publications
publications by categories in reversed chronological order. generated by jekyll-scholar.
2023
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The Effects of Group Composition and Dynamics on Collective PerformanceAbdullah Almaatouq, Mohammed Alsobay, Ming Yin, and Duncan J WattsTop. Cogn. Sci., Nov 2023As organizations gravitate to group-based structures, the problem of improving performance through judicious selection of group members has preoccupied scientists and managers alike. However, which individual attributes best predict group performance remains poorly understood. Here, we describe a preregistered experiment in which we simultaneously manipulated four widely studied attributes of group compositions: skill level, skill diversity, social perceptiveness, and cognitive style diversity. We find that while the average skill level of group members, skill diversity, and social perceptiveness are significant predictors of group performance, skill level dominates all other factors combined. Additionally, we explore the relationship between patterns of collaborative behavior and performance outcomes and find that any potential gains in solution quality from additional communication between the group members are outweighed by the overhead time cost, leading to lower overall efficiency. However, groups exhibiting more “turn-taking” behavior are considerably faster and thus more efficient. Finally, contrary to our expectation, we find that group compositional factors (i.e., skill level and social perceptiveness) are not associated with the amount of communication between group members nor turn-taking dynamics.
2021
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Task complexity moderates group synergyAbdullah Almaatouq, Mohammed Alsobay, Ming Yin, and Duncan J WattsProc. Natl. Acad. Sci. U. S. A., Sep 2021Complexity-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.
