arXiv:2606.11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting. In each period, a customer arrives seeking service, and the platform chooses an assortment of sellers to display. The customer then proposes a transaction to at most one seller in the assortment according to a multinomial logit choice model. After a fixed number of periods, sellers review the proposals they have received and each chooses at most one customer according to another multinomial logi
This research addresses the growing complexity of online platforms and the need for more sophisticated AI-driven decision-making to optimize two-sided markets.
Advanced algorithms for dynamic assortment can significantly improve efficiency and profitability for online platforms by better matching supply and demand, impacting market dynamics.
Platforms can move towards more adaptive and intelligent curation of offerings, learning and responding in real-time to user behavior on both sides of a marketplace.
- · Large online marketplaces
- · E-commerce platforms
- · AI researchers in reinforcement learning
- · Consumers (through better matches)
- · Platforms with static assortment strategies
- · Inefficient manual curation processes
Online platforms will adopt more sophisticated AI models for presenting choices to users and optimizing transactions.
This could lead to increased market concentration among platforms that effectively leverage such learning algorithms to dominate their niches.
These dynamic assortment models might eventually extend beyond consumer goods to labor markets or even financial product allocations, leading to highly optimized but potentially less transparent selection processes.
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Read at arXiv cs.LG