Network performance
This work focuses on the social dynamics of knowledge exchange and learning that drive collaboration, collective intelligence, and discovery. Most problem-solving tasks are carried out in group or team settings, and require complex interactions that involve cognitive, social and informational exchange. The goal is to develop rigorous models and techniques to understand how groups reach consensus, achieve breakthroughs, and perform in groups.
Featured publications
Reconstructing higher-order interactions in coupled dynamical systems
Hyper-cores promote localization and efficient seeding in higher-order processes
Recent publications
Cash or Non-Cash? Exploring Ideators’ Incentive Preferences in Crowdsourcing Contests
Reconstructing higher-order interactions in coupled dynamical systems
Complex network effects on the robustness of graph convolutional networks
Spontaneous emergence of groups and signaling diversity in dynamic networks
Featured project
The Science of Success project focuses on developing measures and methods to model and predict success in a range of settings that have quantifiable indicators of performance (e.g., science, sports, software development). Driven by the hypothesis that success is not an individual phenomenon, but rather a collective one, we use large-scale data sets to identify patterns of career paths, individual and team performance, and the dynamics of impact and attribution. Findings offer actionable information towards a quantitative evaluation of success in a diverse range of competitive settings, from science to sports to software development.
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