Behavioral Operation Research Brown Bag Seminar Series (BORB2S2)

This seminar series was created to foster collaboration, strengthen BOR community, raise interest for BOR topics, increase the visibility of BOR, and fast delivery of new ideas.

The seminars are scheduled for 40 min. The generic timetable suggests a brief introduction (5 min), a contribution (20 min), and a discussion (15 min). However, there is flexibility concerning the length of the contributions. In addition, there is an opportunity for interested in staying in the room to continue discussions.

The seminar takes place during “Brownbag-time for Europeans”

12 PM to 12.40 PM (UK GMT-1)

1 PM; to 1.40 PM (CET, Berlin)

It is scheduled bi-monthly every 2nd Thursday every second month under consideration of other workshops, conferences, etc. The next dates are the following:

December 8th 2022 , February 9th 2023, April 13th 2023, June 8th 2023

Different types of contributions are possible: Conference talks – work in progress, Mini-panel discussions with pre-assigned panelists, Open discussion with initial input of one contributor, Editors of journals discussing publishing BOR papers, Find collaborator – e.g., Ph.D. students present their work and look for a collaborator they could benefit from

Please reserve your BORB2S2 presentation date! Only an abstract of the talk is needed. Topics can cover any facets of BOR. Self-promotions are highly welcome. You can also suggest other speakers. Send all enquiries to Johannes Siebert (Johannes.Siebert (at)

  • BOR B2S2 VIII: The behavior of demand planners: their effectiveness in using diverse information

    Robert Fildes (presenting author), Distinguished Professor (Emeritus) and Founding Director, Centre for Marketing Analytics and Forecasting, Lancaster University Management School, UK

    Paul Goodwin, Professor Emeritus, School of Management, University of Bath; UK

    February 9th 2023

    12 PM to 12.40 PM (UK GMT-1)

    1 PM; to 1.40 PM (CET, Berlin)

    Meeting-ID: 891 6049 7274

    Kenncode: 857585

    Comment: In case of technical problems, please visit before the start of the meeting.


    Most private and many public organizations employ ‘demand planners’ whose job it is to forecast the sales (or activities) arising in their organization. The processes, which the planners undertake, are usually complex, often involving interactions with many colleagues, information from external sources, and ‘advice’ from forecasting software. In this presentation, we describe a typical demand planning process, highlighting the information used and misused through the lens of the ‘heuristics and biases’ literature. Research in this area, based on both experiments and field studies, has been limited. In the current study, we integrate 6 data sources to highlight commonalities in the process by which a final demand forecast is reached. The conclusions are striking and underline the importance of a forecasting system that limits the damage arising from the biases of the participants.

    Why should you join?

    Demand forecasting is ubiquitous. Understanding the processes by which information is interpreted in order to produce a final forecast, which is then used in various operational decisions, is important in practice – poor forecasting has serious financial consequences. But it is also of great current theoretical interest as it asks and partially answers the question of how users interpret the information arising from increasingly complex AI type models. Methodologically, successful research requires a ‘mixed method’ approach.

  • BOR B2S2 VII: The Behavioural Dimensions of Sustainable Energy Transitions – Opportunities and Research Requirements for Behavioural OR

    In cooperation with INESC Coimbra

    Valentin Bertsch (presenting author), Professor of Energy Systems and Energy Economics, Ruhr-Universität Bochum, Germany

    The research presented draws on a variety of findings from different collaborations. In particular, I would like to thank David Huckebrink, Jonas Finke, Sophie Pathe, Leonie Plaga (RUB), Muireann Lynch, John Curtis (ESRI), Jason Harold (NUIG), Desta Fitiwi (UCD), and Joseph DeCarolis (NCSU).

    Meeting-ID: 889 6644 6633
    Kenncode: 446687

    Comment: In case of technical problems, please visit before the start of the meeting:

    December 8th 2022

    12 PM to 12.40 PM (UK GMT-1)

    1 PM; to 1.40 PM (CET, Berlin)


    Many countries worldwide have adopted policies to support the expansion of renewable energy sources aimed at reducing greenhouse gas emissions, combating climate change, and, more generally, establishing a globally sustainable energy system. As a result, energy systems around the world are undergoing a process of fundamental change and transformation that goes far beyond the technological dimension. While energy system models have been developed and used for several decades to support decision makers in governments and companies, these models usually focus on the techno-economic dimension, whereas they fall short in addressing and considering behavioural and societal aspects of decisions related to technology acceptance, adoption, and use. In fact, it is often the societal dimension that comes with the greatest challenges and barriers when it comes to making such a socio-technical transformation happen in reality. This presentation therefore provides an overview of state-of-the-art energy system models on the one hand and research studying behavioural aspects in the energy sector on the other hand. These are two well-developed fields of research but they have not yet been integrated sufficiently well to provide answers to the many questions arising in the context of complex socio-technical transformation processes of energy systems. Some existing approaches integrating these two fields will be shown. Opportunities as well as research and collaboration requirements for Behavioural OR will be discussed.

    Why should you join?

    • For the BOR community: Get an overview of a fascinating field of application for BOR methods and expertise.
    • For the energy systems community: Learn about the (existence of the) BOR community and possibilities for collaboration.

  • BOR B2S2 VI: Do Non-Linear Utility Functions Matter? Unique Insights Derived from almost 2.000 High-Quality Real-World Decisions

    Mendy Tönsfeuerborn (presenting author), RWTH Aachen University, Decision Theory and Financial Services Group, 52062 Aachen, Germany

    Johannes Ulrich Siebert (presenting author), Management Center Innsbruck, Department Business and Management, 6020 Innsbruck, Austria

    Rüdiger von Nitzsch, RWTH Aachen University, Decision Theory and Financial Services Group, 52062 Aachen, Germany

    October 13th, 2022,

    Meeting-ID: 824 4319 4473 // Code: 1

    Comment: In case of technical problems, please visit before the start of the meeting:


    Multi-attribute utility theory (MAUT) is broadly used to evaluate alternatives. With the help of partial utility functions, decision makers can express their preferences in decision situations. For a utility function, the most straightforward shape is linear; however, a variety of other, more complicated shapes are suggested in the literature. Researchers have developed more and more sophisticated methods for several decades to elicit preferences precisely. They work on the assumption that the more precisely the preferences are modeled, the better the results of the decision analysis will be. If these time-intensive methods are applied, the utility functions are often non-linear. We found only scarce empirical evidence analyzing to what degree the precise elicitation of preferences is worth the effort.

    To fill this gap, we investigate the extent to which linearization of non-linear utility functions leads to rank shifts, especially of the best-ranked alternative. We analyzed 1,959 real-world decisions in the decision support tool Entscheidungsnavi. In the decision frontend, the Entscheidungsnavi provides users with substantial support based on value-focused thinking to help them define and structure their decisions. In the decision backend, MAUT is used to elicit and aggregate preferences. The participants were trained to use the Entscheidungsnavi and spent several hours making their decision. Therefore, it can be assumed that they articulated their preferences accordingly. Most participants (97.40%) used non-linear utility functions and identified between 3 and 9 alternatives. We calculated the rankings for the participants’ stated preferences and the rankings that resulted when all utility functions were linearized. Our analysis reveals that in 85.55% of cases, using linear utility functions instead of non-linear utility functions did not affect the best-ranked alternative. Moreover, we demonstrated that the linearization of the highest weighted objective has a higher impact on the best alternative than linearizing the least weighted. On top of that, we provide regression lines with which the decision maker can assess and evaluate the risk of a rank shift by simplifying the determination of one or more utility functions through linearization.   

    Why should you join?

    We provide unique insights about the implications of using non-linear utility functions.

    We provide recommendations about the level of effort, which should be spent to elicit preferences in certain types of decision situations.

    We assume that there will an interesting and controversial discussions about using non-linear utility functions.