论文标题
竞争性需求学习：一种不合作定价算法，具有协调的价格实验
Competitive Demand Learning: A Non-cooperative Pricing Algorithm with Coordinated Price Experimentation
论文作者
论文摘要
我们考虑了在T期限的计划范围内为多个公司的定期均衡定价问题。在每个时期，公司都设定了销售价格并收到消费者的随机需求。公司不知道其潜在的需求曲线，但他们希望确定售价以最大程度地提高竞争总收入。因此，他们必须进行一些价格实验，以使观察到的需求数据有用，以做出价格决策。但是，不协调的价格更新可以使价格实验收集的需求信息少或不准确。我们设计了一种非参数学习算法来促进协调的动态定价，其中竞争性公司根据观察结果估算其需求功能，并以规定的方式调整其定价策略。我们表明，根据估计需求函数决定的定价决策会随着时间的流逝而融合到基本平衡。我们获得的收入差异的界限为O（f^2 t^3/4），而对竞争性公司F和T的数量则具有O（f t^1/2）的遗憾。我们还开发了一种经过修改的算法来处理某些公司可能了解需求曲线的情况。
We consider a periodical equilibrium pricing problem for multiple firms over a planning horizon of T periods. At each period, firms set their selling prices and receive stochastic demand from consumers. Firms do not know their underlying demand curve, but they wish to determine the selling prices to maximize total revenue under competition. Hence, they have to do some price experiments such that the observed demand data are informative to make price decisions. However, uncoordinated price updating can render the demand information gathered by price experimentation less informative or inaccurate. We design a nonparametric learning algorithm to facilitate coordinated dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. We show that the pricing decisions, determined by estimated demand functions, converge to underlying equilibrium as time progresses. We obtain a bound of the revenue difference that has an order of O(F^2 T^3/4) and a regret bound that has an order of O(F T^1/2) with respect to the number of the competitive firms F and T . We also develop a modified algorithm to handle the situation where some firms may have the knowledge of the demand curve.
