship between order, disorder and organization.
2) The principle of reduction against the principle that connects the parts and the whole in a reciprocal relationship.
3) The principle of disjunction against the principle of maintaining the relationship between objects, notions, disciplines and knowledge.
Bréchet (2012) revisits the characteristics of complexity in Edgar Morin’s sense with a reading key: the theory of organizations. Thus:
– complex systems are unstable, unpredictable systems. This attribute exists because they integrate circular causalities and interwoven processes, which makes them difficult to manage and control;
– a complex system does not respond to expression “the whole is greater than the sum of its parts”. Without being less, the “whole” is qualitatively different from the sum of its parts with its own strengths and weaknesses as compared to that of parts;
– a complex system is the circular (or parallel) manifestation of order, disorder and organization;
– complex systems regenerate and reorganize on their own. Their structure evolves as a function of the environment in a broad sense. Edgard Morin speaks of self-eco-organization, referring to the ecology of populations.
In the work “La complexité: vertiges et promesses” (Benkirane 2013), Michel Serres stigmatizes the word complexity. In his opinion, the word complexity includes too many situations and lacks precision. In order to bring about a proper analysis of so-called “complex” situations, it would be wise to replace this characterization with “there are a large number of objects and figures”. He then states that each science must correctly classify subjects and figures in order to rigorously select ones that must be analyzed, understood and solved.
For economic actors in general and enterprises in particular, complexity is tantamount to an inability to adequately predict and thus allocate resources. Complexity is commonly confused with uncertainty, risk, doubt, novelty, interactions, etc. Although it is the manager’s duty, at the end of the day, to handle every situation arising from different expressions, it is best to ascertain them as per their classification.
To respond to the challenges of a complex system, the manager must foresee action and knowledge strategies, i.e. he must anticipate learning and the acquisition of new information as and when the action is carried out. The manager in a complex system is necessarily an ambidextrous manager (as defined by Tushman and O’Reilly, see Barlatier and Dupouët (2016), for more information). He or she prepares scenarios and modifies them along with the appearance of unexpected elements (such as the reactions of competitors or exogenous economic shocks).
1.2.2.Information and learning
The uncertainty tied to the future and the complexity associated with it have been a source of surprise and perplexity for individuals throughout the ages, but their reactions differ (Gollier 2004) and are often accompanied by the creation of new tools (e.g. the “options” for managing financial risks) or organizational mutations.
One of the problems in managing complexity is the difficulty in comprehending the connection between the analysis of the environment, the planning of enterprise strategies and managerial behaviors, and the infinite potential outcomes of managerial actions. In a standard SWOT analysis, the manager is no longer in a position, during state of complexity, to make the connection between resources and the perpetual opportunities that could be seized appropriately if they are put to proper use. The threats and weaknesses are typically perceived in a disproportionate way.
One of the characteristics of complexity is the absence of order and regularity. The resulting situation, with its seemingly random appearance, leads many managers to equip themselves with information systems – even decision-making systems – that enable them to reduce complexity (at least in appearance, and too often by oversimplification). Big data analysis techniques are nothing but the expression of this need addressed with the present technology. The need for information and recourse for adapting better performing tools to guide the decision-maker increases diversity between companies. A VSB, if it does not stem from the technology sector, will, by nature, be endowed with fewer resources and a more limited ability to collect and analyze information than a large enterprise. However, endless collection of information is not always a solution, as Vincent Desportes remarks (2004):
“In fact, the more information one has at his disposal and the longer the timespans needed to process them, the greater the risk is of improperly distinguishing the pertinent from the useless, the significant from the futile, or simply the true from the false. Certainty is much more a matter of understanding than data, yet the multiplication of data requires processing capacities that are adapted to the analysis needs in the useful timeframes. The current problem is less the lack of information than its overabundance; the difficulty lies in processing and synthesizing on the part of decision-makers who are often nearly drowned in the overabundance of information. There is a dialectic of time and information”.
Faced with the need to steer the enterprise in this delicate situation, new tools have seen the light of day, oscillating between scenario methods and real options. These tools seek to integrate new information in order to exploit it robustly, while maintaining a certain plasticity, i.e. not reacting systematically when managerial information is presented.
As information becomes available, the possibilities to choose from increase. Although they are superimposed they are substituted by options that have already been chosen. The task of selecting and hierarchizing that must then take place typically stems from the manager. This is one of the characteristics of the double-loop learning model to allow these actions (Argyris and Schön 1995).
Research on the complexity of practice in enterprises is difficult to measure, and its impact even more so. Complexity develops in numerous fields (life sciences, computer science, mathematics, etc.); it is less present in management because a manager’s discourse, such as an advertising slogan, must be reduced to a few words, and the idea must be simple! Yet complexity is a glutton for words and for the time it takes to explain itself.
In a dynamic and complex framework, learning is an overwhelming element, as pointed out by Mintzberg (2008, p. 240):
“We know that the dynamics of the context have repeatedly challenged every effort made to force the process to mold itself into the framework of a calendar of activities or a predetermined trajectory. Strategies inevitably have some emerging qualities, and even when they are largely deliberate, they appear less formally planned than informally visionary. Learning in the form of adjustments, beginnings, and discoveries arises from chance and the recognition of unexpected forms and it inevitably plays a key role, if not the key role, in the development of all innovative strategies”.
As underlined by Naud (2007), Mintzberg’s reflections on complexity and strategy imply the notion of unpredictability. Mintzberg also states: “As the innovative organization must continuously respond to a complex and unpredictable environment, it cannot be based on a deliberate strategy”. Thus, in order to face complexity, an organization’s strategic behaviors are, first and foremost, a space for initiative. It is in this space in particular that entrepreneurs and intrapreneurs are distinguished. The spirit of initiative and entrepreneurship remains a major aptitude for facing the stakes of complexity (see Chapter 4).
There is also a particular pathology of such systems: coherence issues (limited rationality as defined by Herbert Simon and James March) and steering difficulties that cause large organizations or large projects to often appear irrational in their behavior. For example, megaprojects never respect the iron triangle budget-timeframe-specifications (Lehtonen et al. 2016). Everyone knows in advance, but they all act as if this were not the case. There is an inexplicable incoherence for those who have not understood what the logic specific to a complex project is: large, ambitious, multiple actors and spread out in time.
1.2.3.Rationality
For economic actors, complex environments impact behaviors and rationality, notably:
– in relation to time;
– in relation to space (issue of proximity);
– in terms of decision-making rules;
– in terms of organization (particularly in the pair structure-strategy).
1.2.3.1. Complexity and the relationship to time
Time can be interpreted in several ways in economics and management. From just in time to real-time data processing, acceleration is the watchword. However, time is a source of regulation, and it is regulated – from the creation of a shared worldwide measurement of time (Besanko et al. 2011, p. 112) to the notion of Internet time. Mastering time reduces overt complexity. This need to master time can be found in the prospective processes of economists, processes that are likely to contribute indications on the future. However, prospectivists’ work does not fundamentally modify economic actors’ relationship to time.
The evolutionary approach emphasizes “path dependency” (Nelson et al. 2018), i.e. the recognition of the past, the weight of history in economic activities and enterprise strategies (Barnett and Burgelman, 1996). These path dependencies enables the enterprise to incorporate knowledge from the past and to progress along the learning curve (or the experience curve), but they do not necessarily facilitate decision-making processes or their renewal if the environment is undergoing profound change.
1.2.3.2. Notion of space
Enterprises have multiple frontiers with variable porosity (Pénin and Burger-Helmchen 2012). These frontiers are concerned with the activities, influence, culture and mastery of enterprising costs. Crowd funding techniques and connections with user communities are some of the many approaches that enable the populace to benefit from supplementary resources thanks to partnerships, cooperative action, user and provider integration, etc. The enterprise’s frontiers, which have become more transient, authorize the rapprochement with other actors to a greater degree than before. Based on this fact, distance becomes less of a constraint. Internalization and externalization operations follow one another so that organizations may outsource the activities that are less efficient in terms of added value and concentrate on their core business. Enterprises thus possess a greater capacity to develop new competencies in order to face the next threats and opportunities. The notions of frontier and distance, reconsidered in the framework of complexity, lead enterprises to redefine their fields of activity and their core business.
The notion of distance has been particularly developed in economics and innovation management, whether it is a matter of questioning the distribution of multinational enterprises’ research centers, their connections with local communities, the distribution of issues, etc. These structures answer to the need to mold a system’s complexity to respond better to the enterprise’s objectives.
1.2.3.3. Decisions and controls
The decision-maker’s rationality should not be the same in a complex system as in a basic system. Thus, Cohendet (1997, p. 81) sees this as a self-realizing approach of decision-makers:
“The decision-maker is generally not even aware that he is contributing to the creation of irreversible conditions through his decisions, but if each decision-maker tends to adopt the same technology, irreversible conditions will be created on a global scale by default. There is thus a risk of irreversibility. This phenomenon was originally studied by A. Kahn under the name of the ‘tyranny of small decisions.’ He also showed how a large number of small decisions, when their temporal perspective is limited, can lead to unanticipated, irreversible transformations”.
Decision-making and its implementation in a complex system move towards tension between the decision-maker’s conscious desires and the effects of actions that are no longer foreseeable.
An optimizing rationality leads to a set of actions that are part of a process presumed to be as easily controlled as possible. Two major issues arise in a complex system in regard to this logic: the illusion of control; and, the fact that whenever one part of the system is optimized, it is at the expense of other parts of the system, which is then pushed out of balance (Kerr 2014). Also, as Naud (2007) remarks, this “tyranny of small decisions” combined with a decoupling of the intentionality of actions (causes) and economic effects profoundly changes managers’ positions. For many, this gap explains the relative incapacity of some managers to make the right decisions. This decoupling of intention, action and effects is an organizational manifestation of the complexity that acts on the time and space of the decision.
Among enterprises’ strategic reactions to face this situation, we find the multiplication of decision-making centers – notably in the form of centers of profit, costs, means, etc. The objective of these centers is to bring the decision-maker, the on-site manager, together with the base unit of complexity. Cost centers also recognize the limitation of the negative financial consequences of great exposure to risk. In a complex system where mastering the elements is an illusion, this organizational approach allows the negative impacts of complexity to be limited.
