  The internet has enabled collaborations at a scale never before possible, but
the best practices for organizing such large collaborations are still not
clear. Wikipedia is a visible and successful example of such a collaboration
which might offer insight into what makes large-scale, decentralized
collaborations successful. We analyze the relationship between the structural
properties of WikiProject coeditor networks and the performance and efficiency
of those projects. We confirm the existence of an overall
performance-efficiency trade-off, while observing that some projects are higher
than others in both performance and efficiency, suggesting the existence
factors correlating positively with both. Namely, we find an association
between low-degree coeditor networks and both high performance and high
efficiency. We also confirm results seen in previous numerical and small-scale
lab studies: higher performance with less skewed node distributions, and higher
performance with shorter path lengths. We use agent-based models to explore
possible mechanisms for degree-dependent performance and efficiency. We present
a novel local-majority learning strategy designed to satisfy properties of
real-world collaborations. The local-majority strategy as well as a localized
conformity-based strategy both show degree-dependent performance and
efficiency, but in opposite directions, suggesting that these factors depend on
both network structure and learning strategy. Our results suggest possible
benefits to decentralized collaborations made of smaller, more tightly-knit
teams, and that these benefits may be modulated by the particular learning
strategies in use.
