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 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.

task space map
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.


Duncan Watts

Stevens University Professor & twenty-third Penn Integrates Knowledge Professor

James Houghton

James Houghton

Research Scientist

Mark Whiting

Mark Whiting

Research Scientist

Abdullah Almaatouq


Affiliate Research Scientist

Linnea Gandhi

Linnea Gandhi

Ph.D. Researcher

Andrew Cullen

Andrew Cullen Headshot

Graduate Student Researcher

Sarika Subramaniam

Sarika Subramaniam Headshot

Graduate Student Researcher

Vivian Dinh

Vivian Dinh Headshot

Undergraduate Student Researcher

Karan Sampath

Karan Sampath Headshot

Undergraduate Student Researcher

Tuti Gomoka

Research Coordinator


Almaatouq, Abdullah; Alsobay, Mohammed; Yin, Ming; Watts, Duncan J.

Task complexity moderates group synergy Journal Article

In: Proceedings of the National Academy of Sciences, 118 (36), 2021.

Abstract | Links | BibTeX

Almaatouq, Abdullah; Becker, Joshua; Bernstein, Michael S.; Botto, Robert; Bradlow, Eric T.; Damer, Ekaterina; Duckworth, Angela; Griffiths, Tom; Hartshorne, Joshua K.; Lazer, David; Law, Edith; Liu, Min; Matias, J. Nathan; Rand, David; Salganik, Matthew; Emma Satlof-Bedrick, Maurice Schweitzer; Shirado, Hirokazu; Suchow, Jordan W.; Suri, Siddharth; Tsvetkova, Milena; Watts, Duncan J.; Whiting, Mark E.; Yin., Ming

Scaling up experimental social, behavioral, and economic science Technical Report


Abstract | Links | BibTeX

Cao, Hancheng; Yang, Vivian; Chen, Victor; Lee, Yu Jin; Stone, Lydia; Diarrassouba, N'godjigui Junior; Whiting, Mark; Bernstein, Michael

My Team Will Go On: Differentiating High and Low Viability Teams through Team Interaction Journal Article

In: Proceedings of the ACM Human-Computer Interaction, 4 (CSCW3), pp. 1-27, 2021.

Abstract | Links | BibTeX

Almaatouq, Abdullah; Becker, Joshua; Houghton, James P; Paton, Nicolas; Watts, Duncan J; Whiting, Mark E

Empirica: a virtual lab for high-throughput macro-level experiments Journal Article

In: Behavior Research Methods, pp. 1–14, 2021.

Abstract | Links | BibTeX