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An online labor platform faces an online learning problem in matching workerswith jobs and using the performance on these jobs to create better futurematches. This learning problem is complicated by the rise of complex tasks onthese platforms, such as web development and product design, that require ateam of workers to complete. The success of a job is now a function of theskills and contributions of all workers involved, which may be unknown to boththe platform and the client who posted the job. These team matchings result ina structured correlation between what is known about the individuals and thisinformation can be utilized to create better future matches. We analyze twonatural settings where the performance of a team is dictated by its strongestand its weakest member, respectively. We find that both problems pose anexploration-exploitation tradeoff between learning the performance of untestedteams and repeating previously tested teams that resulted in a goodperformance. We establish fundamental regret bounds and design near-optimalalgorithms that uncover several insights into these tradeoffs.

Exploration vs. Exploitation in Team Formation