Emily Hu

@xehu.bsky.social

306 Followers 179 Following

MIT researcher • 💻 I study teams, communication, & computational social science • 🎓 Stanford, Wharton X: @xemilyhu

An Illustration of the Task Space. Left: Our labeled set of tasks can be thought of as a 102 tasks × 24 dimensional matrix in which each task is represented as a row vector for which each element (column) is a dimension of the task. Right: Each row vector in this matrix can also be mapped to a point in 24-dimensional space. This representation makes the Task Space easily amenable to linear algebra-based analysis (e.g., finding similarity between vectors, clustering, and sampling).
A figure illustrating Heterogeneity in Group Advantage Across Conditions. Each point represents the observed group advantage at the level of an experimental condition, which is a tuple of task (y-axis) × level of complexity (color/line thickness; low complexity is represented by a thin green line, medium complexity by a mid-thickness purple line, and high complexity by a thick orange line) × group size (point shape; small, three-person groups are represented by circles and large, six-person groups are represented by crosses). Tasks are grouped by the experimental wave in which they appeared (top three facets). Error bars represent the analytical 95% confidence intervals (1.96 * 1 standard error) for group advantage in a given condition. Boxes are centered around the mean and show one standard error.
A screenshot of the paper's title and abstract in the journal Management Science. Title of paper: "The Task Space: An Integrative Framework for Team Research." Abstract: Research on teams spans many contexts, but integrating knowledge from heterogeneous sources is challenging because studies typically examine different tasks that cannot be directly compared. Most investigations involve teams working on just one or a handful of tasks, and researchers lack principled ways to quantify how similar or different these tasks are from one another. We address this challenge by introducing the “Task Space,” a multidimensional space in which tasks—and the distances between them—can be represented formally, and use it to create a “Task Map” of 102 crowd-annotated tasks from the published experimental literature. We then demonstrate the Task Space’s utility by performing an integrative experiment that addresses a fundamental question in team research: when do interacting groups outperform individuals? Our experiment samples 20 diverse tasks from the Task Map at three complexity levels and recruits 1,231 participants to work either individually or in groups of three or six (180 experimental conditions). We find striking heterogeneity in group advantage, with groups performing anywhere from three times worse to 60% better than the best individual working alone, depending on the task context. Critically, the Task Space makes this heterogeneity predictable: it significantly outperforms traditional typologies in predicting group advantage on unseen tasks. Our models also reveal theoretically meaningful interactions between task features; for example, group advantage on creative tasks depends on whether the answers are objectively verifiable. We conclude by arguing that the Task Space enables researchers to integrate findings across different experiments, thereby building cumulative knowledge about team performance.