Hypergame Rationalisability: Solving Agent Misalignment In Strategic Play
Vince Trencsenyi

TL;DR
This paper introduces a formal language and algorithms for modeling and solving hypergames, addressing agent misalignment in strategic interactions caused by perception differences and bounded rationality.
Contribution
It presents a declarative, logic-based language and an automated pipeline for hypergame modeling and rationalization, bridging hypergame theory with multi-agent systems and strategic AI.
Findings
Developed a formal language for hypergame structures
Created an automated hypergame rationalisation procedure
Established logical guarantees for hypergame reasoning
Abstract
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…
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Multi-Agent Systems and Negotiation · Constraint Satisfaction and Optimization
