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Cellular Automata & Agent-Based Models can provide city simulations whose behaviors we learn from. What are the strengths & weaknesses of this mode of engaging urban complexity?
There is a large body of research that employs computational techniques - in particular agent based modeling (ABM) and cellular automata (CA) to understand complex urban dynamics. This research looks at rule based systems that yield emergent structures.
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Urban Modeling
This is a default subtitle for this page. Read more and see related content for Denise Pumain →Urban Computational Modeling
Mike Batty is one of the key contributors to modeling cities as Complex Adaptive Systems
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Complex System behaviors often exhibit power-laws: with a small number of system features wielding a large amount of system impact.
Power laws are particular mathematical distribution that appear in contexts where a very small number of system events or entities exist that, while rare, are highly impactful, alongside of a very large number of system events or entities exist that, while plentiful, have very little impact. Power laws arise in both natural and social system, in contexts as diverse earthquake behaviors, city population sizes, and word frequency use.
'Normal' vs 'Power Law' Distributions
Complex systems are often characterized by power law distributions. A power law is a kind of mathematical distribution that we see in many different kinds of systems. It has different properties from a well known distribution - a 'bell curve' 'normal' or 'Gaussian' distribution.
Let's look at the two here:
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