Johannes Siebert

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  • Richard Price Award for MCI Professor Johannes Siebert

    Richard Price Award for MCI Professor Johannes Siebert

    Recognition for contributions to Data Science and its application in planning and prioritization decisions

    Johannes Siebert, Professor of Decision Sciences, Behavioral Economics and Supply Chain Management at MCI | The Entrepreneurial School®, has received the Richard Price Award from the International Academy of Information Technology and Quantitative Management (IAITQM) and has also been elected a “Fellow” of the Academy. The award was presented at the 13th International Conference on Information Technology and Quantitative Management (ITQM 2026) in Rouen. The Richard Price Award honors individuals who have made outstanding contributions to the theory, methodology and application of Data Science. The award is named after Richard Price, whose posthumous publication of Bayes’ theorem laid a foundation for modern data-based inference.

    With the award, IAITQM recognizes Siebert’s work at the interface of Data Science and decision-making. His research program combines data-driven and AI-supported approaches with multicriteria decision methods and provides methodologically sound, empirically validated approaches for translating analytical results into well-founded resource and capital allocation decisions under uncertainty. The practical viability of this approach is demonstrated by high-level applications for which Siebert has been nominated several times for the Decision Analysis Practice Award (INFORMS): For Bayer AG, he developed the evaluation model for prioritizing a project portfolio (published in Decision Analysis, INFORMS, 2022); he advised the California Department of Transportation (Caltrans) on developing the methodological model for allocating an annual infrastructure budget in the double-digit billions of US dollars.

    “Data Science is fundamentally changing how decisions are made – at the same time, the importance of structured, value-focused decision processes has never been greater. This recognition encourages me to continue advancing this field in research, practice and teaching,” says Johannes Siebert.

    At MCI, Siebert conducts research and teaches in data-based decision-making, Business Analytics, Behavioral Economics and multicriteria methods, with a particular focus on the role of Artificial Intelligence in entrepreneurial planning, prioritization and control decisions. This orientation strengthens innovation, digitalization and future-oriented capabilities in academia and business alike. Beyond research, Siebert brings his findings into decision education programs, thereby extending the impact of decision competence beyond the university.

    “This recognition underscores the international significance of MCI research in the fields of Entrepreneurship and Decision Making. It shows that innovative research on entrepreneurial action and intelligent decision-making is recognized worldwide and sustainably strengthens MCI’s international reputation,” adds MCI-Head of Research & Development Martin Pillei.

    Siebert’s academic references include a paper co-authored with Ralph L. Keeney (Duke University) in Operations Research, one of the world’s leading journals in the field. His work has also received extensive international recognition: the Inspirational Achievement Award of the Society for Decision Professionals (SDP), the Best Research Paper Award of the Decision Sciences Journal of Innovative Education (2023), finalist status for the European Award for Excellence in Teaching in the Social Sciences and Humanities, and a TEDx talk with more than 175,000 views.

    The original press release can be found here: https://www.mci.edu/en/news-filter-en/117-studyprogram-news/276-news-business-management-for-professionals/7387-richard-price-award-for-mci-professor-johannes-siebert

  • Deciding for a Secure Tomorrow: Proactive Decision-Making and Retirement Financial Planning Behavior

    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) perspective using Decision Analysis (DA) principles—decision quality and value-focused thinking—operationalized via proactive decision-making (PDM), which integrates DA-grounded proactive cognitive skills (PCS) with proactive personality traits derived from the organizational behavior literature. Using cross-sectional survey data from 457 UK adults and structural equation modelling with systematic model comparisons and multigroup tests, we assess whether PDM influences RFPB through four psychological traits—propensity to plan, confidence in financial information search, willingness to accept investment risk, and general self-efficacy—and whether relationships differ by financial literacy and numeracy. Results show that PDM affects RFPB entirely through these psychological traits (full mediation), with PCS—the trainable, DA-grounded decision-analytic skills—serving as the operative mechanism, while proactive personality traits are non-significant in this pathway. The mediated model explains 57.1% of RFPB variance and outperforms partial-mediation, traits-only, and reverse-causality alternatives. Multigroup analyses indicate that the indirect structure holds across financial literacy and numeracy groups, with patterns suggesting a compensatory role of PCS under lower financial literacy. Together, the evidence links DA-grounded decision-analytic skills to RFPB. Our findings highlight the potential of PCS-focused decision-analytic competence training as an OR-relevant mechanism to promote RFPB, complementing financial literacy and numeracy programs. Additionally, our study complements optimization-focused OR approaches to retirement financial planning by identifying PCS as a decision-analytic lever that strengthens RFPB—the behavioural precondition for adopting such optimized prescriptions in practice.

    Siebert, Jana; Siebert Johannes U., Blösl, Florian; “Deciding for a Secure Tomorrow – Examining Proactive Decision-Making and Retirement Planning Behavior”, European Journal of Operational Research) (in press), https://doi.org/10.1016/j.ejor.2025.10.021

  • MCI Professor Johannes Siebert Receives Prestigious SDP Award

    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 the University of Massachusetts in Boston

    Johannes Siebert, Professor of Decision Sciences, Behavioral Economics, and Supply Chain Management at MCI, has been honored with the “Inspirational Achievement Award” by the Society for Decision Professionals (SDP). This internationally renowned award was presented during the 32nd SDP Annual Conference at the University of Massachusetts in Boston, recognizing exceptional achievements in decision analysis and outstanding commitment to advancing the field.

    At MCI, Johannes Siebert conducts research and teaching in the field of decision analysis, with a strong focus on data-driven decision-making, behavioral economics, and multi-criteria methods. A particular emphasis lies on the role of artificial intelligence in decision processes, as well as on making decision quality measurable and trainable – key aspects in fostering innovation, digitalization, and future competencies in both academia and industry.

    “Decision science is at a turning point: artificial intelligence is transforming how decisions are made. However, at the same time, the importance of structured, value-focused decision processes has never been greater. This award strongly motivates me to further advance the field across research, practice, and teaching,” says Johannes Siebert.

    Johannes Siebert regularly contributes his expertise to international applied projects for both private and public sector partners, including initiatives in the energy, transportation, and pharmaceutical industries. His achievements have earned him multiple nominations for the Practice Award of the Decision Analysis Society. His excellence in teaching has also received international recognition, including a finalist nomination for the highly regarded European Award for Excellence in Teaching in Social Sciences and Humanities.

    MCI Rector Andreas Altmann adds: “This award highlights what MCI stands for: excellence in research, strong practical orientation, and active engagement with key societal challenges. Johannes Siebert exemplifies these values and strengthens MCI’s international reputation as a leading institution in decision sciences and management education.”

    The original press release can be found here: https://www.mci.edu/en/media-en/news/7127-mci-professor-johannes-siebert-receives-prestigious-sdp-award

  • Fifty years of decision analysis in operational research: A review

    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 process, we start with value-focused thinking as a problem structuring method. We then analyze the model building phase, with a focus on graphical models for decision-making under uncertainty: belief networks, decision trees, and influence diagrams. Next, we analyze how DA research has dealt with uncertainty focusing on the areas of elicitation, aggregation, and evaluation. We then discuss sensitivity analysis, describing local and global techniques, from one-way sensitivity analysis to the value of information. Finally, we review the literature on information acquisition and discuss the role of information value in this context

    Borgonovo, Emanuele; José, Victor, R. R, Shachter, Ross; Siebert, Johannes U; Ulu, Canan. “Fifty Years of Decision Analysis in Operational Research: A Review” (Invited Review on occasion of the celebration of the 50th Anniversary of EURO (the European association of Operational Research Societies), European Journal of Operational Research) https://doi.org/10.1016/j.ejor.2025.05.023

  • ChatGPT vs. Experts: Can GenAI Develop High-Quality Organizational and Policy Objectives?

    ChatGPT vs. Experts: Can GenAI Develop High-Quality Organizational and Policy Objectives?

    This paper explores the efficacy of generative artificial intelligence (GenAI) for value-focused thinking, specifically its ability to generate high-quality sets of objectives for organizational and policy decisions. Overall, we find that while most GenAI-generated objectives are individually viable, the objective sets as a whole exhibit substantial shortcomings. They often include nonessential considerations, omit important objectives, and lack structure due to redundancy and poor decomposability. A key issue is the tendency of GenAI to include means objectives, even when explicitly instructed not to do so.

    At the same time, we show that the quality of objective sets can be significantly improved by applying best practices in prompting and incorporating decision analysis (DA) expertise. The findings highlight the importance of a human-in-the-loop approach: GenAI is useful for generating initial objective ideas, but expert input from decision analysts is essential before using the results to support real-world decision making.

    To operationalize this, we present and demonstrate a four-step approach that combines the complementary strengths of GenAI and decision analysts.

    PLease more information in the MCI-press release: https://www.mci.edu/en/news-filter-en/117-studyprogram-news/276-news-business-management-for-professionals/7208-mci-research-where-ai-ends-human-expertise-beginshttps://www.mci.edu/en/news-filter-en/117-studyprogram-news/276-news-business-management-for-professionals/7208-mci-research-where-ai-ends-human-expertise-begins

    Simon, Jay; Siebert, Johannes U. ChatGPT vs. Experts: Can GenAI Develop High Quality Organizational and Policy Objectives? Decision Analysis (in press). https://doi.org/10.1287/deca.2025.0387

  • Linear Transformation of One-Dimensional Utility Functions: Empirical Study on the Impact on the Final Ranking of Alternatives in Personal Decisions

    Linear Transformation of One-Dimensional Utility Functions: Empirical Study on the Impact on the Final Ranking of Alternatives in Personal Decisions

    Determining one-dimensional utility functions for each objective in multiattribute utility theory (MAUT) requires substantial time and cognitive effort from decision makers. They must account for decreasing or increasing marginal utility as well as their individual risk attitudes, often resulting in nonlinear utility functions. This assessment process is prone to errors and distortions.

    In this study, we analyze the extent to which a linear transformation of one-dimensional utility functions compromises decision quality. Specifically, we examine three aspects: the use of (non)linear utility functions, their impact on the ranking of alternatives, and the stability of the best alternatives depending on utility differences under the assumption of linearity.

    Our analysis is based on 2,536 carefully modeled personal decisions conducted by students using the decision support tool Entscheidungsnavi. The results show that 95.9% of participants used at least one nonlinear utility function, and 76.4% of all objectives were evaluated nonlinearly. Simplifying preference-accurate utility functions through linearization led to a rank reversal of the best alternative in 15.5% of the decisions. The set of the top three alternatives changed in 14% of the cases. However, in 98.8% of the decisions, the best alternative remained within the top three under the assumption of linear utility functions.

    Based on these findings, we recommend determining utility functions as preference-accurately as possible, including nonlinearities, especially for important decisions. However, no rank reversal of the best alternative was observed when the absolute utility difference between the best and second-best alternative exceeded 0.27 under linearity. In such cases, assuming linear utility functions can be a useful simplification to save time and effort.

    Tönsfeuerborn, M., von Nitzsch, R., & Siebert, J. U. (2026). Linear transformation of one-dimensional utility functions: Empirical study on the impact on the final ranking of alternatives in personal decisions. Decision Analysis, 23(1), 46–64. https://doi.org/10.1287/deca.2024.0317
     

  • Teaching is described by its methods. It should be judged by its goals.

    Teaching is described by its methods. It should be judged by its goals.

    I was shortlisted for the European Award for Excellence in Teaching in the Social Sciences and Humanities. Central European University has presented it since 2011 to academics teaching in the European Higher Education Area; it carries the Diener Prize of 5,000 euros. The award went to another candidate in early September. The outcome occupies me less than something I noticed while assembling the application.

    A dossier forces you to write things down

    An application of this kind requires you to document your own teaching: course descriptions, assessment concepts, feedback from students and from colleagues, an account of your understanding of teaching. Anyone doing this inevitably reads other teaching concepts, calls for applications and accreditation documents as well. A regularity stands out: almost all of them describe first how teaching is done — blended learning, project-based work, digital tools — and only afterwards what it is meant to achieve. The second part often remains implicit.

    The difference between means and ends

    In decision analysis this distinction is central. A fundamental objective is what you pursue for its own sake. A means objective matters only because it contributes to a fundamental objective. Flipped classroom, peer feedback, AI-supported tools: all of them means. They are replaceable as soon as something better becomes available.

    The order is not merely a question of presentation. It determines which alternatives come into view at all.

    Start with methods and you vary what you already know. Start with objectives and you arrive at alternatives that do not appear in the catalogue of methods.

    That may mean different assessment formats, different assignments, and sometimes the conclusion that a piece of content has no place in the curriculum.

    The question that should come first

    What should graduates be able to do that they could not do without this degree? My answer: they should be able to structure a decision under uncertainty so that they can justify it to themselves and to others. Subject knowledge is a precondition for this, not the result. That answer implies something different from a catalogue of methods: assessments in which students document a decision of their own instead of reproducing models; projects on questions with no known solution; criteria that address the process rather than the result.

    I regard this order as the point at which teaching development is decided.

    What remains

    For me the value of such procedures lies not in their outcome but in the compulsion to be explicit. You have to write down what you otherwise take for granted, and in doing so you notice where the justification gets thin. That does not require a prize. It is enough to read one of your own course descriptions with a single question in mind: does it say what all this is good for — or only how it is done?

    About the award: European Award for Excellence in Teaching in the Social Sciences and Humanities, presented by Central European University — elkana.ceu.edu

    Further reading: MCI news item of 29 September 2025 on the shortlist nomination

  • Decision scientist Johannes Siebert: School over – now what? How young people find the right career

    Decision scientist Johannes Siebert: School over – now what? How young people find the right career

    What comes after school? Choosing a career and course of study is one of the first significant decisions young people make in life—and often one of the most challenging. Decision scientist Prof. Johannes Siebert explains why it is more than just a „compulsory program“ and what opportunities it offers.

    Many young people are currently asking themselves: „What am I going to do after school?“ Why is this decision so important?

    For many of them, choosing a career or course of study is one of the first significant decisions they must make independently. It not only influences their professional career, but also their self-confidence and their prospects. In short: their lives. It is often possible to change the initial decision to study or do vocational training without a great deal of bureaucracy.

    However, as surveys show, this often results in considerable individual psychological stress for young people – not to mention the overall economic consequences, such as an increased shortage of skilled workers and lower economic productivity due to delayed career entry.

    Read the full article here (in German): https://www.focus.de/familie/ausbildung/entscheidungswissenschaftler-johannes-siebert-schule-vorbei-und-jetzt-wie-junge-menschen-den-passenden-beruf-finden_22435455-3583-4c2a-8981-3b80dd727a0b.html

    Please stay current and do not miss any of my contributions on Focus Online. Follow me and network with me for exciting insights and current discussions. I look forward to exchanging ideas with you! https://www.linkedin.com/in/johannes-siebert/

  • Lies and manipulation on the internet: expert shows you two effective ways to protect yourself against fake news

    Lies and manipulation on the internet: expert shows you two effective ways to protect yourself against fake news

    Fake news is deliberately spread to manipulate opinions and, thus, elections. Fact checks alone are powerless against their public effectiveness. However, in combination with other tools, resistance to false claims can be strengthened in the long term.

    „You can fool all of the people some of the time and some of the people all of the time, but you can’t fool all of the people all of the time,“ declared US President Abraham Lincoln more than 150 years ago. Even though he was already vividly aware of the influence of fake news on public opinion back then, he was confident enough to believe that the truth would prevail in the end – at least for most people. Can we still share this optimism in the age of internet media that is prone to manipulation and its global networking?

    Despite prominent experiences to the contrary, resignation is clearly out of place. A large number of scientific studies have recently examined the effects of fake news and developed effective countermeasures. The results are cause for concern, but they also show that we are by no means defenseless against the seductive power of fake news. Perhaps just reading this article can help you react more consciously and resiliently in the future to campaigns that aim to persuade you to believe something you would never think on your own.

    Read the full article here (in German): https://www.focus.de/experts/profi-zeigt-zwei-methoden-wie-sie-sich-effektiv-gegen-fake-news-wappnen-koennen_id_260161530.html

    Please stay current and do not miss any of my contributions on FOCUS Online. Follow me and network with me for exciting insights and current discussions. I look forward to exchanging ideas with you! https://www.linkedin.com/in/johannes-siebert/

  • How to become your own decision architect and make better career decisions

    How to become your own decision architect and make better career decisions

    The only way that you can purposefully influence anything in your life is by your decisions. The rest of your life happens. This article offers practical concepts and proper procedures, empowering you to become your own decision architect and make systematically better decisions, ultimately improving your life. Additionally, specific tips for making career decisions are provided.

    Note: This article is based on the TEDxInnsbruck “Give yourself a nudge: How you can make better decisions”, 2021 (available at https://www.ted.com/talks/johannes_siebert_nudge_yourself_to_make_better_decisions). It also offers specific guidance on making informed career decisions.

    https://www.researchgate.net/publication/374631691_How_to_become_your_own_decision_architect_and_make_better_career_decisions