Decision Intelligence and Complex Computing (DICC) is an international, peer-reviewed, open-access journal dedicated to research at the intersection of decision intelligence, complex systems, and advanced computational methods. The journal aims to advance how complex decisions are formulated, modelled, analysed, supported, and evaluated in environments characterized by uncertainty, multiple objectives, interconnected processes, dynamic behaviour, and large or heterogeneous data.

The journal is founded on the view that many contemporary decision problems cannot be adequately addressed by isolated analytical or computational techniques. Effective decision intelligence increasingly requires the integration of mathematical and computational models, artificial intelligence, optimization, data analytics, domain knowledge, and human judgment. DICC therefore provides a multidisciplinary platform for research that connects methodological innovation with the structure and complexity of real-world decision problems.

The journal welcomes theoretical, methodological, computational, empirical, and application-oriented research that contributes to the development of more intelligent, robust, transparent, explainable, and effective decision processes. Relevant contributions may originate from decision science, operations research, artificial intelligence, computational intelligence, computer science, engineering, economics, finance, management science, environmental science, and other disciplines concerned with complex decision-making.

Particular emphasis is placed on research addressing complexity rather than computational sophistication alone. This includes decision problems involving uncertainty and risk, multiple or conflicting objectives, interacting decision-makers, incomplete or imperfect information, nonlinear or dynamic systems, large-scale computational structures, competing stakeholder interests, and interconnected technological, economic, environmental, or social processes.

The scope of DICC includes, but is not limited to:

• decision science, decision theory, and decision support systems;
• decision intelligence and intelligent decision support;
• multi-criteria and multi-objective decision-making;
• operations research, optimization, and mathematical modelling;
• artificial intelligence and machine learning for decision-making;
• computational intelligence and advanced computational methods;
• predictive, prescriptive, and decision analytics;
• simulation, forecasting, scenario analysis, and computational experimentation;
• complex systems, systems modelling, and networked decision environments;
• decision-making under uncertainty, risk, and incomplete information;
• robustness, resilience, sensitivity, and uncertainty analysis;
• behavioral and human-centered decision-making;
• group, collaborative, and multi-agent decision-making;
• explainable, transparent, responsible, and trustworthy decision models;
• data-driven and knowledge-driven decision systems;
• intelligent and autonomous decision systems;
• business, management, and strategic decision-making;
• finance and investment decision-making;
• engineering, manufacturing, logistics, and supply-chain decisions;
• energy, sustainability, environmental, and climate decision-making;
• infrastructure and smart-system decision-making;
• healthcare and public-policy decision-making.

Interdisciplinary studies connecting several of these areas are particularly encouraged.

DICC encourages research that moves beyond the routine application of established algorithms, models, or computational tools. Manuscripts are expected to provide a clearly identifiable scientific contribution through methodological advancement, theoretical development, computational innovation, rigorous empirical evidence, new decision insight, or a meaningful integration of methods, data, and disciplines.

Methodological and computational contributions should demonstrate their relevance to complex decision-making through appropriate theoretical analysis, benchmarking, experimentation, simulation, or substantive application. The introduction of a new algorithm or computational technique should be accompanied by a clear explanation of its decision-making relevance and scientific contribution.

Application-oriented and empirical studies are welcome when they provide more than a routine implementation of established methods. Such studies should generate transferable methodological knowledge, substantive empirical findings, or decision insights that extend beyond a single organization, dataset, application, or case.

Through this focus, Decision Intelligence and Complex Computing (DICC) seeks to establish a distinctive international forum connecting intelligent analysis, computational complexity, and decision action. Its objective is not only to advance increasingly powerful analytical and computational methods, but also to deepen the scientific understanding of how these methods can contribute to better, more robust, transparent, and effective decisions in complex real-world systems.