Johannes Siebert

Category: Research

  • 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

  • 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
     

  • Enhancing misinformation correction: New variants and a combination of awareness training and counter-speech to mitigate belief perseverance bias

    Enhancing misinformation correction: New variants and a combination of awareness training and counter-speech to mitigate belief perseverance bias

    Belief perseverance bias refers to an individual’s tendency to persist in holding biased opinions even after the misinformation that initially shaped those opinions has been retracted. This study contributes to research on reducing the negative impact of misinformation by mitigating the belief perseverance bias. The study explores the previously proposed awareness-training and counter-speech debiasing techniques, further developing them by introducing new variants and combining them. We investigate their effectiveness in mitigating the belief perseverance bias after the retraction of misinformation related to a real-life issue in an experiment involving 876 individuals, of whom 364 exhibit the belief perseverance bias. The effectiveness of the debiasing techniques is assessed by measuring the difference between the baseline opinions before exposure to misinformation and the opinions after exposure to a debiasing technique. Our study confirmed the effectiveness of awareness-training and counter-speech debiasing techniques in mitigating the belief perseverance bias, finding no discernible differences in effectiveness between the previously proposed and new variants. Moreover, we observed that the combination of awareness training and counter-speech is more effective in mitigating the belief perseverance bias than the single debiasing techniques.

    Siebert, Jana, Siebert, Johannes U. “Enhancing misinformation correction: New variants and a combination of awareness training and counter-speech to mitigate belief perseverance bias”. PLoS ONE 19(2): e0299139, 2024, 1-15, https://doi.org/10.1371/journal.pone.0299139

  • Reducing the impact of misinformation and fake news

    Reducing the impact of misinformation and fake news

    New methods to effectively reduce the negative impact of misinformation and fake news on people’s opinions

    An investigation into Team Jorge’s activites has shown the sinister influence of misinformation and fake news on politics, society and the economy. Misinformation and fake news became a global phenomenon with the 2016 U.S. presidential election and the Brexit referendum, particularly because more and more people are using social media as a source of news without reflection. The use of artificial intelligence (e.g. chat GPT) in the generation and dissemination of misinformation and fake news will strengthen their influence in the future.The spread of misinformation and fake news on the internet and its consequences are being intensively discussed in the European Parliament. Nevertheless, so far, there is no clear agreement on how to reduce the influence of misinformation and fake news.

    “The problem with misinformation and fake news is that even if it is flawlessly identified as such, something still ‘sticks’ – the misinformation and fake news continue to influence our opinion, explains Prof. Johannes Siebert who researches and teaches at MCI | The Entrepreneurial School®. This phenomenon is called “belief perseverance bias“ and explains the great influence of misinformation and fake news on the formation of opinion and the decision-making behavior of many people. “There are numerous newsrooms and nonprofit organizations that identify misinformation and fake news. This very elaborate work helps reduce the influence of misinformation and fake news. However, these fact checks can only be a first step,“ adds Dr. Jana Siebert.

    The two researchers have been working on the methodological reduction of the belief perseverance bias in the context of misinformation and fake news in the project “PerFake“ funded by the European Union and the Czech Ministry of Education, Youth and Sports. The aim of the PerFake project was to contribute to reducing the negative influence of misinformation and fake news. Prof. Johannes Siebert and Dr. Jana Siebert developed two methods to reduce the belief perseverance bias and tested and optimized them in two experiments with numerous participants. The first results have been published in the prestigious journal PLoS ONE.

    Both tested debiasing methods showed promising results in reducing the belief perseverance bias. The debiasing method “counter-speech“ focuses on refuting the misinformation and fake news by clear counter-arguments. The debiasing method “awareness training“ generally informs the participants about the existence of the belief perseverance bias and how the bias works. Such awareness training could help increase society’s resilience to misinformation and fake news. Prof. Johannes Siebert explains how this can work in practice: “Let us assume you have received a piece of information, for example, you have heard a speech by a politician or read a post on social media. A fact check shows that it is fake news. Being aware of the belief perseverance bias should then help you realize that your original opinion may still be negatively influenced by the fake news and subsequently correct this bias.“ Dr. Jana Siebert adds: “It would, therefore, be desirable to educate the public about the belief perseverance bias and the way it works. For example, fact-checking organizations could complement their fact checks with a note informing about the belief perseverance bias. Such a note could significantly increase the effectiveness of fact-checking and society’s resilience to misinformation and fake news.“

    See also: https://www.mci.edu/en/news-filter-en/228-researchnews/4845-reducing-the-impact-of-misinformation-and-fake-news

    Source:

    Siebert, J., & Siebert, J. U. (2023). Effective mitigation of the belief perseverance bias after the retraction of misinformation: Awareness training and counter-speech. PLoS ONE 18(3): e0282202. https://doi.org/10.1371/journal.pone.0282202

  • Supporting Innovation in Early-Stage Pharmaceutical Development Decisions

    Supporting Innovation in Early-Stage Pharmaceutical Development Decisions

    Pharmaceutical companies regularly review their portfolios to monitor development progress and set priorities in product development. The earliest assets are drug candidates whose efficacy is unknown and whose effects on the human body have not yet been fully explored. For these assets, it is highly uncertain whether they will reach the market and be used in clinical practice. Moreover, not all potential applications are foreseeable, and they can often differ substantially. In the absence of satisfactory methods for deciding how to allocate resources to early development assets, decision-makers focus almost exclusively on assessing an asset’s probability of technical success.

    This study proposes a more holistic methodology to support decision-making in the early phase of pharmaceutical development, drawing on value-focused thinking and multi-criteria decision-making. The methodology operates within the decision quality framework and provides a consistent evaluation of different early development assets across a wide range of disease areas. This combination of concepts and methods was implemented at Bayer Pharmaceuticals, where it proved useful, as the company needed a new, more robust decision-making process for early development. This study therefore discusses how concrete trade-offs at the level of corporate objectives can be made in order to align, communicate, and implement the corporate strategy in the portfolio strategy.

    In addition, this study presents insights for decision analysts and decision-makers in the pharmaceutical industry on how to develop a set of fundamental objectives, how to construct scales to operationalize these objectives, and how to take steps to unburden an organizational decision-making process.

    Methling, Florian; Borden, Steffen A., Veeraraghavan, Deepak; Sommer; Insa, Siebert, Johannes Ulrich; von Nitzsch, Rüdiger; Seidler, Mark „Supporting Innovation in Early-Stage Pharmaceutical Development Decisions “, in Special Issue on Health Decision Analysis: Evolution, Trends, and Emerging Topics by Elisa F. Long, Gilberto Montibeller, Jun Zhuang, Decision Analysis (INFORMS), https://doi.org/10.1287/deca.2022.0452

  • Effects of Decision Training On Individuals’ Decision-Making Proactivity

    Effects of Decision Training On Individuals’ Decision-Making Proactivity

    Decision sciences are in general agreement on the theoretical relevance of decision training. From an empirical standpoint, however, only a few studies test its effectiveness or practical usefulness, and even less address the impact of decision training on the structuring of problems systematically. Yet that task is widely considered to be the most crucial in decision-making processes, and current research suggests that effectively structuring problems and generating alternatives—as epitomized by the concept of proactive decision making—increases satisfaction with the decision as well as life satisfaction more generally.

    This paper empirically tests the effect of decision training on two facets of proactive decision making—cognitive skills and personality traits—and on decision satisfaction. In quasi-experimental field studies based on three distinct decision-making courses and two control groups, we analyze longitudinal data on 1,013 decision makers/analysts with different levels of experience. The results reveal positive training effects on proactive cognitive skills and decision satisfaction, but we find no effect on proactive personality traits and mostly non-significant interactions between training and experience. These results imply the practical relevance of decision training as a means to promote effective decision making even by more experienced decision makers.

    The findings presented here may be helpful for operations research scholars who advocate for specific instruction concerning proactive cognitive skills in courses dedicated to decision quality and/or decision theory and also for increasing, in such courses, participants’ proactive decision making and decision satisfaction. Our results should also promote more positive decision outcomes.

    Veröffentlichung Siebert, Johannes U.; Kunz, Reinhard, Rolf, Philipp. “Effects of decision training on individuals’ decision-making proactivity”, European Journal of Operational Research, 294 (1) 2021, 264-282, https://doi.org/10.1016/j.ejor.2021.01.010

  • Developing and Validating the Multidimensional Proactive Decision-Making Scale

    Developing and Validating the Multidimensional Proactive Decision-Making Scale

    The crucial research questions are how their proactivity in decision situations can characterize individuals, the eventual consequences of proactivity in decision situations, and how the degree of proactivity affects satisfaction with one’s decisions. The scale on Proactive Decision Making (PDM) that has been theoretically developed from literature and empirically validated in cooperation with Prof. Reinhard Kunz (University of Cologne) allows for describing the degree of proactivity of individuals with six dimensions. Two dimensions cover proactive personality traits: ‘striving for improvement’ and ‘showing initiative’. The four dimensions ‘systematical identification of objectives’, ‘systematical identification of information’, ‘systematical identification of alternatives’, and “using a ‘decision radar’ concern proactive cognitive skills and integrate the ideas and concepts of value-focused thinking and decision quality into the PDM-scale.

    This scale provides the basis for analyzing many research questions. For instance, proactive individuals are significantly more satisfied with their decisions, and the scale can explain up to 50% of the variance of decision satisfaction. In another study, the scale was used a priori and ex-post to analyze the impact of an online course on decision-making on the participants. In line with hypotheses derived from literature, the degree of the proactive personality traits remains stable while the degree of the proactive cognitive skills improved through the training significantly. Furthermore, we were able to link proactive cognitive skills to life satisfaction. Scholars who teach courses on decision-making can use these results to claim the relevance and impact of their courses.

    Publications

    Siebert, Johannes; Kunz, Reinhard. “Developing and Validating the Multidimensional Proactive Decision-Making Scale”. Special Issue „Behavioral Operations Research“ in European Journal of Operational Research 249(3) 2016, 864-877, dx.doi.org/10.1016/j.ejor.2015.06.066

    Siebert, Johannes; Kunz, Reinhard, Rolf, Philipp. “Effects of Proactive Decision Making on Life Satisfaction”, European Journal of Operational Research 280(1), 2020, 1171-1187, https://doi.org/10.1016/j.ejor.2019.08.011

    Siebert, Johannes U.; Kunz, Reinhard, Rolf, Philipp. “Effects of decision training on individuals’ decision-making proactivity”, European Journal of Operational Research, 294 (1) 2021, 264-282,https://doi.org/10.1016/j.ejor.2021.01.010