Enfoques teóricos de manejo de complejidad en las organizaciones: un análisis comparativo.
| Autor | Boh |
| Cargo | Ensayo |
Theoretical approaches to managing complexity in organizations A comparative analysis
Abordagens teóricas da gestão da complexidade nas organizações: uma análise comparativa
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Introduction
Studying social organizations as complex systems had became more relevant over the last few decades, mostly as a result of strong critiques to the traditional mechanistic paradigm in which organizational theory was originally based, and of related questions about the lack of effectiveness of hierarchical control associated with it (e.g. Turnbull, 2002). In the globalized and open markets context where most organizations operate nowadays, the pressures for competitiveness, flexibility and dexterity has increased, and this demands for more adaptive structures. In this context, contemporary complexity theories that inspire managers with ideas about self-organization and neural network-like organizations are in demand, both in academic journals and in consultancy (e.g. Mitleton-Kelly, 2011). This is also the case with social science researchers, which are increasingly attracted to ideas of permanent innovation, co-evolution, decentralized decision making, among others (e.g. Allen, Maguire, & McKelvey, 2011; Axelrod & Cohen, 2000). Even if the outputs from complexity researchers have been significant in the last few decades, it has deep historical roots. Early works from Adam Smith ("The invisible hand"), Von Neumann (self-replicating systems), and Darwin (evolution theory), among others, provide clear traces of inspiration to the earliest theories on self-organization and complex systems.
Self-organizing systems are understood as systems that operate autonomously, and co-evolve between themselves through transitions between disorder and order: they have been studied from different schools of thought which include the sciences of complexity, complex adaptive systems (CAS) and cybernetics. (1) From an ontological point of view these three streams of thought understand self-organization as the spontaneous emergence of collective behaviors from the interaction between autonomous agents (Di Marzo Serugendo, Gleizes, & Karageorgos, 2011). Additionally they all agree that organizations are nonlinear systems that evolve over time. Various authors have considered self-organization as the central aspect of complex systems (Martinoli, 2001; Nitschke, 2005). Speaking of complex systems generally involves talking about self-organization.
However, there are theoretical and methodological differences in these three approaches to self-organization that have not been often spotted in the management literature (e.g. Battram, 2001; Etkin, 2009). On the contrary, most of the references to self-organization in the management sciences literature seem to assume that the sources are homogenous and sort of coincidental. We aim to clarify the differences and complementarities between the most advanced studies on self-organization in management sciences, in order to explain also the consequences of following one or the other theory and the possibilities each one opens for analyses and understanding of a business or social organization.
The differences in interpretation from the various theories is to be expected, given the plurality of phenomena both in the biosphere and in econosphere that seem to be governed by self-organizing principles, which have been studied by these currents of thought. Complexity sciences and complex adaptive systems (CAS) have studied natural and artificial complex systems (i.e. ants colonies, internet, informatics viruses, etc.). Organizational cybernetics has studied self-organization in businesses and social organizations. We suggest here that the differences between the ways of dealing with complexity from each theoretical proposal come from their different ways of understanding and their differing emphasis when studying complexity.
While there is an increasing interest in research in organizations using complexity theories, CAS and organizational cybernetics, there is not enough explanation on the differences between each of these theoretical approaches and the consequences that taking one or the other has for a particular research project or even academic consultancy. We consider that the lack of understanding of the similarities and differences between the theories has been the origin of misrepresentations, misunderstandings and unsupported criticisms. The similarities between these approaches have been noted when they get classified jointly as a single category of approaches to social systems (e.g. Jackson, 2000). The lack of recognition of differences between complexity management approaches may have the root of: (a) misinterpretations (e.g. to think that organizational cybernetics is founded in the hierarchical and mechanistic control paradigm); (b) critiques (e.g. to assume that complexity sciences study the same issues than organizational cybernetics, so in comparison have no much to offer to management sciences); and (c) confusion (e.g. to assume that given that organizational cybernetics, CAS and complexity sciences all have similar roots, therefore there are not major differences among them).
The specialized literature dealing with complexity in management, have sometimes took inspiration in concepts originating in other disciplines like chemistry, physics, biology, mathematics and computing. As many of these concepts are difficult to 'translate' to the field of management, metaphors have often been suggested (e.g. McMillan, 2008). However useful metaphors may be as learning devices, they not always offer the level of precision and the lack of ambiguity required to be useful enough to interpret complex situations in businesses. Sometimes the use of metaphors--if they are not clearly related to the situation or if they are not clearly understood -, may leave the user confused rather than inspired. We consider that this route (the use of metaphorical language inspired from other scientific disciplines explaining complexity principles) has not always been useful enough in fully understanding the relevance of complexity and self-organization to business. A key reason why metaphors borrowed from one domain (e.g. physical or biological) to the social domain may not be that useful is that any human social systems exhibit higher levels of complexity than other complex systems (e.g. physical systems, biological systems); therefore, the metaphorical comparisons would always be, by the end, somehow limited.
Also lack of knowledge on the underlying differences between the different complexity theories may mislead the practitioner to an inadequate use of the models and tools suggested by each theory. For example, if the purpose of a study is to know better on structural complexity, organizational cybernetics offer the best tools for modeling and diagnosis; for analyzing collective behaviors' emergence, complex adaptive systems offer more comprehensive methods. Aiming to use CAS to guide an organizational design may not be the best choice, as it would not be using viable system model (MSV) to explore dynamic social behaviors over time.
So our purpose here is to clarify the points of difference and similarity between these three approaches. In this paper we review the core arguments suggested by the pioneers of these three main complexity theories in management. From complexity theory we review the contributions from Lorenz (1963), Thom (1977), Nicolis and Prigogine (2007), Prigogine and Stengers (2002), Bonabeau, Dorigo, and Theraulaz (1999), Watts (2006); from complex adaptive systems we take insights from Gell-Mann (1994, 1995) and Holland (1992, 1995, 1998); and we follow Ashby (1962, 1964), Von Foerster (1981) and Beer (1981, 1988) from the cybernetics tradition.
After a careful review of different sources in the literature, the current authors summarized their observations on the key characteristics from each of the main approaches to dealing with complex systems and agreed on the key dimensions and features in which these theories coincide and diverge. The differences and similarities between complexity sciences, CAS and organizational cybernetics allow us to suggest six propositions about self-organization in the context of business and social organizations.
It is important to clarify that there are other approaches to complexity like Edgar Morin's one, focused on complexity thinking and summarizing the best French tradition on subjective philosophy. His work includes broad proposals on how to modify the subject relationship with the world or the world's attitude toward nature (Maldonado and Gómez, 2011). We did not include his work in the analysis as neither organizational cybernetics, CAS nor complexity sciences focus on subjective philosophy: different to Morin's complexity thinking, they all aim to explain how and why a phenomenon is complex and how an individual or team can better deal with such complexity. (2)
In order to clarify the context for discussion, we have defined, in the first part of the document, a three dimensional space, with three main conceptual axis that summarize the core differences and similarities between these complexity approaches. This space allows us to position the different theories and to facilitate an understanding of their varying ways of understanding self-organization.
The definition of the three dimensions comes from a summary of identified patterns in current debates at the complexity literature. Mapping such three dimensional spaces allows us to position the different proposals that have sought to explain self-organization in social systems--natural (e.g. an ant colony), human (e.g. a community) and artificial (e.g. cellular automata). It distinguishes between the varying emphases and focuses on the way of studying the core aspects of self-organization from each approach.
In the second part of this paper we emphasize the differences between complexity sciences, CAS...
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