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.

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.