# Multi-Principal Assistance Games: Definition and Collegial Mechanisms

arXiv: https://arxiv.org/abs/2012.14536 · HTML: https://mlsanity.com/paper/2012.14536/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

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

- [The Oversight Game: Learning to Cooperatively Balance an AI Agent's Safety and Autonomy](https://mlsanity.com/paper/2510.26752/) (2025)
- [Dynamic value alignment through preference aggregation of multiple objectives](https://mlsanity.com/paper/2310.05871/) (2023)

## Abstract (arXiv)

We introduce the concept of a multi-principal assistance game (MPAG), and circumvent an obstacle in social choice theory, Gibbard's theorem, by using a sufficiently collegial preference inference mechanism. In an MPAG, a single agent assists N human principals who may have widely different preferences. MPAGs generalize assistance games, also known as cooperative inverse reinforcement learning games. We analyze in particular a generalization of apprenticeship learning in which the humans first perform some work to obtain utility and demonstrate their preferences, and then the robot acts to further maximize the sum of human payoffs. We show in this setting that if the game is sufficiently collegial, i.e. if the humans are responsible for obtaining a sufficient fraction of the rewards through their own actions, then their preferences are straightforwardly revealed through their work. This revelation mechanism is non-dictatorial, does not limit the possible outcomes to two alternatives, and is dominant-strategy incentive-compatible.