High-Throughput Experiments on Group Dynamics

@article{almaatouq2021task,

title = {Task complexity moderates group synergy},

author = {Almaatouq, Abdullah and Alsobay, Mohammed and Yin, Ming and Watts, Duncan J.},

url = {https://www.pnas.org/content/118/36/e2101062118},

doi = {10.1073/pnas.2101062118},

year  = {2021},

date = {2021-09-07},

journal = {Proceedings of the National Academy of Sciences},

volume = {118},

number = {36},

abstract = {Complexity—defined in terms of the number of components and the nature of the interdependencies between them—is clearly a relevant feature of all tasks that groups perform. Yet the role that task complexity plays in determining group performance remains poorly understood, in part because no clear language exists to express complexity in a way that allows for straightforward comparisons across tasks. Here we avoid this analytical difficulty by identifying a class of tasks for which complexity can be varied systematically while keeping all other elements of the task unchanged. We then test the effects of task complexity in a preregistered two-phase experiment in which 1,200 individuals were evaluated on a series of tasks of varying complexity (phase 1) and then randomly assigned to solve similar tasks either in interacting groups or as independent individuals (phase 2). We find that interacting groups are as fast as the fastest individual and more efficient than the most efficient individual for complex tasks but not for simpler ones. Leveraging our highly granular digital data, we define and precisely measure group process losses and synergistic gains and show that the balance between the two switches signs at intermediate values of task complexity. Finally, we find that interacting groups generate more solutions more rapidly and explore the solution space more broadly than independent problem solvers, finding higher-quality solutions than all but the highest-scoring individuals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cao2021my,

title = {My Team Will Go On: Differentiating High and Low Viability Teams through Team Interaction},

author = {Hancheng Cao and Vivian Yang and Victor Chen and Yu Jin Lee and Lydia Stone and N'godjigui Junior Diarrassouba and Mark Whiting and Michael Bernstein},

url = {https://arxiv.org/pdf/2010.07292},

doi = {10.1145/nnnnnnn.nnnnnnn},

year  = {2021},

date = {2021-01-05},

journal = {Proceedings of the ACM Human-Computer Interaction},

volume = {4},

number = {CSCW3},

pages = {1-27},

abstract = {Understanding team viability — a team’s capacity for sustained and future success — is essential for building effective teams. In this study, we aggregate features drawn from the organizational behavior literature to train a viability classification model over a dataset of 669 10-minute text conversations of online teams. We train classifiers to identify teams at the top decile (most viable teams), 50th percentile (above a median split), and bottom decile (least viable teams), then characterize the attributes of teams at each of these viability levels. We find that a lasso regression model achieves an accuracy of .74–.92 AUC ROC under different thresholds of classifying viability scores. From these models, we identify the use of exclusive language such as ‘but’ and ‘except’, and the use of second person pronouns, as the most predictive features for detecting the most viable teams, suggesting that active engagement with others’ ideas is a crucial signal of a viable team. Only a small fraction of the 10-minute discussion, as little as 70 seconds, is required for predicting the viability of team interaction. This work suggests opportunities for teams to assess, track, and visualize their own viability in real time as they collaborate.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{almaatouq2021empirica,

title = {Empirica: a virtual lab for high-throughput macro-level experiments},

author = {Abdullah Almaatouq and Joshua Becker and James P Houghton and Nicolas Paton and Duncan J Watts and Mark E Whiting},

url = {https://drive.google.com/file/d/1asyd8onp_2df10W1AbzPOyar6WknEiCU/view},

doi = {10.3758/s13428-020-01535-9},

year  = {2021},

date = {2021-01-01},

journal = {Behavior Research Methods},

pages = {1--14},

publisher = {Springer},

abstract = {Virtual labs allow researchers to design high-throughput and macro-level experiments that are not feasible in traditional in-person physical lab settings. Despite the increasing popularity of online research, researchers still face many technical and logistical barriers when designing and deploying virtual lab experiments. While several platforms exist to facilitate the development of virtual lab experiments, they typically present researchers with a stark trade-off between usability and functionality. We introduce Empirica: a modular virtual lab that offers a solution to the usability--functionality trade-off by employing a ``flexible defaults'' design strategy. This strategy enables us to maintain complete ``build anything'' flexibility while offering a development platform that is accessible to novice programmers. Empirica's architecture is designed to allow for parameterizable experimental designs, reusable protocols, and rapid development. These features will increase the accessibility of virtual lab experiments, remove barriers to innovation in experiment design, and enable rapid progress in the understanding of human behavior.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}