Aims and scope

Applied Decision Analytics is an international, peer-reviewed journal that serves as a platform for advancing decision sciences at the intersection of data science, artificial intelligence, and applied mathematics. The journal seeks to publish cutting-edge research that not only develops novel decision methodologies but also demonstrates their effectiveness in solving complex, high-impact real-world problems.

Aims

The primary aims of the journal are to:

• Provide a forum for theory-driven and data-driven decision analytics, emphasizing the integration of mathematical models with modern data science techniques.

• Advance the next generation of decision support systems that combine big data analytics, machine learning, optimization, and simulation for actionable insights.

• Encourage research that addresses uncertainty, vagueness, and dynamic complexity through approaches such as fuzzy sets, probabilistic reasoning, rough sets, and hybrid soft computing.

• Promote cross-disciplinary collaboration among operations research, computer science, statistics, management, engineering, and social sciences.

• Highlight the societal and managerial impact of decision analytics in areas such as healthcare, energy, sustainability, climate change, supply chain resilience, smart cities, and digital transformation.

Scope

The scope of Applied Decision Analytics covers both methodological advances and application-driven studies. Contributions may include but are not limited to:

• Decision Models and Frameworks: Multi-criteria decision-making (MCDM), multi-objective optimization, Bayesian decision theory, stochastic and dynamic programming, and game-theoretic models.

• Data Science in Decision Analytics: Integration of machine learning, deep learning, natural language processing, and network analysis for decision support and predictive analytics.

• Uncertainty and Risk: Modeling imprecision, ambiguity, and incomplete information through fuzzy systems, intuitionistic fuzzy sets, probabilistic graphical models, and scenario analysis.

• Human-Centered and Cognitive Decision Analytics: Behavioral decision-making, explainable AI in decision processes, and human–machine collaboration in analytics.

• Applications: Real-world decision problems in healthcare systems, energy management, finance and banking, logistics and transportation, environmental management, policy analysis, digital epidemiology, and Industry 4.0.

• Unique Contributions:

  • Combining algorithmic advances in data science with decision theory to move beyond descriptive analytics toward prescriptive and cognitive analytics.

  • Bridging the gap between predictive machine learning models and decision optimization frameworks.

  • Demonstrating scalability of decision analytics in big data and real-time environments, with emphasis on computational efficiency and interpretability.

Positioning

What makes Applied Decision Analytics unique is its commitment to showcasing research that not only advances methodology but also provides demonstrable practical value through case studies, decision-support tools, and data-driven applications. The journal positions itself at the frontier of decision intelligence, where data science meets decision theory to inform complex choices under uncertainty.