| dc.description.abstract | 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. | en_US |