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close this bookIndustrial Metabolism: Restructuring for Sustainable Development (UNU; 1994; 376 pages)
View the documentNote to the reader from the UNU
View the documentAcknowledgements
View the documentIntroduction
close this folderPart 1: General implications
Open this folder and view contents1. Industrial metabolism: Theory and policy
Open this folder and view contents2. Ecosystem and the biosphere: Metaphors for human-induced material flows
Open this folder and view contents3. Industrial restructuring in industrial countries
Open this folder and view contents4. Industrial restructuring in developing countries: The case of India
close this folder5. Evolution, sustainability, and industrial metabolism
View the documentIntroduction
View the documentTechnical progress and reductionism
View the documentThe mechanical paradigm
View the documentThe evolution of ecological structure
View the documentDiscussion
Open this folder and view contentsPart 2: Case-studies
Open this folder and view contentsPart 3: Further implications
View the documentBibliography
View the documentContributors

The evolution of ecological structure

In recent years there has been some talk about the ecological restructuring of industry. But before we can discuss this coherently, we need to know what precisely ecological structure is. What is the organizational principle which underlies it? If we cannot answer this, then we surely cannot hope to organize human systems in an "ecological" manner. So, first, let us consider the only example that we have of sustainable structure: an ecosystem.

The key issues concern such questions as why the ecosystem is as it is. Why this number of populations? Why not more or less? Why these connections and not others? What would happen if we interfered with the system? Are the feeding relationships necessary, or do they merely reflect proximity and convenience?

None of these questions can be answered from the flow diagram of figure 2. Not only that, but, if we build a model based on the appropriate mechanisms of birth and death which change the numbers of each population, we might imagine that we could then use a computer to predict the behaviour of the system and perform simulations for policy analysis.

Unfortunately, this is not the case. When we run our computer model, it simplifies down to just a few species, because there are parallel paths through the system. Some of these are more "effective" than others; this leads to the elimination of apparently inferior paths through the action of (un)"natural selection."

But, in reality this does not occur. The system remains complex. Some source of diversity successfully opposes the tendency to simplify down that is apparent in our computer simulation. And this is the key to the understanding of ecological structure, the evolutionary process, and sustainability itself. The organizing principle that underlies sustainable systems is the presence, the maintenance, and the production of microscopic diversity in the system! These ideas have been developed in a series of recent papers (Allen and McGlade, 1987a, 1989; Allen, 1990; Allen and Lesser, 1991).

Ecological structure results from the working of the evolutionary process, and this in turn results from the nature of ecological structure. We can understand the ecological structuring of human activities by considering a "possibility space" representing the technologies and options that could potentially arise. In practice, of course, this is a multidimensional space of which we would only be able to anticipate a few of the principal dimensions. Ecological structure emerges over time, as the types of behaviour present in our possibility space increase and become more complex over time, and this is what we have successfully modelled.

The possibility space will be explored if the methods or techniques that firms use are influenced by new scientific knowledge and new ideas or by information and perceptions concerning others. New no tions must be either generated within a company or may be copied or miscopied from others. Either way, cost and effort are expended in finding, filtering, and adapting ideas. New ideas still involve an element of risk when implemented. Physical constraints automatically ensure that some techniques do better than others, and so there is a differential rate of survival and of profitability.

Fig. 2 The Crystal River estuarine ecosystem (Source: Homes and Kemp, 1983)

The possibility space is filled with an "evolutionary landscape," with hills representing high performance. Our simulations show how behaviours that include "exploration" in possibility space, although loss-making in the short term, will in the longer term eliminate behaviours that are fixed. Although exploration is costly in the short term, a small fraction of the initiatives tried are better then previous practice, and it is the gradual amplification of these, and the suppression of the less successful tries, that allow an adaptive progress to higher performance.

It is the presence of variations in the behaviour which, though costly, provides the capacity to "climb" the hills of the adaptive landscape as a result of the differential success and failure of different variants. The landscape expresses the pay-off that would be experienced by an actor or company in competition with the behaviours used by its competitors at that time. But, of course, the landscape is not really fixed, because as soon as a new technology or technique is found to be successful, and a firm moves up a hill, the other firms will respond and change their behaviour, moving the hill away again. In addition, improvements in competitors' technology will also have the effect of pushing any given participant with fixed behaviour lower down the slope.

This means that, over the longer term, evolution favours populations that retain the ability to climb hills, that is, to learn, rather than those that can perform optimally in any given circumstances. We begin to discern the nature of survivable organizations, and of sustainability itself..

This perspective on evolution shows us the error involved in the traditional "equilibrium" view that has been current. If each technology were sitting on a hilltop, then no advantage could be gained from exploration. Evolution would be "over" and there would be nowhere better to evolve to, and nothing to learn. Complicated equations in possibility space would be unnecessary, and rational analysis would be able to optimize a firm's behaviour without evolutionary adaptation. In short, life would be simple but boring.

Fortunately, or unfortunately, we need not worry about this possibility, because this would only be true if evolution were really over. In the real world, competitors, allies, clients, technologies, raw materials, costs, and skills all change. Any group or firm that fixed its behaviour would sooner or later be eliminated, having no adaptive or learning capacity with which to respond.

The landscape in possibility space reflects the advantage to be gained from any particular option, and depends on the techniques and behaviours that happen to be present at a particular moment. The peaks of the landscape represent the present performance goals of the firm or group in question, whose decisions and innovations will try to move up the slope. However, the other actors of the system will continue to modify the landscape as they also adapt and change in pursuit of their goals. The goals of each type of actor coevolve with those of the others present.

These experiments show that a mixture of exploratory diffusion paths in some behaviour or technology space, and their differential success, makes the difference between what is merely mechanical and what, on the contrary, contains the capacity for adaptation and creativity. It is the latter that might be called "organic." It is this vision of ecological structure as a temporary balance between exploration and constraint that is at the core of our new understanding.

Computer models have been developed that show explicitly how these adaptive landscapes are generated by the mutual interaction of behaviours or technologies. In the space of possibilities, closely similar products are mostly in competition with each other, but there is some "distance" in this space, some level of dissimilarity, at which two products or technologies do not compete with each other.

If we begin with a single type of product, then it will grow until it reaches the limits set by the competition either for underlying resources or for customers. At this point, the pay-off for explorers and entrepreneurs switches from negative to positive, as they can now escape somewhat from competition. We see that any successful behaviour eventually digs a hole in the landscape, until there is a hill to climb on either side and exploration is rewarded. Growth is restricted initially because of the "competitive shadow" of the original behaviour, but at a certain distance the products are sufficiently different from the original type; they begin to reach another market and require different resources.

In its turn, this new behaviour or product increases in volume until it too is limited by internal competition for the limiting resource; and once again there is a pay-off for innovation, particularly for those on the outside of the distribution, as they climb another slope towards new regions of possibility space. An evolutionary tree develops, branching as it grows. However, there are also moments when completely novel options emerge spontaneously during the simulation, and an ecology of interdependent behaviours emerges.

Fig. 3 The evolutionary landscape of untried options. Costly experimentation leads to better performance

The ecology that emerges is dynamic, since the identity of each behaviour is maintained by the balance between a continual diffusion of innovators outwards into the space of untried options and the competitive field that exists around it owing to the others. In fact, it would not be possible to anticipate the final range of technologies or products that will inhabit the system, because random events which occur during the "filling" process will affect the emerging pattern of new technologies or products. Instead of the system simply filling pre-existing market niches, the whole process is a creative one, which would be different if repeated from the same initial conditions.

This model offers us a non-reductionist, scientific basis for discussing the interaction of individuals and their collective structures. Such a system operates beyond the mechanical paradigm, because its response to external interventions can involve changes in structure and in the nature of the behaviours or technologies in the system. Suppressing particular components of such a system, perhaps as a result of changing market conditions or environmental regulations, will provoke a complex response from the system, as other behaviours adjust.

Although the "inventiveness" of the system is constantly present, as there is diffusion into the possibility space, it is fascinating to see that our research shows that only at certain moments in time does this lead to structural change. In other words, the system evolves in phases of apparent stability, separated by periods of instability and fairly rapid reorganizations, although the pressure of exploration and creativity is relatively constant.

Such a picture may eventually explain such phenomena as the cycles of growth and stagnation that seem to characterize our economic systems, a phenomenon that has been linked to "economic long waves" and the patterns of innovation and change.

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