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Patterns in Knowledge Production

Abstract This dissertation will look at large scale collaboration through the lens of online communities to answer questions about what makes a collaboration persist. Results address how collaborations attract contributions, behaviors that could give rise to patterns seen in the data, and the properties of collaborations that drive those behaviors.

It is understood that collaborations, online and otherwise, must retain users to remain productive. However, before users can be retained they must be recruited. In the first project, a few necessary properties of the ``attraction'' function are identified by constraining the dynamics of an ODE (Ordinary Differential Equation) model. Additionally, more than 100 communities of the Stack Exchange ... (more)
Created Date 2017
Contributor Manning, Miles (Author) / Janssen, Marcus A (Advisor) / Castillo-Chavez, Carlos (Advisor) / Anderies, John M (Committee member) / Kang, Yun (Committee member) / Arizona State University (Publisher)
Subject Applied mathematics
Type Doctoral Dissertation
Extent 97 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Applied Mathematics for the Life and Social Sciences 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis