Aims & Scope

The journal seeks to propel research and application at the convergence of machine learning, decision science, and real-world decision-making. It offers a forum for rigorous scholarship that explores the embedding of state-of-the-art ML models into policy design, strategic planning, risk assessment, resource allocation, and data-driven governance frameworks. The journal invites theoretical, empirical, and practice-focused studies that deepen understanding and accelerate technological innovation in intelligent decision systems across global, high-stakes environments.

Single-blinded Peer Review

Committed to scientific integrity and editorial excellence, Machine Learning & Decision Making employs a single-blinded peer-review process and adheres to the highest ethical standards as outlined by COPE. The journal embraces transparency, reproducibility, and accessibility, ensuring that all published content is freely available under an open-access model to maximize dissemination and global impact.

ISSN

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Abstracting & Indexing

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