Fare Game: A Mean Field Model of Stochastic Intensity Control in Dynamic Ticket Pricing

TL;DR

This paper develops a mean field game model for dynamic ticket pricing with multiple sellers, analyzing the stochastic intensity control of sales, and provides numerical methods and empirical insights into market behavior.

Contribution

It introduces a novel mean field game framework for multi-seller dynamic pricing, extending classical single-seller models to more realistic competitive settings.

Findings

Existence and uniqueness of solutions under certain conditions

Numerical algorithm for solving the coupled equations

Qualitative comparison with airfare market data

Abstract

We study the dynamic pricing of discrete goods over a finite selling horizon. One way to capture both the elastic and stochastic reaction of purchases to price is through a model where sellers control the intensity of a counting process, representing the number of sales thus far. The intensity describes the probabilistic likelihood of a sale, and is a decreasing function of the price a seller sets. A classical model for ticket pricing, which assumes a single seller and finite time horizon, is by Gallego and van Ryzin (1994) and it has been widely utilized by airlines, for instance. Extending to more realistic settings where there are multiple sellers, with finite inventories, in competition over a finite time horizon is more complicated both mathematically and computationally. We introduce a dynamic mean field game of this type, and some numerical and existence results. In particular, we analyze the associated coupled system of Hamilton-Jacobi-Bellman and Kolmogorov differential-difference equations, and we prove the existence and uniqueness results under certain conditions. Then, we demonstrate a numerical algorithm to find this solution and provide some insights into the macroeconomic market parameters. Finally, we present a qualitative comparison of our findings with airfare data.