# Modeling Human-AI Team Decision Making

arXiv: https://arxiv.org/abs/2201.02759 · HTML: https://mlsanity.com/paper/2201.02759/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 8 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Cooperating with Machines](https://mlsanity.com/paper/1703.06207/) (2017)
- [Deep Interpretable Models of Theory of Mind](https://mlsanity.com/paper/2104.02938/) (2021)
- [Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving](https://mlsanity.com/paper/1606.04344/) (2016)
- [Dynamic Models of Appraisal Networks Explaining Collective Learning](https://mlsanity.com/paper/1609.09546/) (2016)

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

- [Algorithmic Assistance with Recommendation-Dependent Preferences](https://mlsanity.com/paper/2208.07626/) (2022)
- [What Types of Human-AI Teams Exist?](https://mlsanity.com/paper/2607.02198/) (2026)

## Abstract (arXiv)

AI and humans bring complementary skills to group deliberations. Modeling this group decision making is especially challenging when the deliberations include an element of risk and an exploration-exploitation process of appraising the capabilities of the human and AI agents. To investigate this question, we presented a sequence of intellective issues to a set of human groups aided by imperfect AI agents. A group's goal was to appraise the relative expertise of the group's members and its available AI agents, evaluate the risks associated with different actions, and maximize the overall reward by reaching consensus. We propose and empirically validate models of human-AI team decision making under such uncertain circumstances, and show the value of socio-cognitive constructs of prospect theory, influence dynamics, and Bayesian learning in predicting the behavior of human-AI groups.