主講人：李雨青助理教授
主講人來自：清華大學工業工程與管理學系
題目：Competitive Demand Learning: a Data-Driven Pricing Algorithm
摘要：Dynamic pricing is used to maximize the revenue of a firm over a finite-period planning horizon, given that the firm may not know the underlying demand curve” a priori”. In emerging markets, in particular, firms constantly adjust pricing strategies to collect adequate demand information, which is a process known as price experimentation. To date, few papers have investigated the pricing decision process in a competitive environment with unknown demand curves, conditions that make analysis more complex. Asynchronous price updating can render the demand information gathered by price experimentation less informative or inaccurate, as it is nearly impossible for firms to remain informed about the latest prices set by competitors. Hence, firms may set prices using available, yet out-of-date, price information of competitors. In this paper, we design an algorithm to facilitate synchronized dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. The process is called” learning and earning” elsewhere in the literature. The goal is for the pricing decisions, determined by estimated demand functions, to converge to underlying equilibrium decisions. The main question that we answer is whether such a mechanism of periodically synchronized price updates is optimal for all firms. Furthermore, we ask whether prices converge to a stable state and how much regret firms incur by employing such a data-driven pricing algorithm.
(假設企業可能不知道潛在的需求曲線“先驗”，則可以使用動態定價來最大化企業在有限週期計劃範圍內的收入。特別是在新興市場中，公司會不斷調整定價策略以收集足夠的需求信息，這一過程稱為價格試驗。迄今為止，幾乎沒有論文研究了在競爭環境中需求曲線未知的定價決策過程，這種條件使分析變得更加複雜。異步價格更新可能會使通過價格試驗收集的需求信息的信息量不足或不准確，因為企業幾乎不可能隨時了解競爭對手設定的最新價格。因此，企業可以使用競爭對手的可用但過時的價格信息來設置價格。在本文中，我們設計了一種算法來促進同步動態定價，在這種算法中，競爭企業根據觀察結果估計其需求函數，並以規定的方式調整其定價策略。該過程在文獻中的其他地方稱為“學習和賺錢”。目標是為了由估算的需求函數確定的定價決策可以收斂到基本的均衡決策。我們要回答的主要問題是，這種定期同步的價格更新機制是否為最適合所有公司。此外，我們詢問價格是否收斂到穩定狀態以及企業產生多少遺憾通過採用這種數據驅動的定價算法。)
時間：2021-4-13 15:50~16:40 (星期二) / 地點：M212
