Stochastic optimization for dynamic pricing

TL;DR

This paper applies stochastic gradient methods to optimize dynamic pricing in online marketplaces by modeling consumer demand and supplier behavior, aiming for efficient convergence to market equilibrium.

Contribution

It introduces a stochastic optimization approach tailored for dynamic pricing, incorporating market participant responses to achieve fast convergence.

Findings

Efficient algorithms for pricing in online markets.

Modeling consumer and supplier responses enhances convergence.

Convex optimization framework enables practical implementation.

Abstract

We consider the problem of supply and demand balancing that is stated as a minimization problem for the total expected revenue function describing the behavior of both consumers and suppliers. In the considered market model we assume that consumers follow the discrete choice demand model, while suppliers are equipped with some quantity adjustment costs. The resulting optimization problem is smooth and convex making it amenable for application of efficient optimization algorithms with the aim of automatically setting prices for online marketplaces. We propose to use stochastic gradient methods to solve the above problem. We interpret the stochastic oracle as a response to the behavior of a random market participant, consumer or supplier. This allows us to interpret the considered algorithms and describe a suitable behavior of consumers and suppliers that leads to fast convergence to the equilibrium in a close to the real marketplace environment.