This paper develops a comprehensive theoretical framework for analyzing quality dynamics in oligopsony markets where numerous producers compete for limited consumer attention under information asymmetries. Motivated by artificial intelligence marketplaces and similar digital platforms, we establish existence and uniqueness conditions for optimal quality provision by heterogeneous producers and prove that posterior reputation estimates converge to accurate quality assessments at rate Formula: see text. Our analysis demonstrates precision-weighted aggregation optimality and establishes manipulation resistance properties for well-designed information systems. We further prove that consumer loyalty and brand value mechanisms exhibit synergistic quality enhancement effects exceeding simple additive impacts, leading to endogenous market segmentation into premium, specialist, and commodity producer categories when feedback effects are sufficiently strong. The regulatory analysis characterizes welfare-maximizing policy designs and demonstrates strategic complementarity between market-based mechanisms and direct intervention. Our results provide actionable guidance for platform design, showing how integrated governance approaches combining reputation systems, loyalty mechanisms, and appropriately calibrated regulation can enhance market efficiency and quality provision in digital platform environments.
Rao et al. (2026) studied this question.