# Strongly-Typed Agents are Guaranteed to Interact Safely

arXiv: https://arxiv.org/abs/1702.07450 · HTML: https://mlsanity.com/paper/1702.07450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2017.

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

- [Gradland: On Phenomenal Experience, Differentiated Across Many Dimensions](https://mlsanity.com/paper/2609.09306/) (2026)

## Abstract (arXiv)

As artificial agents proliferate, it is becoming increasingly important to ensure that their interactions with one another are well-behaved. In this paper, we formalize a common-sense notion of when algorithms are well-behaved: an algorithm is safe if it does no harm. Motivated by recent progress in deep learning, we focus on the specific case where agents update their actions according to gradient descent. The paper shows that that gradient descent converges to a Nash equilibrium in safe games. The main contribution is to define strongly-typed agents and show they are guaranteed to interact safely, thereby providing sufficient conditions to guarantee safe interactions. A series of examples show that strong-typing generalizes certain key features of convexity, is closely related to blind source separation, and introduces a new perspective on classical multilinear games based on tensor decomposition.