DECISION SCIENCE · BEHAVIORAL ECONOMICS · DATA SCIENCE
Better Decisions through Science
Good decisions do not emerge from data and models alone, but from clear objectives and a structured process. This is exactly what I work on in research, education, and consulting.
Understanding and improving decisions
I study how individuals and organizations make complex decisions—and how decision quality can be systematically improved using methods from decision analysis, behavioral economics, data science, and artificial intelligence..
Developing decision-making competence systematically
I develop evidence-based approaches that help students and professionals clarify objectives, identify alternatives, and structure, evaluate, and make decisions with confidence.
Decision support for organizations
I support organizations in complex decision and prioritization problems—from portfolio and resource allocation to strategic decisions under uncertainty—using transparent, scientifically grounded decision models..
Selected work
Award-Winning Research at the Intersection of Data and Decision Science
Research on data- and AI-supported decision making, multicriteria methods, and decisions under uncertainty. Johannes Siebert received the Richard Price Award from the International Academy of Information Technology and Quantitative Management (IAITQM) and was elected a Fellow of the Academy. The award recognizes outstanding contributions to the theory, methodology, and practice of data science.
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Systematically Strengthening Decision-Making Competence among Young People
Development and delivery of evidence-based approaches that help students structure decisions and make more deliberate and informed choices. This work is internationally connected and has been recognized, among other things, through membership in the Advisory Council of the Alliance for Decision Education, whose members have included Richard Thaler and the late Daniel Kahneman.
Supporting Strategic Portfolio Decisions in Pharmaceutical Research
Development of a decision model based on Value-Focused Thinking and Multi-Attribute Utility Theory for the consistent evaluation of early-stage pharmaceutical projects across different therapeutic areas. Together with decision makers, objectives were structured, evaluation scales developed, and trade-offs made transparent to support robust portfolio decisions.
Latest posts
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MCI Professor Johannes Siebert Receives Prestigious SDP Award
International recognition honors outstanding contributions to data-driven decision-making, supply chain management, and societal impact | Award presented at the 32nd SDP Annual Conference at…
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Fifty years of decision analysis in operational research: A review
We review the development of research in Decision Analysis (DA) over the past fifty years. After presenting the axiomatic foundations and discussing the DA…
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Deciding for a Secure Tomorrow: Proactive Decision-Making and Retirement Financial Planning Behavior
Retirement financial planning behavior (RFPB) encompasses the concrete actions individuals take to prepare financially for retirement. We examine RFPB from an Operational Research (OR)…