Learning to Hire Teams
By Adish Kumar Singla, Eric Horvitz, Pushmeet Kohli, Andreas Krause. 2015
14 citations recorded by the source corpus.
Crowdsourcing and human computation are being employed in sophisticated projects that require the solution of a heterogeneous set of tasks. We explore the challenge of composing or hiring an effective team from an available pool of applicants for performing tasks required for such projects on an ongoing basis. How can one optimally spend budget to learn the expertise of workers as part of recruiting a team? How can one exploit the similarities among tasks as well as underlying social ties or commonalities among the workers for faster learning? We tackle these decision-theoretic challenges by casting them as an instance of online learning for best action selection with side-observations. We present algorithms with PAC bounds on the required budget to hire a near-optimal team with high confidence. We evaluate our methodology on simulated problem instances using crowdsourcing data collected from the Upwork platform.
Subfields: Computer Networks and Communications, Computer Science Applications, Management Science and Operations Research, Game & Decision Theory
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In Computer Science Applications
In Management Science and Operations Research
Citations in the corpus
- GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, background): “There is potential for such models to contribute to economic decision-making at the task level, for instance, by refining methods for task and sub-task allocation between humans and machines (Singla et al., 2015; Shahaf and Horvitz, 2010).”