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Autonomous Decision Systems for Dynamic Pricing: A Comprehensive Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 22 |
| Year of Publication: 2025 |
| Authors: Shailendra Shrivastava |
10.5120/ijca2025925303
|
Shailendra Shrivastava . Autonomous Decision Systems for Dynamic Pricing: A Comprehensive Review. International Journal of Computer Applications. 187, 22 ( Jul 2025), 14-19. DOI=10.5120/ijca2025925303
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
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