INFORMS Journal on Data Science

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Comparing Exploration–Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-Armed Bandit Experiments
- Ziyuan Zhang
  Corresponding Author
  Ziyuan Zhang
  [email protected]
  https://orcid.org/0009-0005-3627-3443
  Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada
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  ,
- Darcy Wang
  Darcy Wang
  [email protected]
  Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada
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  ,
- Ningyuan Chen
  Ningyuan Chen
  [email protected]
  https://orcid.org/0000-0002-3948-1011
  Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada
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- Rodrigo Mansur
  Rodrigo Mansur
  [email protected]
  Department of Psychiatry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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- Vahid Sarhangian
  Vahid Sarhangian
  [email protected]
  https://orcid.org/0000-0003-2633-6496
  Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Ontario M5S 1A1, Canada
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Published Online:September 1, 2026
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Abstract
Large language models (LLMs) are increasingly used to simulate or automate human behavior in complex sequential decision-making settings. A natural question is then whether LLMs exhibit similar decision-making behavior to humans, and can achieve ...
DISCO: Disentangling Signal Restoration and Corruption Identification via Sample-Wise Latent Low-Rank Similarity
- Yifeng Wang
  Yifeng Wang
  [email protected]
  https://orcid.org/0009-0007-1713-8769
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; and Georgia Tech Manufacturing Institute, Georgia Institute of Technology, Atlanta, Georgia 30332
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- Shancong Mou
  Shancong Mou
  [email protected]
  https://orcid.org/0000-0003-2481-3308
  Department of Industrial and Systems Engineering, University of Minnesota Twin Cities, Minneapolis, Minnesota 55455
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- Jianjun Shi
  Corresponding Author
  Jianjun Shi
  [email protected]
  https://orcid.org/0000-0002-3774-9176
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; and Georgia Tech Manufacturing Institute, Georgia Institute of Technology, Atlanta, Georgia 30332
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- Chuck Zhang
  Chuck Zhang
  [email protected]
  https://orcid.org/0000-0001-7681-5538
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332; and Georgia Tech Manufacturing Institute, Georgia Institute of Technology, Atlanta, Georgia 30332
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Published Online:September 1, 2026
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Abstract
Unsupervised fine-grained anomaly detection is critical for quality monitoring, where decomposition-based methods are widely used to both localize anomalies and restore nonlinear background signals. In the absence of a clean training set, most approaches ...
Deep Learning–Assisted Appointment Scheduling Under Uncertainty
- Amirhossein Moosavi
  Amirhossein Moosavi
  [email protected]
  https://orcid.org/0000-0003-0456-8130
  Michigan Institute for Data and AI in Society, University of Michigan, Ann Arbor, Michigan 48109; and Department of Industrial & Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
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- Onur Ozturk
  Onur Ozturk
  [email protected]
  https://orcid.org/0000-0002-6548-6537
  Telfer School of Management, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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- Rafid Mahmood
  Corresponding Author
  Rafid Mahmood
  [email protected]
  https://orcid.org/0000-0003-1933-066X
  Telfer School of Management, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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- Jonathan Patrick
  Jonathan Patrick
  [email protected]
  https://orcid.org/0000-0003-1364-1803
  Telfer School of Management, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
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Published Online:August 25, 2026
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Abstract
This paper studies the appointment scheduling problem where heterogeneous appointments (jobs) are processed by multiple providers (machines). Our goal is to optimize provider utilization and appointment wait time under uncertainties (processing time, ...
CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models
- Ryan Feng Lin
  Ryan Feng Lin
  [email protected]
  https://orcid.org/0009-0009-6661-7955
  Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 98195
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- Keyu Tian
  Keyu Tian
  [email protected]
  https://orcid.org/0009-0002-6912-8371
  Department of Data Science, City University of Hong Kong, Kowloon, Hong Kong
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- Hanming Zheng
  Hanming Zheng
  [email protected]
  https://orcid.org/0000-0002-6480-2147
  Department of Data Science, City University of Hong Kong, Kowloon, Hong Kong
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- Congjing Zhang
  Congjing Zhang
  [email protected]
  https://orcid.org/0009-0000-4740-609X
  Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 98195
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  ,
- Li Zeng
  Corresponding Author
  Li Zeng
  [email protected]
  https://orcid.org/0000-0003-3503-9135
  Department of Data Science, City University of Hong Kong, Kowloon, Hong Kong
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  ,
- Shuai Huang
  Corresponding Author
  Shuai Huang
  [email protected]
  https://orcid.org/0000-0003-2493-6818
  Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 98195
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Published Online:August 5, 2026
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Abstract
The emergence of large language models (LLMs) has sparked much interest in creating LLM-based digital populations that can be applied to many applications such as social simulation, crowdsourcing, marketing, and recommendation systems. A digital ...
Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
- Yinsong Wang
  Yinsong Wang
  [email protected]
  https://orcid.org/0000-0001-8232-5586
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
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  ,
- Quan Zeng
  Quan Zeng
  [email protected]
  https://orcid.org/0009-0003-3022-7265
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
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  ,
- Xiao Liu
  Xiao Liu
  [email protected]
  https://orcid.org/0000-0003-4510-2847
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
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  ,
- Yu Ding
  Corresponding Author
  Yu Ding
  [email protected]
  https://orcid.org/0000-0001-6936-074X
  H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
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Published Online:August 4, 2026
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Abstract
A community of researchers appears to think that a machine can be surprised and have introduced various surprise measures, principally the Shannon surprise and the Bayesian surprise. The questions of what constitutes a surprise and how to react to one ...

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