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We study the dynamics of the Naming Game (Baronchelli et al. in J Stat Mech Theory Exp P06014, 2006b) in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.  相似文献   
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Is there is a warming trend in the earth's climate caused by an increase in concentrations of greenhouse gases in the upper atmosphere, it may be sensible to try to slow down that process by reducing emissions of greenhouse gases and, in particular, the emissions of carbon dioxide produced by the energy sector of world economies. For a number of reasons, a consensus on such reductions is difficult to reach. In this article, we model the problem as a dynamic game with national governments, or coalitions of such governments, as players. Clearly, the negotiations on worldwide reductions in CO2 emissions can succeed only if there exists a cooperative solution superior to the noncooperative one. According to our model, the existence of a collectively preferable cooperative solution depends on the degree of concern among national governments about negative impacts of increased CO2 concentrations. In addition to this unsurprising conclusion, the model can provide insights as to whose concerns will count most for the success of the negotiations and who will have to be induced by side payments to participate.  相似文献   
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