Career: computational approaches to emergent intelligence in human and AI teams

The National Science Foundation's Office of Multidisciplinary Activities awarded the University of California, San Diego $908,571 on October 1, 2026, under the Social, Behavioral, and Economic Sciences program (CFDA 47.075) to develop and test computational models of team coordination and collective decision-making in human and artificial agent systems.

The research examines how individuals self-organize into different team structures under controlled conditions and evaluates the performance of alternative coordination strategies across tasks. The project uses computational modeling and reinforcement learning approaches to predict effective team structures and to compare human behavior with multi-agent reinforcement learning systems. This comparative framework will distinguish the role of task demands from higher-order cognitive processes that contribute to coordination and decision-making in human teams. The results will inform the design of real-world teams and the development of artificial agents capable of functioning effectively in team settings.

The award, designated a CAREER project grant, covers the period October 1, 2026, through September 30, 2031, with place of performance in Princeton, New Jersey. No sub-awards are planned.

Abstract

Intelligent behavior often emerges through coordination among individuals working together to solve problems and make decisions. This project develops and tests computational models of how teams organize, coordinate action, and distribute responsibilities across different experimental conditions. The proposed research will advance understanding of collective decision-making and coordination in human and artificial systems. The results will inform the design of real-world teams and the development of artificial agents capable of functioning effectively in team settings. This project investigates the cognitive and computational mechanisms by which humans and artificial agent teams organize to solve problems and make decisions. The research examines how individuals self-organize into different team structures under controlled conditions and evaluates the performance of alternative coordination strategies across tasks. Computational modeling and reinforcement learning approaches will be used to predict effective team structures and to compare human behavior with multi-agent reinforcement learning systems?artificial agents with minimal cognitive abilities. This comparative framework will help distinguish the role of task demands from higher-order cognitive processes that contribute to coordination and decision-making in human teams. 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.- subawards are not planned for this award.