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)
|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)|
|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|