A computational framework for analyzing dynamic auctions: The market impact of information sharing
开发了一个计算框架来分析动态拍卖，并研究了竞拍者之间信息共享的影响。研究发现，信息共享在动态环境中可能不会降低福利，甚至对策略性数据共享提供了支持。
Abstract This article develops a computational framework to analyze dynamic auctions and uses it to investigate the impact of information sharing among bidders. We show that allowing for the dynamics implicit in many auction environments enables the emergence of equilibrium states that can only be reached when firms are responding to dynamic incentives. The impact of information sharing depends on the extent of dynamics and provides support for the claim that information sharing, even of strategically important data, need not be welfare reducing. Our methodological contribution is to show how to adapt the experience‐based equilibrium concept to a dynamic auction environment and to provide an implementable boundary‐consistency condition that mitigates the extent of multiple equilibria.