Assortment display, price competition, and fairness in online marketplaces

Online platforms often expand their seller base to offer greater product variety and serve heterogeneous consumer preferences. However, a larger seller base can also intensify price competition among sellers and reduce platform revenue. Building on the literature on assortment reduction, we study whether platforms can mitigate price competition through a partitioned display policy, under which sellers are divided into distinct partitions, and each partition is matched to a portion of incoming customer traffic. We develop a Stackelberg game in which the platform chooses the display policy, and sellers subsequently set prices in response to the competitors they face within their assigned partition. The framework covers single-unit, finite-inventory, and infinite-inventory sellers. We characterize how the platform’s optimal display policy depends on demand and inventory. In finite-inventory settings, when demand is sufficiently high, full display is optimal because the demand-side benefit of showing the entire assortment dominates the price-competition benefit of partitioning. Under low or moderate demand, however, partitioned display can improve platform revenue by softening price competition. In addition, we develop an algorithm that effectively solves seller partitions, traffic allocation, and equilibrium prices. We also incorporate fairness constraints on seller outcomes and customer welfare, and show that partitioned display can remain revenue-improving under moderate fairness requirements. Finally, using Airbnb transaction data to calibrate a counterfactual marketplace environment, we illustrate the magnitude and drivers of the revenue–fairness trade-off under partitioned display.