# Modeling Communication to Coordinate Perspectives in Cooperation

arXiv: https://arxiv.org/abs/2106.02164 · HTML: https://mlsanity.com/paper/2106.02164/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [Theory of Minds: Understanding Behavior in Groups Through Inverse Planning](https://mlsanity.com/paper/1901.06085/) (2019)
- [Too many cooks: Bayesian inference for coordinating multi-agent collaboration](https://mlsanity.com/paper/2003.11778/) (2020)

## Built on by (most cited first, 5 of 5)

- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Inferring the Goals of Communicating Agents from Actions and Instructions](https://mlsanity.com/paper/2306.16207/) (2023)
- [Linguistic communication as (inverse) reward design](https://mlsanity.com/paper/2204.05091/) (2022)
- [From Cognition to Computation: A Comparative Review of Human Attention and Transformer Architectures](https://mlsanity.com/paper/2407.01548/) (2024)
- [Inverse Attention Agents for Multi-Agent Systems](https://mlsanity.com/paper/2410.21794/) (2024)

## Abstract (arXiv)

Communication is highly overloaded. Despite this, even young children are good at leveraging context to understand ambiguous signals. We propose a computational account of overloaded signaling from a shared agency perspective which we call the Imagined We for Communication. Under this framework, communication helps cooperators coordinate their perspectives, allowing them to act together to achieve shared goals. We assume agents are rational cooperators, which puts constraints on how signals can be sent and interpreted. We implement this model in a set of simulations demonstrating this model's success under increasing ambiguity as well as increasing layers of reasoning. Our model is capable of improving performance with deeper recursive reasoning; however, it outperforms comparison baselines at even the shallowest level, highlighting how shared knowledge and cooperative logic can do much of the heavy-lifting in language.