Navigating Complexity
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Diagram: Local Interactions

Local Interactions

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

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Similarly, we can create a very simple agent-based model, a grid composed of cells that follow basic rules that will turn a given cell on the grid to either an 'on' or 'off' setting - depending on the status of neighboring cells - and then let the system unfold step by step.

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Wohl, S. (2022, 8 June). Local Interactions. Retrieved from https://kapalicarsi.wittmeyer.io/definition/local-interactions

Local Interactions was updated June 8th, 2022.

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By Sharon Wohl

This resource if from a course on complex systems taught by Sharon Wohl

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Cellular Automata & Agent-Based Models offer 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 strategy looks at how rule based systems yield emergent structures.

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Many cities around the world self-build without top-down control. What do these processes have in common with complexity?

Cities around the world are growing without the capacity for top-down control. Informal urbanism is an example of bottom-up processes that shape the city. Can these processes be harnessed in ways that make them more effective and productive?

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New ways of modeling the physical shape of cities allows us to shape-shift at the touch of a keystroke.  Can this ability to generate a multiplicity of possible future urbanities help make better cities?

Parametric approaches to urban design are based on creating responsive models of urban contexts that are programmed to change form according to how inputs are varied. Rather than the architect creating a final product, they instead create a space of possibilities ({{phase-space}}) that is activated according to how various flow variables - economic, environmental, or social, are tweaked. This architectural form-making approachholds similarities to complex systems in terms of how entities are framed: less as objects in and of themselves, and more as responsive, adaptive agents, activated by differential inputs.
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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.

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There would be some thought experiments here.

Navigating Complexity © 2015-2024 Sharon Wohl, all rights reserved. Developed by Sean Wittmeyer
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Related (this page): Urban Modeling (11), Parametric Urbanism (10), Incremental Urbanism (13), Evolutionary Geography (12), Rules (213), 
Section: terms
Non-Linearity
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Related (all): Urban Modeling (11, fields), Resilient Urbanism (14, fields), Relational Geography (19, fields), Landscape Urbanism (15, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Assemblage Geography (20, fields), Tipping Points (218, concepts), Path Dependency (93, concepts), Far From Equilibrium (212, concepts), 
Nested Orders
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Related (all): Urban Modeling (11, fields), Urban Informalities (16, fields), Resilient Urbanism (14, fields), Self-Organized Criticality (64, concepts), Scale-Free (217, concepts), Power Laws (66, concepts), 
Emergence
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Related (all): Urban Modeling (11, fields), Urban Informalities (16, fields), Urban Datascapes (28, fields), Incremental Urbanism (13, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Assemblage Geography (20, fields), Self-Organization (214, concepts), Fitness (59, concepts), Attractor States (72, concepts), 
Driving Flows
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Related (all): Urban Datascapes (28, fields), Tactical Urbanism (17, fields), Relational Geography (19, fields), Parametric Urbanism (10, fields), Landscape Urbanism (15, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Assemblage Geography (20, fields), Open / Dissipative (84, concepts), Networks (75, concepts), Information (73, concepts), 
Bottom-up Agents
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Related (all): Urban Modeling (11, fields), Urban Informalities (16, fields), Resilient Urbanism (14, fields), Parametric Urbanism (10, fields), Incremental Urbanism (13, fields), Evolutionary Geography (12, fields), Communicative Planning (18, fields), Rules (213, concepts), Iterations (56, concepts), 
Adaptive Capacity
Related (same section): 
Related (all): Urban Modeling (11, fields), Urban Informalities (16, fields), Tactical Urbanism (17, fields), Parametric Urbanism (10, fields), Landscape Urbanism (15, fields), Incremental Urbanism (13, fields), Evolutionary Geography (12, fields), Feedback (88, concepts), Degrees of Freedom (78, concepts),