Where Will They Click Next? A Social Foraging Model for Collaborating Teams

Modern knowledge work is increasingly collaborative, especially in information-intensive domains such as crisis response, scientific discovery, and software engineering. Software engineering epitomizes these trends through practices like pair programming and collaborative debugging. Yet existing computational models of information foraging remain individual-centric, leaving teams without support for social foraging—leveraging partners’ actions and communication to navigate complex projects. We introduce PFIS-T, a predictive computational model of social information foraging. Building on the PFIS model family, it integrates implicit cues from teammates’ recent navigation and explicit cues from synchronous communication to predict a programmer’s next action. We evaluated PFIS-T with ten three-person debugging teams, finding that it substantially outperforms the strongest individual baseline, PFIS3, predicting 81.5% of navigations and improving accuracy by 16.7%. These results show how predictive models can operationalize social foraging and point to opportunities for collaborative IDEs and interactive systems that adaptively surface social trails to improve coordination and awareness.