AI-Enabled Personalized Pricing: Efficiency, Rent Extraction, and the Conditions for a New Market Equilibrium

DOI

10.2991/978-94-6239-825-2_23How to use a DOI?

Keywords

personalized pricing; dynamic pricing; information asymmetry; consumer welfare; reinforcement learning; algorithmic collusion

Abstract

Artificial intelligence and behavioral data make personalized pricing increasingly scalable, but its welfare effects depend on more than predictive accuracy. This conceptual paper develops an integrated framework linking economic surplus, information asymmetry, algorithmic decision-making, and consumer behavior. It also provides formal criteria that distinguish personalized pricing from ordinary dynamic pricing by data input, objective, decision unit, mechanism, and counterfactual test. Evidence from economic models, field and online experiments, marketplace data, and computational studies shows that personalization can expand participation and lower prices for some consumers, while also transferring surplus, reducing trust, and encouraging strategic data concealment. Reinforcement-learning agents may sustain supra-competitive prices through repeated feedback, although exploration rules and market structure can also produce competitive outcomes. Amazon is retained only as a historical illustration, not as proof of current individualized pricing. The paper concludes that efficiency is most plausible when personalization expands output, markets remain contestable, data use is transparent, and consumers retain meaningful choice.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zijing Xu
PY  - 2026
DA  - 2026/11/10
TI  - AI-Enabled Personalized Pricing: Efficiency, Rent Extraction, and the Conditions for a New Market Equilibrium
BT  - Proceedings of the 2026 5th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2026)
PB  - Atlantis Press
SP  - 230
EP  - 236
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-825-2_23
DO  - 10.2991/978-94-6239-825-2_23
ID  - Xu2026
ER  -