Price Competition in Linear Fisher Markets: Stability, Equilibrium and Personalization

Juncheng Li, Pingzhong Tang
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Abstract

Linear Fisher market is one of the most fundamental economic models. The market is traditionally examined on the basis of individual's price-taking behavior. However, this assumption breaks in markets such as online advertising and e-commerce, where several oligopolists dominate the market and are able to compete with each other via strategic actions. Motivated by this, we study the price competition among sellers in linear Fisher markets. From an algorithmic game-theoretic perspective, we establish a model to analyze behaviors of buyers and sellers that are driven by utility-maximizing purposes and also constrained by computational tractability. The main economic observation is the role played by personalization: the classic benchmark market outcome, namely competitive equilibrium, remains to be a steady-state if every buyer must be treated "equally"; however, sellers have the incentive to personalize, and as a result the market would become more unpredictable and less efficient. In addition, we build a series of algorithmic and complexity results along the road to justify our modeling choices and reveal market structures. We find interesting connections between our model and other computational problems such as stable matching, network flow, etc. We believe these results and techniques are of independent interest.
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线性费雪市场的价格竞争:稳定、均衡与个性化
线性费雪市场是最基本的经济模型之一。传统上,人们根据个人的价格行为来研究市场。然而,在网络广告和电子商务等市场中,这一假设被打破了,因为在这些市场中,几个寡头垄断者主导着市场,并能通过战略行动相互竞争。受此启发,我们研究了线性费雪市场中卖家之间的价格竞争。从算法博弈论的角度出发,我们建立了一个模型来分析买卖双方的行为,这些行为受效用最大化目的的驱动,同时也受限于计算的可操作性。主要的经济观察结果是个性化所起的作用:如果每个买方都必须 "一视同仁",那么经典的基准市场结果,即竞争性均衡,仍然是一个稳态;然而,卖方有个性化的动机,因此市场会变得更加不可预测,效率也会降低。此外,我们还沿路建立了一系列算法和复杂性结果,以证明我们的建模选择是正确的,并揭示市场结构。我们发现了我们的模型与其他计算问题(如稳定匹配、网络流等)之间的有趣联系。我们相信这些结果和技术具有独立的意义。
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