Algorithms Artificial Intelligence And Simple Rule Based Pricing
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Algorithms, Artificial Intelligence and Simple Rule Based Pricing
Automated pricing comes in two forms - rule-based (e.g., targeting or undercutting the lowest price, etc) and artificial intelligence (AI) powered algorithms (e.g., reinforcement learning (RL) based). While rule-based pricing is the most widely used automated pricing strategy today, many retailers have increasingly adopting pricing algorithms powered by AI. Q-learning algorithm (a specific type of RL algorithm) is particularly appealing for pricing because it autonomously learns an optimal pricing policy and can adapt to any evolution in competitors' pricing strategy and market environment. It is commonly believed that the Q-learning algorithm has a significant advantage over simple rule-based pricing algorithms; therefore, in a competitive environment, most firms should adopt Q-learning based pricing algorithms if their competitors are using such algorithms. However, through extensive pricing experiments in a workhorse oligopoly model of repeated price competition, we show that a firm's best response to its competitor's Q-learning based algorithms is to use simple rule-based pricing algorithms. We find that when a Q-learning algorithm competes against a rule-based pricing algorithm, higher prices are sustained in the market in comparison to when multiple Q-learning algorithms compete against each other. The high prices are sustained because the rule-based algorithm introduces stationarity into the repeated price competition, which allows the Q-learning algorithm to more effectively search for the optimal policy benefiting both sellers. Further, the experimental phase where the Q-learning algorithm learns the optimal pricing policy is significantly shorter when it competes against a rule-based pricing algorithm in comparison to when it competes against another Q-learning algorithm. Our results are robust to alternative modeling assumptions on market structure, algorithm type, number of players, etc.
Elgar Encyclopedia of Pricing
Author: Andreas Hinterhuber
language: en
Publisher: Edward Elgar Publishing
Release Date: 2024-07-05
The Elgar Encyclopedia of Pricing presents a holistic view of cutting-edge topics, practical insights, and global perspectives on pricing. In-depth entries cover everything from behavioral pricing and artificial intelligence to sustainability pricing strategies and dynamic online pricing.
Data-Driven Software Engineering and Surge Pricing Algorithms Explained
Author: Abhijeet Bajaj Dr. Shilpa Choudhary
language: en
Publisher: DeepMisti Publication
Release Date: 2025-01-01
In the era of data-centric innovation, software engineering has undergone a paradigm shift, evolving into a discipline where insights derived from data drive critical decisions, performance enhancements, and algorithmic precision. This book, Data-Driven Software Engineering and Surge Pricing Algorithms Explained, explores this transformative intersection of software engineering and real-time data analytics, focusing on the intricate mechanisms behind surge pricing algorithms and their profound impact across industries. Through meticulously curated chapters, we delve into the methodologies, tools, and frameworks that underpin modern software systems designed to adapt to fluctuating demands. From foundational discussions on data acquisition and preprocessing to advanced topics like predictive modeling, algorithm optimization, and real-time decision-making, the book offers a holistic perspective. Moreover, we examine the ethical and technical challenges of surge pricing algorithms, fostering a balanced view of their advantages and potential societal implications. This book is designed to cater to a wide audience, including software engineers, data scientists, students, and business leaders keen on leveraging the power of data to solve complex problems. Each chapter combines theoretical insights with practical examples, ensuring that readers can readily apply the concepts discussed to real-world scenarios. The integration of case studies and hands-on exercises further enriches the learning experience, making this book a practical guide for both beginners and seasoned professionals. The inspiration for this book stems from the growing importance of adaptive, data-driven systems in today’s fast-paced digital economy. We are deeply indebted to the many innovators, researchers, and practitioners who have contributed to the fields of software engineering and algorithm design. Their work has paved the way for the development of scalable, intelligent systems that redefine what technology can achieve. It is our sincere belief that the insights shared in these pages will empower readers to not only understand but also contribute to the evolution of data-driven technologies, fostering solutions that are efficient, equitable, and impactful in the digital age. Thank you for joining us on this intellectual journey. Authors