# Hypergame Rationalisability: Solving Agent Misalignment In Strategic Play

arXiv: https://arxiv.org/abs/2512.11942 · HTML: https://mlsanity.com/paper/2512.11942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## Builds on (most cited first, 5 of 5)

- [Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment](https://mlsanity.com/paper/2406.04231/) (2024)
- [Approximating Human Strategic Reasoning with LLM-Enhanced Recursive Reasoners Leveraging Multi-agent Hypergames](https://mlsanity.com/paper/2502.07443/) (2025)
- [Autoformalization of Game Descriptions using Large Language Models](https://mlsanity.com/paper/2409.12300/) (2024)
- [The Influence of Human-inspired Agentic Sophistication in LLM-driven Strategic Reasoners](https://mlsanity.com/paper/2505.09396/) (2025)
- [A Survey on Hypergame Theory: Modelling Misaligned Perceptions and Nested Beliefs for Multi-Agent Systems](https://mlsanity.com/paper/2507.19593/) (2025)

## Abstract (arXiv)

Differences in perception, information asymmetries, and bounded rationality lead game-theoretic players to derive a private, subjective view of the game that may diverge from the underlying ground-truth scenario and may be misaligned with other players' interpretations. While typical game-theoretic assumptions often overlook such heterogeneity, hypergame theory provides the mathematical framework to reason about mismatched mental models. Although hypergames have recently gained traction in dynamic applications concerning uncertainty, their practical adoption in multi-agent system research has been hindered by the lack of a unifying, formal, and practical representation language, as well as scalable algorithms for managing complex hypergame structures and equilibria. Our work addresses this gap by introducing a declarative, logic-based domain-specific language for encoding hypergame structures and hypergame solution concepts. Leveraging answer-set programming, we develop an automated pipeline for instantiating hypergame structures and running our novel hypergame rationalisation procedure, a mechanism for finding belief structures that justify seemingly irrational outcomes. The proposed language establishes a unifying formalism for hypergames and serves as a foundation for developing nuanced, belief-based heterogeneous reasoners, offering a verifiable context with logical guarantees. Together, these contributions establish the connection between hypergame theory, multi-agent systems, and strategic AI.