強化學習研究垂直差異化之訂價策略：以ChatGPT為例;A Study of Pricing Strategies in Vertical Differentiation Using Reinforcement Learning: A Case Study of ChatGPT

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Title:	強化學習研究垂直差異化之訂價策略：以ChatGPT為例;A Study of Pricing Strategies in Vertical Differentiation Using Reinforcement Learning: A Case Study of ChatGPT
Authors:	李孟儒;Lee, Meng-Ju
Contributors:	資訊管理學系
Keywords:	資訊安全;訂價策略;垂直差異化;強化學習
Date:	2024-07-15
Issue Date:	2024-10-09 16:54:19 (UTC+8)
Publisher:	國立中央大學
Abstract:	隨著AI聊天機器人的快速發展，特別是ChatGPT，其在企業應用中的安全性成為了一個重要議題。本研究旨在探討如何利用強化學習技術，來制定ChatGPT產品的最佳訂價策略，以實現企業利潤最大化。研究主要分析了網路效應係數、資訊洩漏風險係數以及服務成本對ChatGPT Enterprise（企業版）和ChatGPT+（進階版）兩種產品的需求和利潤的影響。ChatGPT Enterprise版本因不使用輸入資訊進行模型訓練，因此在資訊安全上有優勢。本研究的結果為ChatGPT公司在制定產品訂價策略上提供了決策依據。首先，我們建立了使用者對兩種產品的效用函數模型，考慮了產品價值、價格、資訊洩漏風險和網路效應等因素。這些模型幫助我們理解消費者在不同條件下的選擇行為。我們分別討論了選擇企業版和進階版的消費者比例。接下來，我們設計了一系列實驗，利用Q-learning演算法模擬不同參數條件下的最佳訂價策略。綜合實驗結果，我們提出了以下結論：在高網路效應環境下，應集中推廣進階版服務，以增加進階版需求；在高資訊洩漏風險情境下，應強化企業版的安全性優勢，出乎意料的是此時應適度調降其訂價，來增加企業版的使用需求；服務成本因素對於企業利潤極為敏感，因此應做好適度的成本控管。 ;With the rapid development of AI chatbots, particularly ChatGPT, security in enterprise applications has become a significant issue. This study aims to explore how to use reinforcement learning techniques to formulate the optimal pricing strategy for ChatGPT products to maximize corporate profits. The research primarily analyzes the impact of the network effect coefficient, information leakage risk coefficient, and service cost on the demand and profits of ChatGPT Enterprise and ChatGPT+. The ChatGPT Enterprise version, which does not use input information for model training, has an advantage in information security. The results of this study provide a decision-making basis for ChatGPT company in formulating product pricing strategies. First, we established utility function models for users of the two products, considering factors such as product value, price, information leakage risk, and network effects. These models help us understand consumer choices under different conditions. We discussed the proportion of consumers choosing the enterprise version and the advanced version separately. Next, we designed a series of experiments, utilizing the Q-learning algorithm to simulate the optimal pricing strategy under different parameter conditions. Based on the comprehensive experimental results, we propose the following conclusions: in a high network effect environment, the focus should be on promoting the advanced version to increase its demand; in a high information leakage risk scenario, the security advantages of the Enterprise version should be enhanced, and surprisingly, its pricing should be moderately reduced to increase usage demand; the factor of service costs is extremely sensitive to corporate profits, so appropriate cost control measures should be implemented.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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