# Boltzmann State-Dependent Rationality

arXiv: https://arxiv.org/abs/2404.17725 · HTML: https://mlsanity.com/paper/2404.17725/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 26 Apr 2024.

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

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Imitation Learning by Estimating Expertise of Demonstrators](https://mlsanity.com/paper/2202.01288/) (2022)
- [TRAIL: Near-Optimal Imitation Learning with Suboptimal Data](https://mlsanity.com/paper/2110.14770/) (2021)
- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Models](https://mlsanity.com/paper/2204.10759/) (2022)
- [Multi-Task Trust Transfer for Human-Robot Interaction](https://mlsanity.com/paper/1807.01866/) (2018)
- [Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration](https://mlsanity.com/paper/2103.04077/) (2021)

## Abstract (arXiv)

This paper expands on existing learned models of human behavior via a measured step in structured irrationality. Specifically, by replacing the suboptimality constant β in a Boltzmann rationality model with a function over states β(s), we gain natural expressivity in a computationally tractable manner. This paper discusses relevant mathematical theory, sets up several experimental designs, presents limited preliminary results, and proposes future investigations.