# 'Indifference' methods for managing agent rewards

arXiv: https://arxiv.org/abs/1712.06365 · HTML: https://mlsanity.com/paper/1712.06365/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems](https://mlsanity.com/paper/2405.06624/) (2024)
- [Human Control: Definitions and Algorithms](https://mlsanity.com/paper/2305.19861/) (2023)
- [Towards Shutdownable Agents via Stochastic Choice](https://mlsanity.com/paper/2407.00805/) (2024)
- [Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs](https://mlsanity.com/paper/2604.17502/) (2026)

## Abstract (arXiv)

`Indifference' refers to a class of methods used to control reward based agents. Indifference techniques aim to achieve one or more of three distinct goals: rewards dependent on certain events (without the agent being motivated to manipulate the probability of those events), effective disbelief (where agents behave as if particular events could never happen), and seamless transition from one reward function to another (with the agent acting as if this change is unanticipated). This paper presents several methods for achieving these goals in the POMDP setting, establishing their uses, strengths, and requirements. These methods of control work even when the implications of the agent's reward are otherwise not fully understood.