ALGORITHMIC BARGAINING: A DYNAMIC PRICING MODEL FOR PRODUCTS AND SERVICES ACROSS ONLINE PLATFORMS

Summary: This article presents a comprehensive study of algorithmic bargaining within dynamic pricing systems, focusing on its usability, challenges, and broader market implications. Algorithmic bargaining, increasingly used in industries such as e-commerce, retail, and transportation, allows companies to dynamically adjust prices based on real-time data, consumer behavior, and market conditions. However, its widespread adoption raises significant questions regarding fairness, transparency, and regulatory oversight. Drawing on studies from fields like game theory, behavioral economics, and data science, this research explores the impacts of algorithmic bargaining on both businesses and consumers. While machine learning and deep reinforcement learning technologies enhance pricing efficiency, they also present risks of consumer harm and market manipulation. This paper critically examines the ethical implications, regulatory responses, and potential consequences for competition and consumer welfare. Through an analysis of existing literature, including case studies from various industries, this study provides a balanced evaluation of the benefits and drawbacks of algorithmic bargaining, encouraging a deeper understanding of its multifaceted role in modern pricing strategies.