A Family of Algorithms for Computing Consensus about Node State from Network Data - Brush, Eleanor R.; Krakauer, David C.; Flack, Jessica C. (PLOS Computational Biology, 2013)
Author Summary Decision making in complex societies requires that individuals be aware of the group's collective opinions about themselves and their peers. In previous work, social power, defined as the consensus about an individual's ability to win fights, was shown to affect decisions about conflict intervention. We develop methods for measuring the consensus in a group about individuals' states, and extend our analyses to genetic and cultural networks. Our results indicate that breadth algorithms, which measure consensus by taking into account the number and uniformity of an individual's direct connections, correctly predict an individual's function even when some of the group members have erred in their assessments. However, in cases where nodes “form opinions” about other nodes using indirect information algorithms that measure the depth of consensus, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. We also discuss the algorithms' cognitive and computational demands. These are important considerations in systems in which individuals use the collective opinions of others to make decisions. Finally, we discuss the implications for the emergence of social structure.