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Distributed, parallel crowd workers can accomplish simple tasks throughworkflows, but teams of collaborating crowd workers are necessary for complexgoals. Unfortunately, a fundamental condition for effective teams - familiaritywith other members - stands in contrast to crowd work's flexible, on-demandnature. We enable effective crowd teams with Huddler, a system for workers toassemble familiar teams even under unpredictable availability and strict timeconstraints. Huddler utilizes a dynamic programming algorithm to optimize forhighly familiar teammates when individual availability is unknown. We firstpresent a field experiment that demonstrates the value of familiarity for crowdteams: familiar crowd teams doubled the performance of ad-hoc (unfamiliar)teams on a collaborative task. We then report a two-week field deploymentwherein Huddler enabled crowd workers to convene highly familiar teams in 18minutes on average. This research advances the goal of supporting long-term,team-based collaborations without sacrificing the flexibility of crowd work.

Huddler: Convening Stable and Familiar Crowd Teams Despite Unpredictable Availability