Decoding Algorithmic Pricing: A Bibliometric Journey Through Fairness, Resource Optimization, and Ai -Driven Innovation in Artificial Intelligence

The advent of artificial intelligence (AI) has significantly reshaped the landscape of market dynamics, with algorithmic pricing emerging as one of the most critical areas influenced by this transformation. The integration of AI-driven algorithms into pricing strategies has profound implications for both market efficiency and regulatory frameworks. In order to better understand the research trends and developments in this domain, a comprehensive bibliometric analysis was conducted. This analysis aimed to map the intellectual structure of the field of algorithmic pricing by identifying key researchers, universities, and countries contributing to the development of this field. Through the use of VOSviewer, four distinct yet interconnected research clusters emerged, shedding light on the various thematic dimensions of algorithmic pricing: (1) Algorithmic Pricing and Fairness, (2) Resource Allocation and Management, (3) Game Theory and Stackelberg Models, and (4) Demand Response and Electric Vehicle Integration. This abstract delves into each of these clusters, examining their contributions to the broader discourse on AI in pricing strategies and resource management.

Decoding Algorithmic Pricing: A Bibliometric Journey Through Fairness, Resource Optimization, and Ai -Driven Innovation in Artificial Intelligence Keywords:

Algorithmic Pricing , Resource Allocation , Fairness , Game Theory , Demand Response Systems