Read our latest paper titled Social Dynamics of Science in Nature Scientific Reports. Authors Xiaoling Sun, Jasleen Kaur, Staša Milojević, Alessandro Flammini & Filippo Menczer ask, How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.
Tag Archives: agent-based modeling
Competition among memes in a world with limited attention
In our paper on Competition among memes in a world with limited attention in Nature Scientific Reports, Lilian Weng and coauthors Sandro Flammini, Alex Vespignani, and Fil Menczer report that we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas. The findings have been mentioned in the popular press, including Information Week, The Atlantic, and the Dutch daily NRC.
Paper on Web traffic modeling presented at WAW 2010
On December 16, Mark Meiss presented our paper “Modeling Traffic on the Web Graph” (with Bruno, José, Sandro, and Fil) at the 7th Workshop on Algorithms and Models for the Web Graph (WAW 2010), at Stanford. In this paper we introduce an agent-based model that explains many statistical features of aggregate and individual Web traffic data through realistic elements such as bookmarks, tabbed browsing, and topical interests.