The Geographic Mosaic of Coevolution and Co-Evolutionary Degree and Effect

Co-evolution is one of the major processes organizing the intertwined populations (for example, technology and institution). But co-evolution is an ongoing process so that a geographic perspective is needed. The biggest current challenge for economics and regional development science is to understand how co-evolution operates across broad geographic landscapes, how some regions gained high economic performance while others did not. At the same time, it is necessary to see co-evolution itself as a self-reinforcing process in which the interactive effects and degrees of intertwined populations varied over time.

2.4.1 The Theory of the Geographic Mosaic of Coevolution in Biology

Recently John N. Thompson, an outstanding scholar in evolutionary biology, provides a framework for asking how co-evolution continually reshapes interactions across different spatial and temporal scales (Thompson, 1994, 2005). This framework of “the geographic mosaic of co-evolution” analyzes how the biology of species provides the raw material for long-term co-evolution, evaluates how local co-adaptation forms the basic module of evolutionary change, and explores how the co-evolutionary process reshapes locally coevolving interactions across the earth's constantly changing landscapes, and then tries to answer how geographically structured co-evolution differs in various locations. I believe that this intellectual satisfying work would be also important for understanding these co-evolutionary processes in human-altered systems, namely how co-evolution continually reshapes interactions across different spatial and temporal scales.

2.4.2 The Geographic Mosaic of Coevolution in Economy

In fact, we can find similar phenomena of geographic mosaics in economic life.

Economic growth is not geographically even. Britain gained the hegemony in textile industry in the late half of the 18^th century, continental Europe (Germany and Switzerland) in the 19th century dominated over machine tools, chemicals, and

pharmaceuticals etc, but during most of the 20th century America held the supremacy in semiconductors, petrochemicals, computers, and biotechnology. Later the Asian Newly-Industrializing countries (NICs, Japan, Korea) got partial leaderships in electronics, machine tools during the second half of the last century (Nelson and Wright, 1992; Nelson and Pack, 1999; Schamp, 2000; also see Fatas-Villafranca et al, 2008).

There is a well-known fact at some point in the development process of multiple industries that a handful of regions and a small number of firms from the same nation (or from a small group of nations) have reached an unquestionably competitive position on a worldwide level. Today’s world economic climate is dominated by the first-class industrial clusters, which have become powerful instruments for building economic capacity for regions to compete in the global market. The typical examples cover the computer technology clusters in Silicon Valley, the financial clusters in New York and London, the movie production cluster in Hollywood, the automotive clusters in Southern Germany and Detroit, the aerospace cluster in Toulouse, the fashion clusters in Northern Italy, software outsourcing in Bangalore, the diamond cluster in Antwerp and others (Porter, 1990). The geographic concentration of competitive industries constitutes the geographic mosaic of material wealth. Here I prefer to call these competitive industrial cluster hotspots of co-evolution among firms, technology and institution. Hot spots are regions in which interacting populations have reciprocal effects on each other’s fitnesses through the mechanisms of local co-adaptation and selection and are often embedded within broader surrounding regions in which the fitnesses of at most one of the two species depends on the interactions with the second species (Co-evolutionary cold spots).

2.4.3 Call for a Study on Co-Evolutionary Degree and Effect

There has been no disputation in the field of biology about the idea that species co-evolve as groups of genetically distinct populations. Initially, many biologists believed co-evolution occurred rarely but only under strong pair-wise interaction. From the late 1960s onwards, however, a growing number agreed with Darwin that evidence of co-evolution was far from rare (Winder et al., 2005; Thompson, 1994)³⁴. The idea that grass and grazers, predators and prey, mammals and their parasites have not emerged by co-evolution seems implausible, and even absurd. But we should note that

34 See Thompson 1994 for a history of co-evolutionary biology.

the concept of co-evolution is very fuzzy, because no entity is isolated, all processes interact with their environment and other species, and species and populations are a part of the environment that determines the selection pressure experienced by others. As Winder et al. （2005, p: 356) pointed out, perhaps bees and donkeys have a co-evolutionary impact on each other, but the dynamic linkages between them are so weak and rates of co-evolution are so slow that they can be treated as isolated evolutionary systems at a first level of approximation.

Moreover, the uncritical and direct application of the biological concept of co-evolution to the study of human society is problematic. Even in biology, no population of one species co-evolves with one population of another species within a real biological world. Co-evolution in real species, however, involves multiple interconnected populations, and complex environments (Thompson, 2005, p: 9). From this point, we have to say that, if we can’t carefully examine the degree of interacting link between populations, co-evolution will otherwise make no sense. Another important statement is that we should differentiate positive co-evolution from negative one. Despite evolution, like evolution, is a value-neutral concept, the co-evolutionary result can be added to value, for example, good and bad for human welfare. Furthermore, the plea for an examination of co-evolutionary effect and degree is also in connection with a few empirical studies on this aspect in social sciences.

Hence I will adopt a dynamic viewpoint to study co-evolution itself, but my geographical level is the level of industrial cluster, a sub-national level.

2.4.4 Co-Evolutionary Degree and Effect at a Regional Level

In order to illustrate this argument more clearly, we can group different types of regions along two axes: the degree of relationship between co-evolving populations (coevolutionary strength: week or strong) and the effect of relationship between co-evolving populations (coevolutionary effect: positive or negative). We can identify four types of regions. First, there are some regions with a lower degree of co-evolution among firm, institution and technology as well as a lower level of positive effect, as referred to ‘cold spots’. A good example is Zhong'guancun before 1980. It was not until the early 1980s that the commercialization of scientific and technological knowledge began in China’s “Silicon Valley” and largest intellect-intensive region where the research and education establishments have been (and still are) densely concentrated (Wang and Wang, 1998).

Figure 2.2: Coevolutionary curve of degree and effect

Secondly, there are only a few examples concerning regions with a lower degree of interaction in firm, institution and technology but dominated by a higher positive co-evolutionary effect. One German example is the textile industry in Westmuensterland (from 1980s onwards) in which continuous new entrants, moreover the minor importance of this sector to local society in the terms both of economic and employment contribution, jointly weaken potential lock-ins, and these relatively weak functional, cognitive, and political lock-ins in turn lead to a successful renewal (Hassink, 2007).

Thirdly, at the opposite end of the figure we find some regions in which technology, institution and firms are tightly coupled (“strong co-evolution”), and this higher interaction brings about a “positive co-evolutionary effect”. This type of region is what Pouder and St John (1996)define as “a hot spot”. Nearly all most successful regions in history can be classified in this group, for example, the information industry in Silicon Valley in California from the 1980s onwards and nowadays top biotechnological clusters over the globe, such as Cambridge Biotech cluster and German BioRhine, the Scientific City in France, the ceramics industry in Sassuolo, Italy, auto manufacturing in the Basque Region, and medical instruments in Tuttlingen, Germany.

Fourthly, there are regions or industries with a high co-evolutionary degree but

Effect

Degree

Type Ⅳ Coevolutonary

cold spots Type I Positive

Coevolutionary hotspots

Negative

Strong Weak

Type Ⅲ TypeⅡ

without so much positive co-evolutionary effect. Nearly all heavy industry complexes in old industrial regions, for example, Wales in UK, the Ruhr Area in Germany, and Great Lakes Regions in America between 1970s and 1980s (Cooke, 1995) belong to this type. The deteriorating performance of a hot spot can also be referred to as the development of ‘core rigidities’ (Leonard-Barton, 1992) or negative ‘lock-in’ (Grabher, 1993)

It is necessary to note that the different types of industries or regions are movable.

Generally and theoretically speaking, economic performance rises with strong interacting links between institution, firms and technology. However, extreme strong ties lead them to ignore changes outsides the local community, and further result in a dangerous situation of lower performance. Once on this trajectory, it is not easy to go out, without strong external shocks.