算法建议能成为市场协调的隐性信号，影响人类竞争行为
Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets
- 用个性化算法建议引导参与者向均衡产量收敛
- 偏向下调产量的建议导致持续低产出和超额利润
- 适合关注算法治理与市场设计的研究者
随着算法越来越多地介入竞争性决策，其影响已超越个体结果，延伸至塑造市场策略动态。实验中，129名参与者在古诺产量竞争游戏中接受与均衡一致或具有策略偏差的算法建议。个性化均衡建议促进稳定收敛，而偏向合谋的向下建议则导致持续低产出和超竞争性利润，表现出默许共谋特征。相比集体均衡建议，个性化建议使参与者的产量更快、更一致地趋近目标，可能源于客观优势或更强归属感。结果表明，算法建议可作为战略信号，即使无直接沟通也能促成协调。研究呼应了现实世界中算法合谋的担忧，强调需审慎设计和监管竞争环境中的算法辅助系统。
原文摘要 · Abstract (English)
As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market dynamics. In our experiment, we examined how algorithmic advice affects human behavior in a classic economic game with a unique, non-collusive, and analytically traceable equilibrium. Participants (N = 129) played a Cournot quantity competition with equilibrium-aligned or strategically biased algorithmic recommendations. While individualized equilibrium advice supported stable convergence, collusively downward-biased advice led to sustained underproduction and supracompetitive profits - hallmarks of tacit collusion. Participants' quantities converged faster and more consistently toward individualized than collective equilibrium advice, potentially due to an objective quality advantage or greater perceived ownership of the former. These findings demonstrate that algorithmic advice can function as a strategic signal, shaping coordination even without explicit communication. The results echo real-world concerns about algorithmic collusion and underscore the need for careful design and oversight of algorithmic decision-support systems in competitive environments.
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