Journal of Information-Based Decision Making (JIBDM) is an international, peer-reviewed journal devoted to advancing the understanding of how information is acquired, represented, evaluated, transformed, and used to support decisions.

The journal is founded on the premise that the quality of a decision depends not only on the analytical method used, but also on the quality, relevance, uncertainty, interpretation, and use of the information on which the decision is based. JIBDM therefore focuses on the relationship between information and decision making, including the processes through which data become information, information becomes decision-relevant evidence or knowledge, and such evidence is translated into decisions and outcomes.

The journal provides an interdisciplinary forum connecting decision sciences, information systems, operations research, management science, artificial intelligence, data science, statistics, behavioral decision research, economics, engineering, and related disciplines. Contributions from these fields are welcomed when they provide a substantive contribution to understanding or improving information-based decision processes.

Core Scope

JIBDM welcomes theoretical, methodological, computational, empirical, and applied research addressing one or more stages of the information-to-decision process, including:

• information acquisition, selection, representation, aggregation, and interpretation;
• information quality, relevance, reliability, completeness, timeliness, and credibility;
• decision making under incomplete, uncertain, imprecise, conflicting, or dynamically changing information;
• decision theory, decision analysis, and decision quality;
• multi-criteria and multi-objective decision making;
• preference modeling, elicitation, aggregation, and learning;
• evidence-based and data-informed decision making;
• information fusion and knowledge integration for decision support;
• uncertainty, risk, robustness, sensitivity, and scenario analysis;
• optimization, simulation, forecasting, and prescriptive analytics for decision making;
• decision support systems and intelligent decision-support environments;
• artificial intelligence and machine learning for decision support;
• explainable, interpretable, transparent, and trustworthy decision models;
• human–AI interaction and human–AI collaborative decision making;
• behavioral and cognitive aspects of information use in decisions;
• information overload, selective information use, and decision biases;
• group, collaborative, organizational, and distributed decision making;
• dynamic, sequential, adaptive, and real-time decision processes;
• decision monitoring, feedback, learning, and evaluation of decision outcomes.

Information-Based Decision Making in the Age of AI

A particular area of interest for JIBDM is the changing role of information in decision making in environments increasingly influenced by artificial intelligence and automated analytical systems.

The journal encourages research examining how human decision makers interact with algorithmically generated information, predictions, recommendations, explanations, and autonomous or semi-autonomous decision systems. Relevant issues include trust in AI-supported decisions, explainability, uncertainty communication, human oversight, accountability, algorithmic bias, information provenance, and the appropriate allocation of decision authority between humans and intelligent systems.

The journal is interested not only in whether an AI or analytical model achieves high predictive performance, but also in whether and how its output produces better-informed, more transparent, robust, accountable, and effective decisions.

From Information to Decision

A distinguishing objective of JIBDM is to encourage research that examines the complete or partial chain between information and decision outcomes.

Research may investigate questions such as:

• What information is actually relevant to a decision?
• How should information from heterogeneous or conflicting sources be combined?
• How does information quality affect decision quality?
• How should uncertainty and ambiguity be represented and communicated?
• How do decision makers interpret analytical and AI-generated recommendations?
• When does additional information improve decisions, and when does it create information overload?
• How should preferences, values, constraints, evidence, and uncertainty be integrated?
• How can the quality of a decision be evaluated independently of the eventual outcome?
• How can decision processes learn from previous decisions and their consequences?
• How should responsibility and accountability be allocated in human–AI decision environments?

Application Domains

The journal is application-domain independent. Relevant decision problems may arise in, but are not limited to:

• business and management;
• finance and economics;
• operations and supply chains;
• engineering and manufacturing;
• healthcare and medical decision making;
• energy and environmental systems;
• transportation and logistics;
• public administration and public policy;
• risk, safety, and emergency management;
• sustainability and resource management;
• digital platforms and information systems;
• smart cities and intelligent infrastructures;
• education and research management.

Application-oriented manuscripts are welcomed when the application provides insights that extend beyond the specific case and contributes to the understanding, methodology, evaluation, or practice of information-based decision making.

Methodological Diversity

JIBDM welcomes methodological diversity and does not privilege a single research paradigm. Appropriate approaches may include mathematical modeling, optimization, simulation, statistical analysis, machine learning, experimental research, behavioral studies, surveys, case studies, design science, qualitative research, mixed methods, and systematic or critical reviews.

Methodological sophistication alone, however, is not sufficient for publication. The method must serve a clearly defined decision problem and the manuscript must explain how the research advances the acquisition, interpretation, transformation, evaluation, or use of information in decision making.

What JIBDM Does Not Seek

To maintain a distinctive scientific identity, JIBDM generally does not consider manuscripts whose primary contribution is:

• the routine application of an established method or algorithm to a new dataset or case;
• comparison of algorithms based solely on predictive or computational performance without implications for decision making;
• development of an AI or machine-learning model without a clearly articulated decision context;
• optimization without a substantive connection to information, preferences, uncertainty, or decision processes;
• descriptive data analysis without a defined decision problem or decision-related contribution;
• a case study whose findings do not provide transferable methodological, theoretical, or decision-relevant insights;
• development of an information system whose contribution is primarily technical and does not advance decision support or decision processes;
• application of a standard multi-criteria decision-making method without methodological advancement, substantive validation, or new insight into the decision process.

A change of dataset, application domain, geographical context, or case organization alone does not constitute sufficient novelty.

Editorial Perspective

The central question for manuscripts submitted to JIBDM is:

A manuscript may make a theoretical, methodological, technological, behavioral, empirical, or practical contribution. However, the connection between information and decision making must be explicit and substantive.

Through this focus, Journal of Information-Based Decision Making aims to establish a distinct interdisciplinary research space for studying the principles, methods, technologies, and human processes that enable better decisions from available information.