# Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

arXiv: https://arxiv.org/abs/2206.01645 · HTML: https://mlsanity.com/paper/2206.01645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2022.

## Built on by (most cited first, 2 of 2)

- [Minimax-optimal trust-aware multi-armed bandits](https://mlsanity.com/paper/2410.03651/) (2024)
- [Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales](https://mlsanity.com/paper/2411.01866/) (2024)

## Abstract (arXiv)

In this paper, we present a framework for trust-aware sequential decision-making in a human-robot team. We model the problem as a finite-horizon Markov Decision Process with a reward-based performance metric, allowing the robotic agent to make trust-aware recommendations. Results of a human-subject experiment show that the proposed trust update model is able to accurately capture the human agent's moment-to-moment trust changes. Moreover, we cluster the participants' trust dynamics into three categories, namely, Bayesian decision makers, oscillators, and disbelievers, and identify personal characteristics that could be used to predict which type of trust dynamics a person will belong to. We find that the disbelievers are less extroverted, less agreeable, and have lower expectations toward the robotic agent, compared to the Bayesian decision makers and oscillators. The oscillators are significantly more frustrated than the Bayesian decision makers.