Quantifying success in individual and team careers in intellectual domains

Le résumé fourni par la source

Human progress is driven by actions of successful individuals. For this reason, quantifying patterns and habits of success in major human endeavors is a key challenge in computational social science. Yet, its quantification is often challenging due to its subjective nature, and the lack of fine-grained data about human performance.The recent availability of large scale datasets in a variety of social contexts-- from Science to Businesses and Sports-- presents an opportunity for success to be rigorously quantified.The first part of this thesis explores temporal patterns of success in individual careers. First, I unveil how social network and collaboration structure determines funding success of scientists in academia.Furthering our data-driven approaches to sports, I delve into the evolution of chess careers, tracking the career trajectories of successful chess players, and their temporal evolution. Beyond single individuals, I then move on characterizing success in teams. Focusing on publication data, I extract persistent collaborations and extend the investigation of scientific careers from single scientists to team trajectories.I investigate how persistent collaborations emergence and eventually dissolve, and which compositional factors make some teams more successful than others. Lastly, I explore the spread of ideas and innovations through social contagion modeling, highlighting the importance of group contagion and temporal persistence in shaping population-level spread of ideas. By quantifying patterns success across careers in different domains, this thesis contributes to the evolving discourse on successful careers and their correlates.

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