Decision-Making under Uncertainty

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Decision-making under uncertainty studies how agents should choose actions when outcomes, states, preferences, or even the governing model are imperfectly known. Common approaches include Bayesian beliefs and partially observable Markov decision processes for sequential learning, robust optimization for misspecified distributions, risk-sensitive criteria such as CVaR, and conformal methods that calibrate when to act, gather information, or abstain. These methods are tested in settings such as autonomous driving and robotics, clinical diagnosis, energy and supply-chain scheduling, finance, and human–AI interaction. A central tension is that better-calibrated or more conservative uncertainty estimates do not automatically improve decisions: methods must balance safety and tail risk against utility, computational cost, and the value of additional information.

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204 papers24 papers in the last four weeks, up 26% on the four weeks before. 0.1% of all new papers.

Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation
Mar 10, 2026Hongyu Cao, Jinghan Zhang, Kunpeng Liu +5Robust RL Decision-Making under Uncertainty
Arizona State University · Clemson University · University of Kansas +3
Efficient Policy Learning with Hybrid Evaluation-Based Genetic Programming for Uncertain Agile Earth Observation Satellite Scheduling
Mar 9, 2026Junhua Xue, Yuning Chen, Mingyan Shao +3Genetic Programming Automated Heuristic Design
Sequential Service Region Design with Capacity-Constrained Investment and Spillover Effect
Mar 9, 2026Tingting Chen, Feng Chu, Jiantong ZhangSequential Decision Making Decision-Making under Uncertainty
School of Economics and Management, Tongji University, Shanghai, 200092, China · Laboratoire IBISC, Univ-Evry, Université Paris-Saclay, Evry, 91000, France
Risk-Aware Rulebooks for Multi-Objective Trajectory Evaluation under Uncertainty
Mar 4, 2026Tichakorn WongpiromsarnRisk-Aware Path Planning Decision-Making under Uncertainty
Department of Computer Science, Iowa State University, Ames, IA 50010
What Capable Agents Must Know: Selection Theorems for Robust Decision-Making under Uncertainty
Mar 3, 2026Aran NayebiWorld Models Decision-Making under Uncertainty
Machine Learning Department and Neuroscience & Robotics Institutes, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
When Alpha Breaks: Two-Level Uncertainty for Safe Deployment of Cross-Sectional Stock Rankers
Feb 24, 2026Ursina SanderinkQuantitative Finance Decision-Making under Uncertainty
A Dynamic Survey of Soft Set Theory and Its Extensions
Feb 24, 2026Takaaki Fujita, Florentin SmarandacheDecision-Making under Uncertainty
POMDPPlanners: Open-Source Package for POMDP Planning
Feb 24, 2026Yaacov Pariente, Vadim IndelmanPartially Observable Markov Decision Processes Decision-Making under Uncertainty
Faculty of Mathematics, Technion - Israel Institute of Technology, Haifa 3200003, Israel · Stephen B. Klein Faculty of Aerospace Engineering, Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa 3200003, Israel
Wasserstein Distributionally Robust Online Learning
Feb 23, 2026Guixian Chen, Salar Fattahi, Soroosh ShafieeDistributionally Robust Optimization Decision-Making under Uncertainty
University of Michigan · Cornell University
Calibrate-Then-Act: Cost-Aware Exploration in LLM Agents
Feb 18, 2026Wenxuan Ding, Nicholas Tomlin, Greg DurrettLLM Agents Decision-Making under Uncertainty
New York University
AI Arms and Influence: Frontier Models Exhibit Sophisticated Reasoning in Simulated Nuclear Crises
Feb 16, 2026Kenneth PayneStrategic Reasoning Risks in Language Models Large Language Model-Based Social Simulation
King’s College London
Multimodal Belief-Space Covariance Steering with Active Probing and Influence for Interactive Driving
Feb 16, 2026Devodita Chakravarty, John Dolan, Yiwei LyuRisk-Sensitive Planning Autonomous Driving Planning
Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, India · Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
Uncertainty-aware Causal Decision Making via Effect Bound Decomposition
Jan 30, 2026Md Musfiqur Rahman, Ziwei Jiang, Hilaf Hasson +1Causal Effect Estimation Parameter Identifiability
Electrical and Computer Engineering, Purdue University · Computer Science, Johns Hopkins University · Cohesity
Bayesian dynamic scheduling of multipurpose batch processes under incomplete look-ahead information
Nov 30, 2025Taicheng Zheng, Dan Li, Jie LiDecision-Making under Uncertainty
Centre for Process Integration, the Department of Chemical Engineering, The University of Manchester, Oxford Road, Manchester, M13 9SS, United Kingdom
People use fast and flat simulation to reason about new games
Oct 13, 2025Katherine M. Collins, Cedegao E. Zhang, Lionel Wong +6Computational Cognitive Modeling Human Behavior Simulation
Massachusetts Institute of Technology, Cambridge, MA, United States · Princeton University, Princeton, NJ, United States · University of Cambridge, Cambridge, United Kingdom +3
Meta-learning ecological priors from large language models explains human learning and decision making
Aug 28, 2025Akshay K. Jagadish, Mirko Thalmann, Julian Coda-Forno +2Computational Cognitive Modeling Meta-Learning
Institute for Human-Centered AI, Helmholtz Computational Health Center, Munich, Germany · Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Tübingen, Germany · Eberhard Karls University of Tübingen, Tübingen, Germany +1
The price of uncertainty for social consensus
Aug 24, 2025Yunzhe Bai, Alec SunOpinion Dynamics Multi-Agent Consensus
Sequential Cohort Selection under Uncertainty
Aug 22, 2025Hortence Yiepnou, Christos DimitrakakisSequential Decision Making Policy Optimization
University of Neuch atel
Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty
Aug 12, 2025Rui Wang, Qihan Lin, Jiayu Liu +7Prospect Theory LLM Decision-Making
Hong Kong University of Science and Technology · Huazhong University of Science and Technology · University of Illinois Urbana-Champaign
Sufficient Decision Proxies for Decision-Focused Learning
May 6, 2025Noah Schutte, Grigorii Veviurko, Krzysztof Postek +1Stochastic Optimization Learning to Optimize
Delft University of Technology · Independent Researcher
Plan-Driven Adaptive Bidding for First-Price Auctions with Budget Constraints under Nonstationarity
May 5, 2025Yige Wang, Jiashuo JiangPrimal-Dual Optimization Automated Bidding
Department of Industrial Engineering & Decision Analytics, Hong Kong University of Science and Technology
Active Exploration via Autoregressive Generation of Missing Data
May 29, 2024Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo +1RL Exploration Autoregressive Generation
Department of Statistics Columbia University New York, NY 10025, USA · Decision, Risk, and Operations Division Columbia Business School New York, NY 10025, USA · Department of Mathematics Imperial College London London SW7 2AZ, United Kingdom
Double Fuzzy Probabilistic Interval Linguistic Term Set and a Dynamic Fuzzy Decision Making Model based on Markov Process with tts Application in Multiple Criteria Group Decision Making
Nov 30, 2021Zongmin LiuMulti-Criteria Decision Analysis Fuzzy Logic
Stanford University, Stanford, CA 94305 USA
Learning in a Small/Big World
Sep 24, 2020Benson Tsz Kin LeungDecision-Making under Uncertainty Bounded Rationality
Hong Kong Baptist University.

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Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation

Hongyu Cao, Jinghan Zhang, Kunpeng Liu, Dongjie Wang+4

Arizona State University · Clemson University · University of Kansas+3

Robust RL Decision-Making under Uncertainty

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Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment, and has become essential in mission-critical domains such as supply chains and industrial systems. However, simulators learned from noisy or biased real-world data often exhibit prediction errors in decision-critical regions, leading to unstable action ranking and unreliable policies. Existing approaches either focus on improving average simulation fidelity or adopt conservative regularization, which may cause policy collapse by discarding high-risk high-reward actions. We propose Sim2Act, a robust simulation-to-decision framework that addresses both simulator and policy robustness. First, we introduce an adversarial calibration mechanism that re-weights simulation errors in decision-critical state-action pairs to align surrogate fidelity with downstream decision impact. Second, we develop a group-relative perturbation strategy that stabilizes policy learning under simulator uncertainty without enforcing overly pessimistic constraints. Extensive experiments on multiple supply chain benchmarks demonstrate improved simulation robustness and more stable decision performance under structured and unstructured perturbations.