# On Team Decision Problems with Nonclassical Information Structures

arXiv: https://arxiv.org/abs/2101.10992 · HTML: https://mlsanity.com/paper/2101.10992/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2021.

## Built on by (most cited first, 10 of 10)

- [Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging](https://mlsanity.com/paper/2304.00397/) (2023)
- [A Q-learning Approach for Adherence-Aware Recommendations](https://mlsanity.com/paper/2309.06519/) (2023)
- [A Framework for Effective AI Recommendations in Cyber-Physical-Human Systems](https://mlsanity.com/paper/2403.05715/) (2024)
- [Approximate Information States for Worst-Case Control and Learning in Uncertain Systems](https://mlsanity.com/paper/2301.05089/) (2023)
- [Policy Evaluation in Decentralized POMDPs with Belief Sharing](https://mlsanity.com/paper/2302.04151/) (2023)
- [CorrA: Leveraging Large Language Models for Dynamic Obstacle Avoidance of Autonomous Vehicles](https://mlsanity.com/paper/2503.02076/) (2025)
- [Off-Policy Evaluation for Sequential Persuasion Process with Unobserved Confounding](https://mlsanity.com/paper/2504.01211/) (2025)
- [A Communication-Efficient Decentralized Actor-Critic Algorithm](https://mlsanity.com/paper/2510.19199/) (2025)
- [Provably Optimal Learning Algorithms for Assistance Games](https://mlsanity.com/paper/2607.08012/) (2026)
- [A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing](https://mlsanity.com/paper/2609.26783/) (2026)

## Abstract (arXiv)

In this paper, we consider sequential dynamic team decision problems with nonclassical information structures. First, we address the problem from the point of view of a ``manager" who seeks to derive the optimal strategy of the team in a centralized process. We derive structural results that yield an information state for the team which does not depend on the control strategy, and thus it can lead to a dynamic programming decomposition where the optimization problem is over the space of the team's decisions. We, then, derive structural results for each team member that yield an information state which does not depend on their control strategy, and thus it can lead to a dynamic programming decomposition where the optimization problem for each team member is over the space of their decisions. Finally, we show that the solution of each team member is the same as the one derived by the manager. We present an illustrative example of a dynamic team with a delayed sharing information structure.
