Rule-Based Pricing Algorithms and Market Outcomes: An Experimental Study

2026-09-22

AI Summary

研究通过控制实验，探讨了基于规则的定价算法设计如何影响市场结果，采用警告、预设策略和大语言模型建议等方法，发现这些设计普遍提高了市场价格。

Abstract

Rule-based pricing tools are widespread in digital commerce, yet we know little about how their design shapes market outcomes. In a controlled market experiment, participants use dashboards to build pricing algorithms competing in a sequential Bertrand game over multiple periods. We vary design features commonly found in commercial repricing tools: warnings about price wars, pre-configured strategies, and advice from a large language model. Most treatment variations raise market prices with effects driven by an increase in starting prices and more cooperative algorithm designs. The results matter for competition policy, platform regulation and current discussions on regulating algorithm design tools.

Problem

• rule-based pricing
• market outcomes
• digital commerce
• algorithm design

Innovation

• Rule-based pricing
• Market experiment
• Bertrand game
• Algorithm design
• Large language model