12 March 2007
This is an abstract of todays PNG/CCCSN seminar with Daniel Diermeier (Kellogg School of Management, Northwestern University). We encourage you to discuss his presentation via comments on the blog.
"We use agent-based modeling to study collective problem solving in complex social networks where information aggregation and consensus building is modeled as the density classification task. We show that simple individual aggregation rules in conjunction with complex interaction patterns are highly efficient in solving the density classification task. We then investigate the effect of conservatism and partisanship on classification efficiency in large populations. We find that conservative agents enhance the populations’ ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs."
Here are the related publications:
Global Coordination in Modular Networks
Efficient system-wide coordination in noisy environments
Posted by Bernie Cahill at March 12, 2007 11:59 AM