Algorithms for Hiring and Outsourcing in the Online Labor Market

• URL: http://arxiv.org/abs/2002.07618v1
• Date: Sun, 16 Feb 2020 18:56:26 GMT
• Title: Algorithms for Hiring and Outsourcing in the Online Labor Market
• Authors: Aris Anagnostopoulos and Carlos Castillo and Adriano Fazzone and Stefano Leonardi and Evimaria Terzi
• Abstract summary: We provide algorithms for outsourcing and hiring workers in a general setting. We call this model team formation with outsourcing. Our contribution is an efficient online cost-minimizing algorithm for hiring and firing team members and outsourcing tasks.
• Score: 12.893230873578878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although freelancing work has grown substantially in recent years, in part facilitated by a number of online labor marketplaces, (e.g., Guru, Freelancer, Amazon Mechanical Turk), traditional forms of "in-sourcing" work continue being the dominant form of employment. This means that, at least for the time being, freelancing and salaried employment will continue to co-exist. In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task. We call this model team formation with outsourcing. In our model, tasks arrive in an online fashion: neither the number nor the composition of the tasks is known a-priori. At any point in time, there is a team of hired workers who receive a fixed salary independently of the work they perform. This team is dynamic: new members can be hired and existing members can be fired, at some cost. Additionally, some parts of the arriving tasks can be outsourced and thus completed by non-team members, at a premium. Our contribution is an efficient online cost-minimizing algorithm for hiring and firing team members and outsourcing tasks. We present theoretical bounds obtained using a primal-dual scheme proving that our algorithms have a logarithmic competitive approximation ratio. We complement these results with experiments using semi-synthetic datasets based on actual task requirements and worker skills from three large online labor marketplaces.

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