# Towards a Unifying Model of Rationality in Multiagent Systems

arXiv: https://arxiv.org/abs/2305.18071 · HTML: https://mlsanity.com/paper/2305.18071/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 3 of 3)

- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [K-level Reasoning for Zero-Shot Coordination in Hanabi](https://mlsanity.com/paper/2207.07166/) (2022)
- [On the Impossibility of Learning to Cooperate with Adaptive Partner Strategies in Repeated Games](https://mlsanity.com/paper/2206.10614/) (2022)

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

- [On the Complexity of Learning to Cooperate with Populations of Socially Rational Agents](https://mlsanity.com/paper/2407.00419/) (2024)

## Abstract (arXiv)

Multiagent systems deployed in the real world need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. To design such AI, and provide guarantees of its effectiveness, we need to clearly specify what types of agents our AI must be able to cooperate with. In this work we propose a generic model of socially intelligent agents, which are individually rational learners that are also able to cooperate with one another (in the sense that their joint behavior is Pareto efficient). We define rationality in terms of the regret incurred by each agent over its lifetime, and show how we can construct socially intelligent agents for different forms of regret. We then discuss the implications of this model for the development of "robust" MAS that can cooperate with a wide variety of socially intelligent agents.