If You Love Your Agents, Set Them Free: Task Discretion in Online Marketplaces

In manufacturing and service operations, flexibility is beneficial for matching supply with demand, but it comes at a cost. In modern digital workplaces, users/agents possess a spectrum of different skills associated with corresponding and variable task preferences: Some are inclined to give up part of their payment to avoid unfavorable matches or prioritize the preferred ones. The platform manager, in turn, gains extra freedom in allocating tasks by possibly charging servers for a favorable assignment. Innovative marketplaces facilitate task discretion and the development of novel implementations and the analysis of resulting benefits are important parts of the platform design. This naturally leads to the problem of exploring and optimizing the task allocation process. We introduce an innovative mechanism for task assignment in the workplace, and we compare it against the traditional one where task routing is solely the platform's decision. In order to improve all users' welfare, agents are allowed some task discretion in exchange for a fee. We model different working environments and different servers' preferences via different distributions, and we study how the agent's preferences, the task cost, and the flexibility fee, affect the equilibrium assignment. In a single-server system, the platform benefits when the agent's preferences are not aligned with its own. In a multi-server system, a server may request autonomy and choose the costly-to-the-platform option, depending on the behavior of other agents. In this case, it may or may not be beneficial for the platform when the agents' preferences are misaligned to its own. In every case, by pricing and offering flexibility the platform can do at least as well as the no-flexibility scheme. An important conclusion is that pricing discretion in task assignments can often improve the agents' welfare as well as the labor platform's profit.