# Risk-Sensitive Cooperative Games for Human-Machine Systems

arXiv: https://arxiv.org/abs/1705.09580 · HTML: https://mlsanity.com/paper/1705.09580/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2017.

## Built on by (most cited first, 1 of 1)

- [Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty](https://mlsanity.com/paper/2203.10045/) (2022)

## Abstract (arXiv)

Autonomous systems can substantially enhance a human's efficiency and effectiveness in complex environments. Machines, however, are often unable to observe the preferences of the humans that they serve. Despite the fact that the human's and machine's objectives are aligned, asymmetric information, along with heterogeneous sensitivities to risk by the human and machine, make their joint optimization process a game with strategic interactions. We propose a framework based on risk-sensitive dynamic games; the human seeks to optimize her risk-sensitive criterion according to her true preferences, while the machine seeks to adaptively learn the human's preferences and at the same time provide a good service to the human. We develop a class of performance measures for the proposed framework based on the concept of regret. We then evaluate their dependence on the risk-sensitivity and the degree of uncertainty. We present applications of our framework to self-driving taxis, and robo-financial advising.