Abstract

This review article delves into the new and growing field of dynamic pricing models for AI agentic use—autonomous AI agents capable of independent decision-making. As applications of such agents are picking up speed in industries such as e-commerce, transport, and finance, the need for dynamic pricing solutions has gained strength significantly. This paper covers recent developments in dynamic pricing algorithms for AI agents extensively, examining their theoretical models, their applications, and their performance metrics. This paper discusses reinforcement learning algorithms, multi-agent pricing models, and context-aware price models, and address the ethical issues and regulatory hurdles such systems pose. This research uncovers that while AI-driven dynamic price models insist on dazzling improvements in terms of revenue maximization and market efficiency, they raise vital questions about fairness, transparence, and customer trust as well. This comprehensive review is a valuable benchmark for researchers and practitioners alike with an interest in discovering and extending the state of the art of AI-driven dynamic pricing systems.

References

• Chen, L., Mislove, A., & Wilson, C. (2021). "An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace." Proceedings of the 2021 ACM Web Conference, 672-683.
• Zhao, H., Yang, J., & Li, X. (2022). "Reinforcement Learning for Dynamic Pricing in E-Commerce: A Survey." IEEE Transactions on Knowledge and Data Engineering, 34(5), 2094-2112.
• S. Agarwal, S. Kumar, P. Chilakapati, and S. Abhichandani, "Artificial Intelligence in Data Governance Enhancing Security and Compliance in Enterprise Environments," Nanotechnology Perceptions, vol. 20, no. 1, pp. 34–45, 2024.
• P. Chilakapati, "Leveraging Generative AI in Digital Transformation: Real-World Applications Beyond Chatbots," International Journal of Innovative Research in Science, Engineering and Technology, vol. 13, no. 12, pp. 20791–20796, Dec. 2024.
• Johnson, T., Larson, K., & Taylor, M. (2022). "Multi-Agent Reinforcement Learning for Dynamic Pricing in Competitive Markets." Journal of Artificial Intelligence Research, 74, 1623-1659.
• Rodriguez, M., & Thompson, C. (2021). "Fairness Considerations in Algorithmic Pricing: A Critical Review." Journal of Business Ethics, 169(4), 587-601.
• Garcia, D., Smith, A., & Patel, R. (2022). "Regulatory Approaches to Algorithmic Pricing: Balancing Innovation and Consumer Protection." Harvard Journal of Law & Technology, 35(2), 429-481.
• Zhang, H., Li, Y., & Wang, F. (2022). "Deep Q-Networks for E-commerce Pricing: Performance Analysis in Volatile Markets." IEEE Transactions on Neural Networks and Learning Systems, 33(7), 3128-3142.
• Brown, T., Martinez, L., & Roberts, J. (2021). "Policy Gradient Methods for Dynamic Pricing in Ride-Sharing Platforms." Transportation Research Part C: Emerging Technologies, 128, 103215.
• Nguyen, V., & Harris, S. (2023). "Contextual Bandits for Flash Sales Pricing: A Thompson Sampling Approach." Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1935-1944.
• Calvano, E., Calzolari, G., & Denicolò, V. (2020). "Artificial Intelligence, Algorithmic Pricing and Collusion." American Economic Review, 110(10), 3267-3297.
• S. Seth, P. Chilakapati, R. Prathikantam, and A. Jangili, "AI-Powered Customer Segmentation and Targeting: Predicting Customer Behaviour for Strategic Impact," International Journal of Data Mining & Knowledge Management Process (IJDKP), vol. 15, no. 1, pp. 31–45, Jan. 2025.
• Kim, J., Zhang, L., & Patel, N. (2022). "Deep Learning with Attention Mechanisms for Ride-Sharing Demand Prediction." Transportation Research Part C: Emerging Technologies, 131, 103328.
• Davidson, A., & Roberts, M. (2021). "Causal Machine Learning for Demand Estimation: Methods and Empirical Evaluation." Management Science, 67(9), 5832-5850.
• Huang, Y., Taylor, P., & Wang, Z. (2023). "Context-Aware Dynamic Pricing: The Impact of Incorporating Temporal and Geographic Features." Journal of Retailing, 99(2), 267-282.