异构语言模型市场中的算法合谋研究
Who Leads and Who Collects:Algorithmic Collusion in Markets of Heterogeneous Language Models
四种主流模型定价行为对比，发现Gemini市场合谋性最强，GPT无法促成市场收敛。
研究显示四种语言模型在伯川德市场中表现出不同程度的合谋行为。Claude和Gemini市场达到垄断租金的72-79%，DeepSeek市场为24%，GPT市场则无合谋。混合市场不会自动减少合谋，包含Gemini的市场比同质市场更具合谋性，包含Claude的市场合谋性较低。租金分配呈现DeepSeek > Claude > Gemini > GPT的反向顺序，符合价格领导模型预测。
Who Leads and Who Collects:Algorithmic Collusion in Markets of Heterogeneous Language Models
Evidence that pricing algorithms collude comes from markets in which every seller runs the same algorithm. We ask what happens when they do not. Four language models from four providers, each at its cheapest tier, price in a four-firm logit Bertrand market without communication, in every homogeneous, two-by-two and fully mixed composi- tion (11 cells, 20 runs, 200 periods). Collusion is a property of the model: Claude and Gemini markets reach 72 to 79 percent of the monopoly rent, DeepSeek markets 24 percent, and GPT markets none, though GPT prices drift above the monopoly level rather than toward competition. Mixing does not reduce collusion by itself. Markets containing Gemini, which opens at the highest price and settles highest, are more col- lusive than the homogeneous markets they are built from; markets containing Claude, which opens lower and follows its rivals down, are less so; and only the fully mixed market is significantly less collusive than the average homogeneous one. Stability de- pends on the least stable participant: two GPT firms suffice to keep any market from converging. Inside mixed markets the rent is shared in a transitive order, DeepSeek over Claude over Gemini over GPT, that inverts the anchor ranking. The model that raises the price collects the least of the rent, as the price-leadership model predicts.
