We continue by telling people that they can look into the history and learn more about the cartograph. History
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Relates to {{Assemblage-Geography}}
All points are interconnected and interdependent, unfolding in a nonlinear manner with no central source of authority.
Early versions of systems theory assumed that systems could be 'optimized' to a single condition. CAS analysis assumes that more than one system state can satisfy optimizing criteria, and so the system is able to gravitate to multiple equilibria.
This is relevant to the field of Relational Geography
An enslaved state can persist as an attractor (see Attractor States) within a Fitness Landscape.
Beyond its day-to-day usage, this term used in now employed in the social sciences to highlight the Path Dependency exhibited in many social systems. This is seen to contrast with prior conceptions like "the march of history", which imply a clear causal structure. By speaking about the work as something contingent, it also begs the question of what other "worlds" might have just as equally manifested, had things been slightly different.
Similar ideas are captured in the ideas of Non-Linearity, {{sensitivity-to-initial-conditions}}, History Matters.
Pictured below: the contingent trajectory of the double pendulum:
See also: Causal loop diagram - Wikipedia
In geography there has been a move away from thinking about space as a "thing" and to instead think about how different places exist due to how they interact with flows. Places that capture more flows, are more geographically relevant
The nature of a building block varies according to the system: it may take the form of an ant, a cell, a neuron or a building.
Complex Adaptive Systems theory provides a useful lens with which to understand various phenomena. Keep reading about Complexity
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Urban FieldsWe continue by telling people that they can look into the history and learn more about the cartograph. People
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Complex systems tend towards scale-free, nested hierarchies. By 'Scale-free', we mean to say that we can zoom in on the system at any level of magnification, and observe the same kind of structural relations.
A good example here is the fractal features of a leaf:
We can think of the capillary network as the minimum structure required to reach the maximum surface area.
Here, the scale-free structure of the capillary network allows the most efficient transport of nutrients to all parts of the leaf surface within the overall shortest capillary path length. This 'shortest overall path length' is one of the reasons that we might often see scale-free features in nature: this may well be the natural outcome of nature 'solving' the problem of how to best economize flow networks.
minimum global path length to reach all nodes
The two images serve to illustrate the idea of shortest overall path length. If we wish to get resources from a central node to 16 nodes distributed along a surrounding boundary, we can either trace a direct path to each point from the center, or we can partition the path into splitting segments that gradually work their way towards the boundary. While each individual pathway from the center to an individual node is longer in the right hand image, the total aggregate of all pathways to reach all nodes from the center is shorter. Thus the image on the right (which shows scale-free characteristics), is the more efficient delivery network.
We should therefore expect to see such forms of scale-free dynamics in other non-natural systems that carry and distribute flows: thus, if we think of size distribution of road networks in a city, we would expect a small number of key expressways carrying large traffic flows, followed by a moderate number of mid-scaled arteries carrying mid-scale flows, then a large number of neighborhood streets carrying moderate flows, and finally a very high number of extremely small alleys and roads that each carry very small flows to their respective destinations.
mud fractals and street networks
Fractals, scale-free networks, self-similar entities and power-law distributions are concepts that can be difficult to disambiguate. Not all scale-free networks look like fractals, but all fractals and scale-free networks follow power-laws. Finally, there are many power-law distributions that neither 'look' like fractals, nor follow scale-free network characteristics: if we take a frozen potato and smash it on the ground, then classify the size of each piece, we would find that the distribution of smashed potato pieces follows a power law (but is not nearly as pretty as a fractal!). Finally, self-similar entities (like the romanesco broccoli shown below) are fractal-like (you can zoom in and see similar structure at different scales), but are not as mathematically precise as a fractal.
credit: Wikimedia commons (Jon Sullivan)
Photo Credit and Caption: Underwater image of fish in Moofushi Kandu, Maldives, by Bruno de Giusti (via Wikimedia Commons)
Cite this page:
Wohl, S. (2022, 30 May). Scale-Free. Retrieved from https://kapalicarsi.wittmeyer.io/definition/scale-free
Scale-Free was updated May 30th, 2022.
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Scaling | Criticality | Power Laws
'Fingerprint of complexity' extraction plunger pot, bar single shot froth eu shop latte et, chicory, steamed seasonal grounds dark organic.
Read more and see related content for Ricard Solé →Scale-Free Networks
Really the first to move it beyond graphs
Read more and see related content for Albert Laszlo Barabasi →This is a list of Terms that Scale-Free is related to.
Patterns that repeat, regardless of scale/zoom
An aspect (not always) of certain {{fractals}}
Read more and see related content for Self Similarity →While Fractals are visually striking on their own, such topological regularities in complex systems are significant not only due to their inherent beauty , but because of the dynamics at work which generate such patterns.
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