# Exploratory Models of Human-AI Teams: Leveraging Human Digital Twins to Investigate Trust Development

arXiv: https://arxiv.org/abs/2411.01049 · HTML: https://mlsanity.com/paper/2411.01049/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 69 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation](https://mlsanity.com/paper/2311.03348/) (2023)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Toward quantifying trust dynamics: How people adjust their trust after moment-to-moment interaction with automation](https://mlsanity.com/paper/2107.07374/) (2021)
- [Regulation Games for Trustworthy Machine Learning](https://mlsanity.com/paper/2402.03540/) (2024)
- [Converging Measures and an Emergent Model: A Meta-Analysis of Human-Automation Trust Questionnaires](https://mlsanity.com/paper/2303.13799/) (2023)

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

- [Exploring Big Five Personality and AI Capability Effects in LLM-Simulated Negotiation Dialogues](https://mlsanity.com/paper/2506.15928/) (2025)
- [Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies](https://mlsanity.com/paper/2604.15607/) (2026)

## Abstract (arXiv)

As human-agent teaming (HAT) research continues to grow, computational methods for modeling HAT behaviors and measuring HAT effectiveness also continue to develop. One rising method involves the use of human digital twins (HDT) to approximate human behaviors and socio-emotional-cognitive reactions to AI-driven agent team members. In this paper, we address three research questions relating to the use of digital twins for modeling trust in HATs. First, to address the question of how we can appropriately model and operationalize HAT trust through HDT HAT experiments, we conducted causal analytics of team communication data to understand the impact of empathy, socio-cognitive, and emotional constructs on trust formation. Additionally, we reflect on the current state of the HAT trust science to discuss characteristics of HAT trust that must be replicable by a HDT such as individual differences in trust tendencies, emergent trust patterns, and appropriate measurement of these characteristics over time. Second, to address the question of how valid measures of HDT trust are for approximating human trust in HATs, we discuss the properties of HDT trust: self-report measures, interaction-based measures, and compliance type behavioral measures. Additionally, we share results of preliminary simulations comparing different LLM models for generating HDT communications and analyze their ability to replicate human-like trust dynamics. Third, to address how HAT experimental manipulations will extend to human digital twin studies, we share experimental design focusing on propensity to trust for HDTs vs. transparency and competency-based trust for AI agents.