# A Computable Game-Theoretic Framework for Multi-Agent Theory of Mind

arXiv: https://arxiv.org/abs/2511.22536 · HTML: https://mlsanity.com/paper/2511.22536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [Machine Theory of Mind](https://mlsanity.com/paper/1802.07740/) (2018)
- [MMToM-QA: Multimodal Theory of Mind Question Answering](https://mlsanity.com/paper/2401.08743/) (2024)
- [MuMA-ToM: Multi-modal Multi-Agent Theory of Mind](https://mlsanity.com/paper/2408.12574/) (2024)
- [Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning](https://mlsanity.com/paper/1901.09216/) (2019)
- [AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling](https://mlsanity.com/paper/2502.15676/) (2025)
- [A General Multi-agent Epistemic Planner Based on Higher-order Belief Change](https://mlsanity.com/paper/1806.11298/) (2018)
- [On Convergence and Optimality of Best-Response Learning with Policy Types in Multiagent Systems](https://mlsanity.com/paper/1907.06995/) (2019)
- [Single-Agent Planning in a Multi-Agent System: A Unified Framework for Type-Based Planners](https://mlsanity.com/paper/2502.08950/) (2025)
- [Constant-Memory Strategies in Stochastic Games: Best Responses and Equilibria](https://mlsanity.com/paper/2505.07008/) (2025)

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

- [A formal definition and meta-model for a machine theory of mind](https://mlsanity.com/paper/2606.03471/) (2026)

## Abstract (arXiv)

Originating in psychology, textitTheory of Mind (ToM) has attracted significant attention across multiple research communities, especially logic, economics, and robotics. Most psychological work does not aim at formalizing those central concepts, namely textitgoals, textitintentions, and textitbeliefs, to automate a ToM-based computational process, which, by contrast, has been extensively studied by logicians. In this paper, we offer a different perspective by proposing a computational framework viewed through the lens of game theory. On the one hand, the framework prescribes how to make boudedly rational decisions while maintaining a theory of mind about others (and recursively, each of the others holding a theory of mind about the rest); on the other hand, it employs statistical techniques and approximate solutions to retain computability of the inherent computational problem.