Regulatory Learning or Regulatory Privilege? How Sandbox Design Shapes the Governance Value of Experimental Regulation

Abstract

Regulatory sandboxes are controlled, time‐limited environments in which selected innovations are tested under regulatory supervision. This article asks how sandbox design can make selective experimentation capable of generating public regulatory value rather than regulatory privilege. It develops regulatory learning asymmetry to distinguish private, supervisory, and public regulatory learning and unequal distributions of access, information, learning, and benefits. A structured reading of public documents from five deliberately diverse FinTech and artificial intelligence cases illustrates a six‐element operational framework. The case material is not used to infer private or supervisory learning; it records only publication, generalization mechanisms, and documented uptake. The framework identifies a public‐learning configuration, a supervisory‐learning configuration that public documents cannot verify, and a regulatory‐privilege risk configuration. Market signaling is treated as a cross‐cutting dynamic. The article offers a bounded diagnostic for assessing whether case‐specific experience becomes contestable and institutionally usable knowledge.