2016/10/26 by Niloufar Salehi, Salehi, Niloufar, Andrew McCabe +6
Computer Science · Decision Sciences · Psychology · #FOS: Computer and information sciences #H.5.3 #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Multi-Agent Systems and Negotiation #Peer-to-Peer Network Technologies #Personal Information Management and User Behavior #Team Dynamics and Performance
paper · pdf · doi:10.48550/arxiv.1610.08216
openalex publication_date 2016/10/26 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Distributed, parallel crowd workers can accomplish simple tasks through\nworkflows, but teams of collaborating crowd workers are necessary for complex\ngoals. Unfortunately, a fundamental condition for effective teams - familiarity\nwith other members - stands in contrast to crowd work's flexible, on-demand\nnature. We enable effective crowd teams with Huddler, a system for workers to\nassemble familiar teams even under unpredictable availability and strict time\nconstraints. Huddler utilizes a dynamic programming algorithm to optimize for\nhighly familiar teammates when individual availability is unknown. We first\npresent a field experiment that demonstrates the value of familiarity for crowd\nteams: familiar crowd teams doubled the performance of ad-hoc (unfamiliar)\nteams on a collaborative task. We then report a two-week field deployment\nwherein Huddler enabled crowd workers to convene highly familiar teams in 18\nminutes on average. This research advances the goal of supporting long-term,\nteam-based collaborations without sacrificing the flexibility of crowd work.\n