Hyper-Personalization vs. Algorithmic Collusion: A Review and Synthesis of the Welfare Economics of AI-Driven Dynamic Pricing

Abstract

The rapid deployment of autonomous pricing software has fundamentally restructured contemporary consumer marketplaces, transforming traditional retail mechanics into high-frequency, data-driven optimization ecosystems. This paper provides a systematic, interdisciplinary review and synthesis of the expanding literature at the intersection of quantitative marketing strategy, behavioral consumer psychology, and industrial organization economics. We reconcile two historically polarized paradigms: the pro-efficiency narrative of hyper-personalized price discrimination and the anti-competitive concern of automated tacit collusion. To address a critical gap in extant literature—which predominantly treats the consumer as a passive agent and views market conditions in isolation—this paper introduces the Integrative Welfare Outcomes Framework. This novel framework maps how market concentration, data granularity, and algorithmic architecture interact to dictate when AI deployment enhances consumer surplus or devolves into structural market failure. Furthermore, we outline a transparent, replicability-focused systematic review methodology to synthesize key foundational studies, offering a comprehensive reference table for scholars. Finally, we advance actionable, specific guidance for corporate managers designing ethical-by-design algorithms and for antitrust authorities formulating modern regulatory enforcement mechanisms within AI-dominated industries.