Algorithmic price recommendations and collusion: Experimental evidence
通过实验研究算法价格建议对市场结果的合谋与竞争效应，发现不同策略的算法分别产生促进竞争或增加合谋结果的影响。
Abstract This paper investigates the collusive and competitive effects of algorithmic price recommendations on market outcomes. These recommendations are often non-binding and common in many markets. We develop a theoretical framework and derive two algorithms that recommend collusive pricing strategies. Utilizing a laboratory experiment, we find that sellers condition their prices on the recommendation of the algorithms. The algorithm with a soft punishment strategy lowers market prices and has a pro-competitive effect. The algorithm that recommends a subgame perfect equilibrium strategy increases the range of market outcomes, including more collusive ones.