Advancing High‐Seas Fisheries Governance: Bayesian Models for Enhancing Compliance and Sustainability Under the <scp>BBNJ</scp> Agreement

ABSTRACT High‐seas fisheries governance faces significant challenges due to transboundary complexities, enforcement limitations, and scientific uncertainties. The recently adopted Agreement on the Conservation and Sustainable Use of Marine Biological Diversity of Areas Beyond National Jurisdiction (BBNJ Agreement) represents a pivotal legal framework aimed at strengthening regulatory mechanisms. This study explores how Bayesian methodologies can enhance high‐seas fisheries governance by improving stock assessments, quota allocations, and compliance monitoring. Bayesian inference offers a probabilistic approach to decision‐making, integrating prior knowledge with real‐time data to optimize policy predictability and adaptive management. Through a case study approach, this research assesses the practical applicability of Bayesian models under the BBNJ framework, highlighting their potential to address legal and enforcement gaps while ensuring sustainability. The findings contribute to both legal and fisheries management scholarship, offering data‐driven recommendations for policymakers to enhance science‐based governance in areas beyond national jurisdiction.