# Modeling and Predicting Trust Dynamics in Human-Robot Teaming: A Bayesian Inference Approach

arXiv: https://arxiv.org/abs/2007.13151 · HTML: https://mlsanity.com/paper/2007.13151/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2020.

## Built on by (most cited first, 14 of 14)

- [KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems](https://mlsanity.com/paper/2502.07350/) (2025)
- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
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- [A Mental Model Based Framework of Trust](https://mlsanity.com/paper/2301.12569/) (2023)
- [Humans Coexist, So Must Embodied Artificial Agents](https://mlsanity.com/paper/2502.04809/) (2025)
- [Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review](https://mlsanity.com/paper/2307.08024/) (2023)
- [Updating Clinical Risk Stratification Models Using Rank-Based Compatibility: Approaches for Evaluating and Optimizing Clinician-Model Team Performance](https://mlsanity.com/paper/2308.05619/) (2023)
- [ToP-ToM: Trust-aware Robot Policy with Theory of Mind](https://mlsanity.com/paper/2311.04397/) (2023)
- [Conversational Explanations: Discussing Explainable AI with Non-AI Experts](https://mlsanity.com/paper/2503.16444/) (2025)
- [Combining Bayesian Inference and Reinforcement Learning for Agent Decision Making: A Review](https://mlsanity.com/paper/2505.07911/) (2025)
- [Rethinking Trust Repair in Human-Robot Interaction](https://mlsanity.com/paper/2307.11763/) (2023)
- [Ethics of Artificial Intelligence and Robotics in the Architecture, Engineering, and Construction Industry](https://mlsanity.com/paper/2310.05414/) (2023)
- [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)

Trust in automation, or more recently trust in autonomy, has received extensive research attention in the past two decades. The majority of prior literature adopted a "snapshot" view of trust and typically evaluated trust through questionnaires administered at the end of an experiment. This "snapshot" view, however, does not acknowledge that trust is a time-variant variable that can strengthen or decay over time. To fill the research gap, the present study aims to model trust dynamics when a human interacts with a robotic agent over time. The underlying premise of the study is that by interacting with a robotic agent and observing its performance over time, a rational human agent will update his/her trust in the robotic agent accordingly. Based on this premise, we develop a personalized trust prediction model based on Beta distribution and learn its parameters using Bayesian inference. Our proposed model adheres to three major properties of trust dynamics reported in prior empirical studies. We tested the proposed method using an existing dataset involving 39 human participants interacting with four drones in a simulated surveillance mission. The proposed method obtained a Root Mean Square Error (RMSE) of 0.072, significantly outperforming existing prediction methods. Moreover, we identified three distinctive types of trust dynamics, the Bayesian decision maker, the oscillator, and the disbeliever, respectively. This prediction model can be used for the design of individualized and adaptive technologies.