Catalyzing collaborations: Prescribed interactions at conferences determine team formation

TL;DR

This study demonstrates that prescribed interactions at conferences can predict and enhance future scientific collaborations, supported by a novel dataset, empirical findings, and a mathematical model.

Contribution

It introduces a new longitudinal dataset, shows the impact of prescribed interactions on collaboration likelihood, and proposes a model for team formation based on these interactions.

Findings

Participants who formed new collaborations interacted 63% more.

Higher interaction scenarios increased collaboration odds by over eight times.

The proposed model accurately predicts collaboration formation across conferences.

Abstract

Collaboration plays a key role in knowledge production. Here, we show that patterns of interaction during conferences can be used to predict who will subsequently form a new collaboration, even when interaction is prescribed rather than freely chosen. We introduce a novel longitudinal dataset tracking patterns of interaction among hundreds of scientists during multi-day conferences encompassing different scientific fields over the span of 5 years. We find that participants who formed new collaborations interacted 63% more on average than those who chose not to form new teams, and that those assigned to a higher interaction scenario had more than an eightfold increase in their odds of collaborating. We propose a simple mathematical framework for the process of team formation that incorporates this observation as well as the effect of memory beyond interaction time. The model accurately reproduces the collaborations formed across all conferences and outperforms seven other candidate models. This work not only suggests that encounters between individuals at conferences play an important role in shaping the future of science, but that these encounters can be designed to better catalyze collaborations.