The value of adaptive menu sizes in peer-to-peer platforms. (December 2022) - Karabulut, Ezgi; Gholizadeh, Fatemeh; Akhavan-Tabatabaei, Raha
Abstract: We consider a peer-to-peer logistics system, where the agents and the platform communicate in both directions in a multi-period setting. The goal of the platform is to select a subset of the requests to offer to each agent that would result in a maximum valued matching for the platform. The platform utilizes the agent's choice to learn about her preferences and improve the estimated decision mechanism of the agent. Through systematic experimentation, we first establish that at the earlier iterations and while the decision mechanism of the agent is not yet accurately estimated, a larger menu size could benefit the platform's overall gain better. However, after the estimates become more accurate through the learning process, a smaller menu size can further increase the overall benefits. Highlights: We consider a peer-to-peer platform where agents are matched with requests. The agent's utility functions are unknown and learned through their choices. We study the tradeoff between learning rate and platform benefit. Adaptive menu size strategy is shown to perform better than fixed menu sizes.