Online Matching with Bayesian Rewards

Abstract

In This Issue Navigating Dynamic Resource Allocation: A Bayesian Approach In “Online Matching with Bayesian Rewards,” D. Simchi-Levi, R. Sun, and X. Wang address an online matching problem where a central platform must allocate limited resources to user groups arriving sequentially over time. The paper innovatively considers the variability in the reward for each matching option, which depends on both the resource type and the user’s arrival time. The challenge lies in the fact that these matching rewards are initially unknown but are assumed to be drawn from known probability distributions. The platform is then tasked with learning these true rewards in real time based on the observed matching results. This intriguing exploration of online Bayesian matching techniques provides valuable insights for improving resource allocation in dynamic environments.