This seminar series was created to foster collaboration, strengthen the BOR community, raise interest in BOR topics, increase the visibility of BOR, and fast deliver 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 those 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, London)
1 PM; to 1.40 PM (CET, Berlin)
It is scheduled bi-monthly every 2nd Thursday of every second month, under consideration of other workshops, conferences, etc. The next dates are the following:
June 11, 2026, October 8, 2026, December 10, 2026
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, Finding collaborators – e.g., Ph.D. students presenting their work and looking 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) mci.edu)
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BORBS XXIV: Black Box or Glass Box? Overconfidence and the hidden cost of decision support interfaces
Authors: Ayşegül Engin, Department of Business Decisions and Analytics, University of Vienna
Abstract: Digital transformation has expanded the business applications of decision support and recommender systems. However, optimizing user-system interaction remains challenging due to cognitive biases — such as hindsight bias and overconfidence — that are not easily addressed through design modifications alone. This study explores how overconfidence, driven by hindsight bias, influences the effectiveness of personalized recommendation and decision support systems. Adopting an experimental approach and employing an Experience-Weighted Attraction (EWA) reinforcement learning model, I investigate the impact of these systems on repeated decision-making under uncertainty, where outcomes can only be improved through experience. Three conditions are compared: no decision support, a black-box recommender system providing only a recommendation, and an explicit decision support system providing information-summarizing support without an explicit recommendation. Findings reveal that users benefit from different DSS interfaces in markedly different ways depending on their cognitive biases. Notably, even when the underlying decision rules are held constant across interfaces, the form in which support is delivered leads to substantial differences in decision quality and learning behavior. The results contribute to the behavioral OR literature by demonstrating that interface design — not only algorithmic sophistication — is central to whether users realize the promised benefits of AI-augmented decision support, and by introducing a decision-training perspective that goes beyond single-shot outcomes.
Why should you join?
A counter-intuitive finding for an AI-investment era. Organizations are pouring substantial sums into AI-augmented decision support — with finance firms commonly investing
more than US$10 million, and high-profile failures such as the IBM Watson Healthcare project reaching the multi-billion-dollar range. This talk shows why the standard assumption that “all users benefit equally” can quietly undermine these investments, and what the moderating role of overconfidence looks like empirically.
A clean experimental test of an under-examined design question. Most comparisons of recommender systems and explicit decision support confound interface differences with
algorithmic differences. By holding the underlying decision rule constant across conditions, this study isolates what the interface alone does to decision quality — a methodological choice that makes the behavioral mechanism interpretable rather than buried in algorithmic noise.
A reinforcement-learning lens on user behavior. The talk introduces an Experience-Weighted Attraction (EWA) model adapted to the DSS context, allowing us to estimate not only whether users make better choices, but how they learn — separating learning rate, decision consistency, and experience-weight decay. This computational layer offers a richer behavioral diagnostic than outcome-based comparisons alone.
From single-shot outcomes to a decision-training perspective. The talk reframes DSS evaluation away from “did the user pick correctly this time?” and toward “is the user becoming a better decision-maker over repeated interactions?” — a perspective that opens up new design and managerial questions for behavioral OR and information systems researchers alike.
June 11th, 2026
12 PM to 12.40 PM (UK, London)
1 PM; to 1.40 PM (CET, Berlin)
https://us02web.zoom.us/j/89143663283?pwd=kxdJrqXHfZ0O2nJVPL7TgfCBfuJtRq.1
Meeting-ID: 891 4366 3283
Kenncode: 1
Comment: In case of technical problems, please visit https://www.euro-online.org/websites/bor/behavioral-operation-research-brown-bag-seminar-series/ before the start of the meeting.
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Authors: Jun Zhuang (University at Buffalo)
Abstract
Society faces growing risks from natural and human-caused disasters such as wildfires, extreme weather, infrastructure disruptions, and emerging security threats. Building resilience requires coordinated decision-making among multiple stakeholders—including governments, industry, communities, and strategic adversaries—with differing objectives and incentives. This research develops a unified framework integrating game theory, data analytics, and artificial intelligence, including reinforcement learning, to study strategic interactions and adaptive decision-making in disaster management and homeland security. The work designs risk-informed policies that balance efficiency and equity, public and private investment, preparedness and response, and economic development and safety. Applications span wildfire management, public safety, misinformation, border and infrastructure security, and Arctic resilience, advancing next-generation decision-support tools for adaptive and coordinated disaster resilience.
Why should you join?
Reasons why one should attend: This seminar explores how behavioral operations research can be integrated with game theory, artificial intelligence, and reinforcement learning to better understand multi-stakeholder decision-making in disaster management. Attendees will gain insights into how human behavior, strategic interaction, and AI-assisted decision support jointly shape resilience policies in complex environments such as wildfire management and public safety systems.
Additional comment: Jun will also provide insights about publishing a paper in the interface of BOR and DA in the INFORMS Journal Decision Analysis
April 9th, 2026
12 PM to 12.40 PM (UK, London)
1 PM; to 1.40 PM (CET, Berlin)
https://us02web.zoom.us/j/89143663283?pwd=kxdJrqXHfZ0O2nJVPL7TgfCBfuJtRq.1
Meeting-ID: 891 4366 3283
Kenncode: 1
Comment: In case of technical problems, please visit https://www.euro-online.org/websites/bor/behavioral-operation-research-brown-bag-seminar-series/ before the start of the meeting.
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Authors: Oliver Behn (University of Göttingen), Michael Leyer (University of Marburg), Silke Hüttel (University of Göttingen)
Abstract
Climate change unfolds through increasing frequency and magnitude of climate extreme events posing threats to agricultural productivity, farm incomes, and economic resilience of food systems. In response, farms face an urgent need to adapt their production systems. However, empirical research consistently reports significant adaptation gaps.
Many economic and climate-related decision models are based on the assumption that adaptation decisions are made from description. In these models, farmers are expected to evaluate adaptation options by considering their anticipated consequences under future climatic conditions, often relying on assumed probabilities of future agro-climatic states. Such approaches, however, often insufficiently capture the complexity of real-world decision-making under uncertainty.
In practice, farmers are often uncertain about the likelihood, timing, and severity of future climate extreme events. Moreover, human factors such as cognitive biases, emotions, and subjective risk perceptions are frequently overlooked. However, farm adaptation decisions are more realistically understood as a “wicked problem” with no clear or universally optimal solution. In such situations, farmers may rely less on abstract probabilistic information and instead draw on personal experiences with past climate extreme events when making adaptation decisions.
This presentation provides an overview of how farmers adapt to climate change and discusses different types of adaptation strategies. It further presents experimental results that examine the role of personal experience with climate extreme events in shaping farmers’ adaptation behavior.
Why should you join?
– Participants will gain insight into how climate adaptation is implemented in agricultural contexts and the key challenges farmers face.
– The presentation will report experimental evidence on how the cognitive processing of experienced climate extreme events influences farmers’ decision-making.
February 12th, 2026
12 PM to 12.40 PM (UK, London)
1 PM; to 1.40 PM (CET, Berlin)
https://us02web.zoom.us/j/89143663283?pwd=kxdJrqXHfZ0O2nJVPL7TgfCBfuJtRq.1
Meeting-ID: 891 4366 3283
Kenncode: 1