Visualizing Emergence seeks to explore and visualize phenomena of emergence in data representing technologically mediated human communication and exchange, that is, within a techno-social complex adaptive system (CAS).
Through interactions among their large numbers of parts, such systems exhibit emergent properties that cannot be predicted or understood simply from the rules of their lower-level components (Capra 1996, 29). For example an organism’s behaviour cannot be understood from the properties of its bio-molecules (Kauffman, Logan, Este, Hobill, Shmulevich, 2007). A principal means of understanding emergence is through modeling and visualization (Miller & Page, 2007; Sawyer, 2005).
The initial question we’ll investigate is, “How might data visualization techniques illuminate the role of emergence in the process of diffusion of innovations (Rogers 1962/2003), including the adoption and spread of new technologies and new ideas (memetics)?” This motivation stems from Van Alstyne and Logan (2007) in which emergence is seen as necessary for innovation. Hypotheses regarding the role of ‘bottom-up’ (emergent) and ‘top down’ (designed) forces will be formulated and tested. We will elaborate insights from Capra (1996; 2002), Barabási (2003), and Christakis and Fowler (2009).
Using textual analysis and other data as substrate, we’ll focus on data from CIV-DDD partners, IBM Cognos and public sources, possibly including Twitter and other accessible APIs. In time we expect to aggregate data from additional sources. Leveraging senior researcher and student contributions from OCAD and York Universities, the project will explore and exploit a synthesis of scientific, artistic and aesthetic techniques, with software from partners including IBM / Cognos.
Visualizing Emergence is supported by NCE-GRAND. This project is funded in part by the Centre for Information Visualization and Data Driven Design established by the Ontario Research Fund (ORF).
We will test, review and select the most appropriate software approach for developing the data models and generating dynamic results. The work will deliver findings tied to the following CIV-DDD project aims:
This research will address challenges including:
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