About the Journal

Annals of Decision Analytics (ADA) is an international, peer-reviewed academic journal dedicated to publishing original, innovative, and high-quality research in decision analytics, decision sciences, operations research, management science, business analytics, data science, artificial intelligence, machine learning, and data-driven decision-making. The journal advances knowledge by integrating theoretical developments, methodological innovations, computational methods, and practical applications within a comprehensive interdisciplinary framework.

ADA provides a scholarly platform for research combining statistical and mathematical modeling, optimization, simulation, machine learning, computational intelligence, and organizational and behavioral perspectives to address complex decision problems. The journal welcomes contributions in business analytics, healthcare analytics, predictive and prescriptive analytics, neural networks, deep learning, reinforcement learning, and related areas of intelligent decision support.

By encouraging research that connects decision theory, statistical science, operations research, artificial intelligence, computational methods, and real-world decision problems, ADA seeks to contribute to both the advancement of academic knowledge and the improvement of decision-making practice across organizations, industries, healthcare systems, governments, and society.

All submissions must be written in English. The journal follows a continuous online publication model, under which accepted manuscripts are published online after the completion of the peer-review, editorial, and production processes. Articles published during a calendar year are subsequently compiled into a single annual volume.

 

Aims and Scope

The primary aim of Annals of Decision Analytics is to provide a high-quality academic forum for innovative research at the intersection of decision sciences, statistics, analytics, applied mathematics, operations research, artificial intelligence, machine learning, and data-driven decision support.

The journal seeks to deepen the theoretical understanding of decision processes
while advancing analytical, computational, and intelligent methods for addressing complex organizational and societal problems.

The journal encourages studies grounded in sound theoretical foundations, rigorous methodological development, reproducible computational analysis, and meaningful practical implications. Research that develops decision models, proposes analytical frameworks, introduces statistical or algorithmic innovations, constructs intelligent decision-support systems, or applies advanced computational approaches to consequential decision problems is particularly encouraged.

ADA welcomes diverse research designs and methodological approaches, including quantitative, statistical, computational, optimization-based, simulation-based, experimental, data-driven, and mixed-method studies. The journal publishes several types of scholarly contributions, including:

  • Original theoretical and empirical research articles
  • Conceptual and foundational papers
  • Methodological, statistical, computational, and algorithmic studies
  • Machine learning and artificial intelligence studies
  • Optimization and simulation-based decision-support studies
  • Business, healthcare, and public-policy analytics studies
  • Case studies involving important real-world decision problems
  • Literature reviews, systematic reviews, and meta-analyses
  • Reproducibility studies, benchmark studies, and comparative evaluations
  • Perspectives on emerging directions in decision analytics


The journal particularly encourages contributions that integrate quantitative decision models with statistics, data science, machine learning, artificial intelligence, behavioral insights, systems thinking, and organizational decision processes. All submissions should demonstrate originality, analytical rigor, methodological transparency, and a clear contribution to the decision analytics
literature or its applications.

 

Focus and Scope

The scope of Annals of Decision Analytics encompasses a broad range of theoretical, methodological, computational, and applied topics related to decision analytics, decision sciences, operations research, management science, business analytics, healthcare analytics, data science, statistics, and intelligent decision-making.

The journal welcomes research addressing the modeling, analysis, prediction, optimization, and solution of complex decision problems across different sectors, systems, and institutional settings. Particular emphasis is placed on emerging and future-oriented research, including AI-driven decision systems, generative artificial intelligence, explainable AI, human-centered analytics, real-time analytics, digital twins, autonomous and intelligent systems, augmented decision-making, responsible AI, algorithmic ethics, sustainable decision systems, and next- generation decision-support technologies.


Areas of interest include, but are not limited to:

  • Foundations of decision theory and decision analytics
  • Statistical decision theory and Bayesian decision analysis
  • Business analytics and data-driven management
  • Healthcare, medical, clinical, and public-health analytics
  • Predictive, prescriptive, and descriptive analytics
  • Causal analytics and policy evaluation
  • Multi-criteria and multi-objective decision analysis
  • Optimization under uncertainty
  • Stochastic, robust, and distributionally robust optimization
  • Simulation, Monte Carlo methods, and simulation-based decision support
  • Machine learning and artificial intelligence for decision-making
  • Statistical learning and high-dimensional data analysis
  • Neural networks and deep-learning methods
  • Reinforcement learning and sequential decision-making
  • Supervised, unsupervised, semi-supervised, and self-supervised learning
  • Generative artificial intelligence and foundation models
  • Explainable, interpretable, trustworthy, and responsible AI
  • Computational intelligence and evolutionary algorithms
  • Ensemble learning, hybrid intelligent systems, and metaheuristics
  • Natural language processing and text analytics
  • Time-series analysis, forecasting, and predictive modeling
  • Behavioral and cognitive decision analytics
  • Human–AI collaboration and augmented decision-making
  • Risk analysis, reliability, resilience, and uncertainty quantification
  • Information-theoretic methods and model selection
  • Data integration, data mining, big-data analytics, and decision-support systems
  • Real-time, streaming, and adaptive analytics
  • Digital twins, cyber-physical systems, and intelligent automation
  • Operations research, management science, and supply-chain analytics
  • Financial, marketing, customer, and human-resource analytics
  • Transportation, logistics, energy, and environmental analytics
  • Manufacturing, quality, maintenance, and service-system analytics
  • Public-sector, policy, security, and crisis-management analytics
  • Sustainable development and responsible decision analytics
  • Autonomous systems, robotics, and intelligent control
  • Privacy-preserving analytics, fairness, accountability, and algorithmic ethics
  • Software, platforms, and computational tools for decision analytics


Applications are welcomed in areas such as management, business, finance, marketing, healthcare, medicine, public health, engineering, manufacturing, supply chains, transportation, logistics, energy, environmental systems, education, public policy, government, social sciences, and information systems.

Interdisciplinary studies connecting decision analytics with management, economics, engineering, computer science, statistics, mathematics, psychology, medicine, public health, public policy, and information systems are strongly encouraged, provided that they make a substantive contribution to the understanding, modeling, prediction, optimization, or improvement of decision-making processes.