This article is an attempt to explore the implications of the emerging science of complexity for the management of organizations. It is not intended as an introduction to complexity thinking, but rather an attempt to consider how thinking ‘complexly’ might affect the way in which managers do their jobs. This is achieved in a rather abstract way with some theory, but I hope the general message that there is no one way to manage comes through loud and clear, and that management is as much an art as it is a science. In a sense complexity thinking is about limits, limits to what we can know about our organizations. And if there are limits to what we can know, then there are limits to what we can achieve in a pre-determined, planned way.
While complexity researchers have made considerable advances in recent years, complexity thinking, as a formal discipline, has yet to enter the mainstream. We believe that this is partially a consequence of the packaging. The relative dearth of research into practical tools, when compared with that conducted in the areas of philosophy and theory, serves to compound the problem. Given the difficulties experienced by those attempting to transfer complexity ideas from the laboratory to the field, maybe we can best approach the development of tools from alternative theoretical directions—and use our understanding of complexity to evaluate and enhance them. In this article, we introduce Confrontation Management—a theory of human interaction that has its roots in Game Theory—and show that this theory supports the modeling and analysis of, and planning within, complex social systems. As such, we suggest that it represents a powerful addition to any complexity practitioner’s toolbox.
Industrial ecology is a rapidly developing field of research and practice in which the sustainability of industrial systems is thought to be improved through closing of material and energy loops among firms. In this paper, I look at the developing practice around this concept from a self-organization perspective. A central question is the extent to which closing of material loops has to be planned and guided by governmental agencies. Based on a longitudinal case study of industrial ecology development in the Rotterdam harbor area (the Netherlands), the interplay between self-organization, external control, and vision development is analyzed.
This article approaches the spatial development of the port of Rotterdam in the Netherlands from a coevolutionary point of view. We use two main concepts within coevolutionary framework; bounded instability and punctuated equilibrium, to understand the relationship between Dutch spatial policies and actual developments in the port of Rotterdam. We observe that the actual port system is generally more diverse than the public policy that governs it, and that the policy appears to simply follow and codify port developments. This result negates the assumption that spatial developments in the port of Rotterdam are steered and planned through public policy and raises several questions on the role of such policy initiatives.
This paper sets out how models from natural science can be used within the management domain. We contend that this transformation between domains is best served by agent-based models, where the agent behavior is important, not the specifics of the agent type. We also note that these models are useful for exploring complexity and extending the research that has been performed within management to date. We demonstrate this with two models: the NK model, a theoretical biology model that has had 10 years of development within the strategy field, and the Forest Fire model, a model from physics that is at an early stage within its application within the management domain. In doing so, we also focus on the specific issues that need to be addressed when applying and extending these models to management studies due to the ontological differences between the realms of natural science and social science.
The harmonious melding of structure and function—biological design—is a striking feature of complex living systems such as tissues, organs, organisms, even superorganismal assemblages like social insect colonies or ecosystems. How designed systems come into being remains a central problem in evolutionary biology. The prevailing explanation for biological design rests on essentially atomist doctrines such as Neodarwinism or emergence of complexity from self-organized systems of interacting agents. The Neodarwinist explanation for design, for example, posits that good design results from selection for “good function/structure genes” at the expense of “poor function/structure genes.” Along the same lines, self-organization promises “order for free”—sophisticated structures and behaviors that emerge from simple interactions among agents at lower levels of organization. It is doubtful, however, whether such atomist doctrines by themselves can explain the origins of designed living systems. In this article, I argue that the missing piece of the puzzle is homeostasis, a classical concept that is not itself inherent in atomist explanations for adaptation and design. I couch my argument in observations on the emergence of a spectacular social insect “superorganism”: the nest and mound of the macrotermitine termites, which can best be explained as the emergent product of agents of homeostasis. This poses interesting challenges to the prevailing reductionism that permeates our current thinking on design, adaptation and evolution.
Seven problems that occur in attempts to measure complexity are pointed out as they occur in four proposed measurement techniques. Each example method is an improvement over the previous examples. It turns out, however, that none are up to the challenge of complexity. Apparently, there is no currently available method that truly gets the measure of complexity. There are two reasons. First, the most natural approach, quantitative analysis, is rendered inadequate by the very nature of complexity. Second, the intrinsic magnitude of complexity is still holding at bay attempts to use both quantitative and qualitative methods combined. Further progress in complexity science and in systems science is required. Any method that simplifies will fail because it ignores what complexity is. Techniques of understanding that do not simplify, but rather provide ways for the mind to grasp and work with complexity are more effective in getting its measure.
Last year I wrote a column about the notion of Applied Chutzpah—a willingness to step forward into audacious action even though one may have no idea how one will either pull it off, or where it will ultimately lead. The only real knowing at the moment of Applied Chutzpah is an intuition that if some […]