social bonding; network formation; social behaviour; rational strategies
Scholtes Ingo, Mavrodiev Pavlin, Schweitzer Frank (2016), From Aristotle to Ringelmann: a large-scale analysis of team productivity and coordination in Open Source Software projects, in Empirical Software Engineering
, 21(2), 642-683.
Scholtes Ingo, Wider Nicolas, Garas Antonios (2016), Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities, in The European Physical Journal B
, 89(3), 1-15.
Scholtes Ingo, Wider Nicolas, Pfitzner René, Garas Antonios, Tessone Claudio J., Schweitzer Frank (2014), Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, in Nature Communications
, 5, 5024-5024.
Auclair Yannick, König Barbara, Ferrari Manuela, Perony Nicolas, Lindholm Anna K. (2014), Nest attendance of lactating females in a wild house mouse population: Benefits associated with communal nesting, in Animal Behaviour
Sarigol Emre, Pfitzner Rene, Scholtes Ingo, Garas Antonios, Schweitzer Frank (2014), Predicting Scientific Success Based on Coauthorship Networks, in EPJ Data Science
, 3, 9.
Garcia David, Mavrodiev Pavlin, Schweitzer Frank (2013), Social resilience in online communities the autopsy of friendster, in Proceedings of the 1st ACM Conference in Online Social Networks (COSN'13)
, Boston, Massachusetts, USAACM, New York, NY, USA.
Zanetti Marcelo Serrano, Scholtes Ingo, Tessone Claudio Juan, Schweitzer Frank (2013), The rise and fall of a central contributor: Dynamics of social organization and performance in the GENTOO community, in 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE)
, San Francisco, CA, USAIEEE, USA.
Perony Nicolas, Tessone Claudio J, König Barbara, Schweitzer Frank (2012), How random is social behaviour? Disentangling social complexity through the study of a wild house mouse population., in PLOS computational biology
, 8(11), 1002786-1002786.
In this interdisciplinary project we aim at studying the incentives ofsocial interaction and social bond formation by combining the analysis ofa unique long-term data set on social interactions in an animal societywith formal models of network formation in economic and social systems.Social interactions play a crucial role in the lives of many organisms,including humans. Yet we hardly understand why individuals establishsocial bonds, nor the way in which such interactions influence thecomplex social networks on the level of a larger group or population.We intend to make a major contribution towards the scientificunderstanding of social network formation, by revealing the hiddenprinciples underlying the strategic decisions of their individual actors.To fulfil this goal, we will undertake an interdisciplinary approachcombining knowledge, data and methods from both economics and animalbehaviour.As a model of social organisation, we will use a long-term data set onsocial interactions in a population of free-living mice in a barn nearZurich. We follow individually marked mice from cradle to grave oremigration out of the barn, continuously document all socialinteractions, and analyse genetic relatedness and offspring production.The latter allow us to use lifetime reproductive success as a measure foreach individual's biological fitness or success. We will use that dataset to test general hypotheses about the strategic interaction of agentson a microscopic, i.e. individual-based, level, to explain the observedstructure of social networks on a macroscopic, i.e. systemic, level. Someof these hypotheses will be tested by experimental manipulation of thesocial network (removal of individuals) in the study population of housemice.By integrating animal social behaviour and social network analysis, ourinnovative project will contribute to reveal the importance ofestablishing individual social bonds in terms of long-term benefitsgained by the individuals. The results will bring new insights into thelinks between the individual effort invested in social bonding, theresulting individual payoff, the position and role of agents in a networkand the overall network structure.