Online Q&A communities have become an important channel for internet users to seek information and share knowledge. Existing research extensively focuses on the individual components of Q&A communities, such as content quality and user characteristics, but fails to provide a comprehensive understanding of the communities as complex social systems, whose behavior depends on the interactions of a large number of social agents. In this dissertation, I integrated the key components in online Q&A communities via agent-based modeling to provide a systematic examination of Q&A communities and help inform better community design to manage users' expertise. I conducted computer simulations and virtual experiments based on existing findings and theories as well as data from a large online Q&A community to understand how two design decisions, including expertise indication and question routing, influence the sustainability of a Q&A community as well as result in possible trade-offs involved in implementing these design decisions. Results indicate that these design decisions are likely to lead to a larger membership size and a higher rate of solved questions. In addition, implementing design decisions will also influence the member structure of a community. Question routing tends to prioritize experts' needs and benefits while expertise indication is more likely to attract beginners. These findings suggest that these design decisions should be leveraged according to the development stage a community is in. This research also demonstrates the value of agent-based modeling in terms of generating insights for Q&A community design by showing the underlying structural outcomes of the design decisions.
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