# Minimax-optimal trust-aware multi-armed bandits

arXiv: https://arxiv.org/abs/2410.03651 · HTML: https://mlsanity.com/paper/2410.03651/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Bandits with Knapsacks](https://mlsanity.com/paper/1305.2545/) (2013)
- [Linear Stochastic Bandits Under Safety Constraints](https://mlsanity.com/paper/1908.05814/) (2019)
- [Stochastic Bandits with Linear Constraints](https://mlsanity.com/paper/2006.10185/) (2020)
- [Meta-Thompson Sampling](https://mlsanity.com/paper/2102.06129/) (2021)
- [Bayesian Incentive-Compatible Bandit Exploration](https://mlsanity.com/paper/1502.04147/) (2015)
- [Meta-learning with Stochastic Linear Bandits](https://mlsanity.com/paper/2005.08531/) (2020)
- [Sequential Transfer in Multi-armed Bandit with Finite Set of Models](https://mlsanity.com/paper/1307.6887/) (2013)
- [Trust-Aware Decision Making for Human-Robot Collaboration: Model Learning and Planning](https://mlsanity.com/paper/1801.04099/) (2018)
- [Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR](https://mlsanity.com/paper/2302.03201/) (2023)
- [An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints](https://mlsanity.com/paper/2102.05295/) (2021)
- [Transfer Learning for Contextual Multi-armed Bandits](https://mlsanity.com/paper/2211.12612/) (2022)
- [Refined Lower Bounds for Adversarial Bandits](https://mlsanity.com/paper/1605.07416/) (2016)
- [Dynamic Assortment Personalization in High Dimensions](https://mlsanity.com/paper/1610.05604/) (2016)
- [Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach](https://mlsanity.com/paper/2007.13151/) (2020)
- [Evaluating the Impact of Personalized Value Alignment in Human-Robot Interaction: Insights into Trust and Team Performance Outcomes](https://mlsanity.com/paper/2311.16051/) (2023)
- [Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task](https://mlsanity.com/paper/2206.01645/) (2022)

## Abstract (arXiv)

Multi-armed bandit (MAB) algorithms have achieved significant success in sequential decision-making applications, under the premise that humans perfectly implement the recommended policy. However, existing methods often overlook the crucial factor of human trust in learning algorithms. When trust is lacking, humans may deviate from the recommended policy, leading to undesired learning performance. Motivated by this gap, we study the trust-aware MAB problem by integrating a dynamic trust model into the standard MAB framework. Specifically, it assumes that the recommended and actually implemented policy differs depending on human trust, which in turn evolves with the quality of the recommended policy. We establish the minimax regret in the presence of the trust issue and demonstrate the suboptimality of vanilla MAB algorithms such as the upper confidence bound (UCB) algorithm. To overcome this limitation, we introduce a novel two-stage trust-aware procedure that provably attains near-optimal statistical guarantees. A simulation study is conducted to illustrate the benefits of our proposed algorithm when dealing with the trust issue.