An Expert Commentary on Agent Strategic Behavior in Online Marketplaces
This article explores the challenges posed by strategic agents in online marketplaces and proposes a practical matching policy to optimize performance in such environments. The problem arises when agents in online platforms, such as ridesharing and freelancing platforms, have different levels of compatibility with different types of jobs.
The conventional wisdom suggests that it is more efficient to reserve more flexible agents for jobs, as they can fulfill any task. However, this creates an incentive for agents to pretend to be more specialized in order to increase their chances of being matched with a job that suits them well. This behavior results in a loss of matches and inefficiencies in the system.
The authors of this article model the allocation of jobs to agents as a matching queue and investigate the equilibrium performance of various matching policies when agents strategically report their own types. The findings reveal that reserving flexibility without considering strategic behavior can backfire, leading to extremely poor performance compared to a policy that randomly dispatches jobs to agents.
To address this challenge and strike a balance between matching efficiency and agents’ strategic considerations, the authors propose a new policy called “flexibility reservation with fallback.” This policy takes into account both agent flexibility and specialization but also incorporates a fallback mechanism to prevent agents from manipulating their reported types dishonestly. The authors demonstrate that this policy exhibits robust performance under strategic behavior.
This research has important implications for managers and service platform operators. It highlights the need to consider agent strategic behavior when designing matching policies in online platforms. Ignoring strategic behavior can lead to significant inefficiencies and loss of matches. The proposed flexibility reservation with fallback policy offers a practical solution that is easy to implement in practice due to its parameter-free nature. It provides a robust performance guarantee while balancing the needs of both the platform and its agents.
The article also provides a real-world example of how this policy has been implemented in the driver destination product of major ridesharing platforms. This demonstrates the feasibility and effectiveness of the proposed policy in improving matching efficiency and addressing strategic behavior in online marketplaces.
In conclusion, this article makes a valuable contribution by addressing the challenges posed by agent strategic behavior in online marketplaces. The proposed flexibility reservation with fallback policy offers a practical solution to optimize matching efficiency while considering agents’ strategic considerations. It provides managers and platform operators with insights and guidelines to design effective matching policies that can improve performance in online platforms.+