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By using graph-theoretic techniques to compare the information processing behaviors of three groups of mid-level working professionals as each undertakes a series of four complex, interdependent, computer-mediated decision-making exercises, this thesis explores 1) the relationship between network centrality and individual performance and 2) the relationship between network density and group performance. The results of this exploration, though mostly inconclusive, call into question both intuition and social network analysis literature. It is predicted that centrality in a network correlates positively with high performance among individuals, but statistical analysis of data collected during controlled experimentation reveal an almost negligible relationship. It is also hypothesized that high density groups outperform low density groups, but density and performance are found to correlate in exactly the opposite direction: as density increases, group performance decreases. As an explanation, this thesis proposes that as network density increases actors require more time to process and respond to incoming information. In as much as central actors possess a greater number of edges (i.e., communication linkages to others), this thesis also argues that centrality in a network has costs, as well as benefits. Further experimentation is needed to test the validity of these conjectures and bring better understanding to Organization Theory, Social Network Analysis, and Information Processing networks. http://archive.org/details/attributesofsucc109453330 US Navy (USN) author. Approved for public release; distribution is unlimited. |
