Designing Smart Environments to Augment Collective Learning & Creativity 

Award Abstract #: 2421387

PI: Ofer Tchernichovski, PhD

Program Manager: Soo-Siang Lim

NSF Link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2421387&HistoricalAwards=false

Abstract:

New technologies promise to augment the interactions of group members with their environment and with each other, enhancing the learning and effectiveness of work teams, community groups, classrooms, and other collectives. Already, many communities communicate via "smart" technological environments that are dynamically adapted to support shared goals. This project examines how to optimize such technological augmentations for collective intelligence and goes a step further, by investigating how to give groups, themselves, agency over the optimization process. Giving a community control over its own socio-technical learning environment could potentially allow a greater degree of effectiveness and legitimacy, but it invites new sets of challenges: How does a collective develop and learn the rules that will best structure their interactions? What are the unintended effects of granting such freedom on collective outcomes? The investigators address these questions through the development of large-scale online experiments and computational models aimed at understanding collective learning and its relation to self-governance. Data collected and analyzed will provide insight into technological mechanisms that support small-scale democratic decision-making. This research will advance science at the intersections of sociology, cognitive science, and computer science and will help prepare the workforce to work optimally and cooperatively, in future technological environments.

The research aims of this project focus on the parameters that drive group-directed learning in three areas important to groups: cooperative behavior, collective intelligence, and collective creativity. The experiments bring together hundreds of participants in computationally designed environments to understand how their interactions can be tuned to optimize group outcomes along these three dimensions. This work increases the access of scientists to complex, large-scale experimental designs and accelerate the pace of scientific research on human group behavior. Research and planning tools that empower communities to incrementally explore the rule spaces that govern their interactions will be shared with the research and other relevant communities.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.