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Illustration: Evolutionary Geography

Evolutionary Geography

Across the globe we find spatial clusters of similar economic activity. How does complexity help us understand the path-dependent emergence of these economic clusters?

Evolutionary Economic Geography (EEG) tries to understand how economic agglomerations or clusters emerge from the bottom-up. This branch of economics draws significantly from principles of complexity and emergence, seeing the rise of particular regions as path-dependent, and looking to understand the forces that drive change for firms - seen as the agents evolving within an economic environment.

Evolutionary Economic Geography is a branch of economics that tries to understand how the same kinds of processes observed in evolution can be applied to geographically situated economic clusters. It shares some similarities to Relational Geography in that it sees the specificity of the physical environment as something that arises due to networks of driving flows. Where it differs is partially in terms of its specific focus - that of economic actors situated in urban contexts (that is firms with particular expertise and economic output) - rather than the broader multiplicity of actors found within cities. Further, the field forefronts more of the dynamics of complexity than relational geography: attuning in particular to the Bottom-up Agents (in the form of firms), that make up these economic systems, as well the dynamics underlying their Adaptive Capacity to become more fit. Accordingly, the field employs what is known as "General Darwinism": using principles of variation, selection and retention (VSR) that we see in organic evolving systems, and applying these same principles to non-organic systems. 

Examples of the kinds of geographic phenomena that these evolutionary geographers might consider of interest would be the rise of Silicon Valley as a Tech hub, Holland's Tulip growing fields, or Taiwan's Orchid growing sector (see video below). These kinds of regions of specialized intensifications are called "agglomerations", and are described as arising in ways that conceptually correspond with Emergence. Thus, these kinds of intensities of expertise were not necessarily pre-planned from the top-down, but instead arose due to processes that are more akin to the evolutionary dynamics we see in nature. Furthermore, the ways in which these dynamics unfold are tied to how Bottom-up Agents in complex systems are steered towards fitness. Here, individual firms are seen as "agents" in an economic system, all of which are competing to find niches for success. These firms are steered not only by the Feedback gathered from monitoring the success of their own actions, but also the signals gathered by attuning to the actions of their nearest competitors. 

Spill-overs and Negentropy

These signals help steer individual firm success, due to the benefits of what are known as "spill-over" effects. Another way to think about this is that, left to the their own independent devices, each firm needs to navigate the economic landscape with maximum uncertainty about how best to proceed in order to "harness" the Driving Flows of monetary gain. By co-locating near similar agents, the amount of uncertainty to achieve this can be reduced (see Information). Uncertainty in this case, might pertain to industry "best practices" that are coming to the fore, personnel that are knowledgeable and available in the region to be hired, and synergetic support businesses present and able to carry out aspects of the delivery model. Thus, the backdrop of Silicon Valley provides expertise and support "in the air" to give businesses in the region a competitive edge over others located in more isolated regions. 

Intensifying Flows & Feedback

Some of the dynamics pertaining to why a particular economic agglomeration emerges involve the kinds of network effects seen in conditions of growth and Preferential Attachment. As certain business sectors begin - potentially at random - to co-locate in a particular region, other support services become attracted to that area, which then attract further businesses, and so on. We see again the mechanism of Positive Feedback reinforcing particular patterns, which then take hold as Attractor States for agents in the system. 


We can therefore consider firms in a regions as competing Bottom-up Agents, each trying to tweak the Variables of their business models so as to outcompete their neighbors. Yet even though they are engaged in competition, they nonetheless have some reliance on their competitors: it is through their co-presence that many simultaneous business protocol Iterations can be tested in parallel, with the overall expertise of the co-located enterprises being enhanced. Accordingly, agglomerations of these co-located competing firms are more likely to increase their Fitness than firms operating at a distance. 

Enslavement or "Lock-in"

It becomes very difficult to disrupt an agglomeration once it has emerged. Too many of the flows related to a particular sector become concentrated in this geographic regions, meaning that massive structural shifts are required to rearrange these flows. This is not to say that this can never occur. Detroit, for example, was for many years the power-house for automotive manufacturing. It was only with the advent of major underlying shifts of flows - tied to such aspects as wages, access to cheaper workers, and lowered shipping costs - that these flows gradually reconstituted themselves in new geographic locations off-shore. But these major shifts are rare, with regions of expertise reproducing themselves over time, even in the face of other underlying disruptions. Such systems can be described as being in Enslaved States, or what is called "lock-in" by Evolutionary Economic Geographers. 

The video below outlines an example of an emergent agglomeration: that of Orchid growing in Taiwan.

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Photo Credit and Caption: Image Credit: via Flickr

Cite this page:

Wohl, S. (2022, 13 June). Evolutionary Geography. Retrieved from

Evolutionary Geography was updated June 13th, 2022.

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Evolutionary Economic Geography

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Assemblage Geography | Deleuze

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This is a list of Terms that Evolutionary Geography is related to.

Related to the idea of Iterations that accumulate over time

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The quantity and breadth of a system's adaptive potential is its 'requisite variety'.

In order for a complex system to adapt, it needs to contain agents that have the capacity to behave in different ways - to enact adaptation you need adaptable things. Learn more →

..or the rich get richer!

Think of preferential attachment as an attribute of when 'the rich get richer' within a networked system. This occurs when nodes that have a lot of links tend to attract more links as other nodes enter the system resulting in super-nodes. Learn more →

Agents in a Complex System are guided by neighboring agents - nonetheless leading to global order.

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Emergent states often constrain  the agents that initially formed that state.

An enslaved state can persist as an attractor (see Attractor States) within a Fitness Landscape.

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Excerpt from a course on Complex Adaptive Systems

Input by Dr. Sharon Wohl

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This is a list of Urban Fields that Evolutionary Geography is related to.

This is a list of Key Concepts that Evolutionary Geography is related to.

Self-organization refers to processes whereby coordinated patterns or behaviors manifest in a system without the need for top-down control.

A system is considered to be self-organizing when the behavior of elements in the system can, together, arrive at a globally more optimal functional regimes compared to if each system element behaved independently. This occurs without the benefit of any controller or director of action. Instead, the system contains elements acting in parallel that will gradually manifest organized, correlated behaviors: Emergence.

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'Path-dependent' systems are ones where the system's history matters - the present state is contingent upon random factors that governed system unfolding, and that could have easily resulted in other viable trajectories.

Complex systems can follow many potential trajectories: the actualization of any given trajectory can be dependent on small variables, or "changes to initial conditions" that are actually pretty trivial. Accordingly, if we truly wish to understand system dynamics, we need to pay attention to all system pathways (or the system's phase space) rather than the pathway that happened to unfold.

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Network theory allows us think about how the dynamics of agent interactions in a complex system can affect the performance of that system.

Network theory is a huge topic in and of itself, and can be looked at on its own, or in relation to complex systems. There are various formal, mathematical ways of studying networks, as well as looser, more fluid ways of understanding how networks can serve as a structuring mechanism. Learn more →

What drives complexity? The answer involves a kind of sorting of the differences the system must navigate. These differences can be understood as flows of energy or information.

In order to be responsive to a world consisting of different kinds of inputs, complex systems tune themselves to states holding just enough variety to be interesting (keeping responsive) and just enough homogeneity to remain organized (keeping stable). To understand how this works, we need to understand flows of information in complex systems, and what "information" means. Learn more →

Feedback loops occur in system where an environmental input guides system behavior, but the system behavior (the output), in turn alters the environmental context.

This coupling between input affecting output - thereby affecting input - creates unique dynamics and interdependencies between the two.

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Complex Systems can unfold in multiple trajectories. However, there may be trajectories that are more stable or 'fit'. Such states are considered 'attractor states'.

Complex Adaptive Systems do not obey predictable, linear trajectories. They are "Sensitive to Initial Conditions", such that small changes in these conditions can lead the system to unfold in unexpected ways. That said, in some systems, particular 'potential unfoldings' are more likely to occur than others. We can think of these as 'attractor states' to which a system will tend to gravitate.

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There would be some thought experiments here.

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