Optimal Auction Design for Dynamic Stochastic Environments: Myerson Meets Naor

TL;DR

This paper develops a dynamic auction mechanism for stochastic markets like cloud computing and blockchain, balancing efficiency and control through value-dependent admission and reserve prices.

Contribution

It introduces a novel dynamic auction framework that combines queue-based admission control with reserve pricing, extending classical auction theory to stochastic, time-varying environments.

Findings

Optimal policy pairs high-value allocation with dynamic admission thresholds.

Mechanism ensures dominant strategy implementation via dynamic reserve prices.

Balances efficiency and market stability in stochastic supply-demand settings.

Abstract

Motivated by applications such as cloud computing, gig platforms, and blockchain auctions, we study optimal selling mechanisms for dynamic markets with stochastic supply and demand. In our model, buyers with private valuations and homogeneous goods arrive stochastically and can be held in queues at a cost. The optimal mechanism pairs allocative efficiency with dynamic admission control: goods are assigned to the highest-value buyer, while entry is restricted by value thresholds that strictly increase with the queue length and decrease with available inventory. This policy smooths competitive pressure across time and is implemented in dominant strategies via auctions with dynamic reserve prices.