High-Throughput Experiments on Group Dynamics
Experimental social science is moving slower and accumulating less knowledge than it could if it fully leveraged recent advances in digital technologies and crowdsourcing services. These technologies allow individual experiments to be deployed and run faster than in traditional physical labs; however, most experiments still focus on one-off results that do not generalize easily to real-world contexts, or even to other variations of the same experiment.
To achieve replicable, generalizable, scalable, and ultimately useful social science, we believe that it is necessary to rethink the fundamental “one at a time” paradigm of experimental social and behavioral science. In its place we intend to design and run “high-throughput” experiments that are radically different in scale and scope from the traditional model. This approach opens the door to new experimental insights, as well as new approaches to theory building.
Realizing the potential of high-throughput experiments in turn requires (1) significant investments in software design and participant recruitment, (2) innovations in experimental design and analysis of experimental data, (3) adoption of new models of collaboration, and (4) a new understanding of the relationship between theory and experiment. The High-Throughput Virtual Lab Project pursues this ambitious path to facilitate a new class of scientific advances in our understanding of collective social phenomena.

ABOVE: Mapping the task space
CSSLab’s research on group dynamics is generating insight into how teams collaborate, along with which factors impact their success. Rather than generalize individual conclusions to teams of different compositions working on different tasks, the lab’s high-throughput experiment design allows for more careful mapping of the task space.
Shown above is a schematic of a “task space” in which many different tasks can be mapped in relation to one another according to their defining attributes. By separating this space into distinct domains where different types of skills dominate group performance, the contextual dependencies of current theories can be precisely identified.
PEOPLE
Duncan Watts
—
Stevens University Professor & twenty-third Penn Integrates Knowledge Professor
James Houghton
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Research Scientist
Mark Whiting
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Research Scientist
Abdullah Almaatouq
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Affiliate Research Scientist
Linnea Gandhi
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Ph.D. Researcher
Dodge Hill
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Graduate Student Researcher
Sumant Shringari
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Graduate Student Researcher
Sarika Subramaniam
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Graduate Student Researcher
Joyce An-Jie Wang
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Graduate Student Researcher
Ruichen Zhang
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Graduate Student Researcher
Vikram Balasubramanian
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Undergraduate Student Researcher
Vivian Dinh
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Undergraduate Student Researcher
Henry Ge
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Undergraduate Student Researcher
Karan Sampath
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Undergraduate Student Researcher
Daniel Xue
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Undergraduate Student Researcher
Tuti Gomoka
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Research Coordinator
FEATURED PUBLICATIONS
Task complexity moderates group synergy Journal Article
In: Proceedings of the National Academy of Sciences, vol. 118, no. 36, 2021.
Scaling up experimental social, behavioral, and economic science Technical Report
2021.
My Team Will Go On: Differentiating High and Low Viability Teams through Team Interaction Journal Article
In: Proceedings of the ACM Human-Computer Interaction, vol. 4, no. CSCW3, pp. 1-27, 2021.
Empirica: a virtual lab for high-throughput macro-level experiments Journal Article
In: Behavior Research Methods, pp. 1–14, 2021.
Can We Just Start Over Again? Resetting Remote Team Dynamics Journal Article
In: 2020.
Parallel Worlds: Repeated Initializations of the Same Team To Improve Team Viability Journal Article
In: Proceedings of the ACM Human-Computer Interaction, vol. 4, no. CSCW1, pp. 22, 2020.
Did It Have To End This Way? Understanding the Consistency of Team Fracture Journal Article
In: Proceedings of the ACM Human-Computer Interaction, vol. 3, no. 209, pp. 23, 2019.
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