Motivated by the setting of Airbnb, we consider a game theoretical setup in which each seller on the platform provides a single-unit product and competes on price. We investigate sellers' optimal pricing decisions and the platform's optimal assortment display policy. Additionally, we incorporate constraints to guarantee a certain degree of seller and customer fairness. Using data from Airbnb, we present a case study to illustrate how our model framework can be applied in practice.

We study the relative merits of different dispute resolution mechanisms in two-sided platforms, in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution.

We consider an on-demand service platform (e.g., Uber, Lyft, DiDi) that serves two customer classes with distinct willingness to wait and to pay. We formulate this problem as a Markov decision process in which the platform controls customer admission and service provider allocation. Using our model's structural properties, we show that the optimal policy is characterized by two admission thresholds. In a numerical study, we then compare the optimal policy with several simpler policies.