Seeing information in motion powerful because we get a sense of the relational dimensions of data. Each piece of data can be examined but we can't understand the patterns and possible implications until we see the wider context and the dynamics of that context. Though the following video wasn't devised to simulate network formation, I couldn't help but see the deep connection to how various agents/people in motion form networks over time that are patterned but not neatly.
In this program, variables can be changed and the program re-run. The crossing point in this came with my recent reading of Greg Spencer's paper "Creative economies of scale: an agent-based model of creativity and agglomeration" which features modelling of the dynamics of location, knowledge generation, and innovation. When the ideas in that paper are considered alongside Alvin's art/visualization/programming work, the possibilities are intriguing. Here's what Alvin says about his project:
With some adjustment of variables, we can see how people, rather than particles, might form communities of practice, social gatherings, business connections, and other identifiable social patterns. Simulation does not equal an understanding of what actually exists, how it has changed or, most importantly, how it may change in the future. But if we set aside the predictive grail, modelling and simulations can help us gain understanding about how multiple variables interact over time. That can be useful indeed and the visualizations produced are powerful educational tools. They are also fantastic in translating specialized knowledge and complexity into a form that a whole range of people can understand and appreciated.
Depth Of Field (DoF) w/o Blur from Alvin C. on Vimeo.
In this program, variables can be changed and the program re-run. The crossing point in this came with my recent reading of Greg Spencer's paper "Creative economies of scale: an agent-based model of creativity and agglomeration" which features modelling of the dynamics of location, knowledge generation, and innovation. When the ideas in that paper are considered alongside Alvin's art/visualization/programming work, the possibilities are intriguing. Here's what Alvin says about his project:
Form finding particle field may generate a variety of forms through simple behavioural interactions. The interactions are dependant on variables such as displacement, proximity and density.
With some adjustment of variables, we can see how people, rather than particles, might form communities of practice, social gatherings, business connections, and other identifiable social patterns. Simulation does not equal an understanding of what actually exists, how it has changed or, most importantly, how it may change in the future. But if we set aside the predictive grail, modelling and simulations can help us gain understanding about how multiple variables interact over time. That can be useful indeed and the visualizations produced are powerful educational tools. They are also fantastic in translating specialized knowledge and complexity into a form that a whole range of people can understand and appreciated.
Depth Of Field (DoF) w/o Blur from Alvin C. on Vimeo.
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